{"title":"Databases \/ Data management Books","description":"","products":[{"product_id":"big-data-how-the-information-revolution-is-transforming-our-lives-9781785782343","title":"Big Data: How the Information Revolution Is","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eIs the Brexit vote successful big data politics or the end of democracy? Why do airlines overbook, and why do banks get it wrong so often? How does big data enable Netflix to forecast a hit, CERN to find the Higgs boson and medics to discover if red wine really is good for you? And how are companies using big data to benefit from smart meters, use advertising that spies on you and develop the gig economy, where workers are managed by the whim of an algorithm?\u003cbr\u003e\u003cbr\u003e\u003cbr\u003eThe volumes of data we now access can give unparalleled abilities to make predictions, respond to customer demand and solve problems. But Big Brother's shadow hovers over it. Though big data can set us free and enhance our lives, it has the potential to create an underclass and a totalitarian state. \u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003eWith big data ever-present, you can't afford to ignore it. Acclaimed science writer Brian Clegg - a habitual early adopter of new technology (and the owner of the second-ever copy of Windows in the UK) - brings big data to life.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003eAs always, Clegg writes with an easy clarity that draws us in - no technical expertise required to understand his exploration of this essential subject - and throughout Big Data's highly enjoyable pages, the spread and range of material is highly impressive - dizzying in fact. I personally found entirely new perspectives on the subject that will keep me pondering for quite some time. I should add that, if I were still a statistics lecturer at Oxford, I would recommend the book to my students as bedside reading. -- Peet Morris * Former Lecturer in Statistics (St Hilda’s College Oxford), Lecturer\/Researcher in software development *\u003cbr\u003eClegg provides an engaging insight, reflecting on its positives and negatives. A holiday workout for the brain. * Saga Magazine *\u003cbr\u003eAcclaimed science writer Brian Clegg - a habitual early adopter of new technology (and the owner of the second-ever copy of Windows in the UK) brings big data to life. * Laboratory News *","brand":"Icon Books","offers":[{"title":"Default Title","offer_id":47851266244951,"sku":"9781785782343","price":8.54,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781785782343.jpg?v=1710631809"},{"product_id":"star-schema-the-complete-reference-9780071744324","title":"Star Schema The Complete Reference","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003ch4\u003e\u003c\/h4\u003e\u003cp class=\"MsoNormal\"\u003e\u003cspan style=\"font-size:12.0pt;line-height:107%;font-family:\" times new roman\u003ePublisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, \u003cspan style=\"white-space:pre\"\u003e\u003c\/span\u003eauthenticity, or access to any online entitlements included with the product.\u003co:p\u003e\u003c\/o:p\u003e\u003c\/span\u003e\u003c\/p\u003e\u003ch4\u003e\u003cbr\u003e\u003c\/h4\u003e\u003ch4\u003eThe definitive guide to dimensional design for your data warehouse\u003c\/h4\u003e\u003cp\u003eLearn the best practices of dimensional design. \u003ci\u003eStar Schema: The Complete Reference\u003c\/i\u003e offers in-depth coverage of design principles and their underlying rationales. Organized around design concepts and illustrated with detailed examples, this is a step-by-step guidebook for beginners and a comprehensive resource for experts.\u003c\/p\u003e\u003cp\u003eThis all-inclusive volume begins with dimensional design fundamentals and shows how they fit into diverse data warehouse architectures, including those of W.H. Inmon and Ralph Kimball. The book progresses throu\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cb\u003ePart I: Fundamentals\u003c\/b\u003e; \u003cb\u003eChapter 1:\u003c\/b\u003e Analytic Databases and Dimensional Design; \u003cb\u003eChapter 2:\u003c\/b\u003e Data Warehouse Architectures; \u003cb\u003eChapter 3:\u003c\/b\u003e Stars and Cubes; \u003cb\u003ePart II: Multiple Stars\u003c\/b\u003e; \u003cb\u003eChapter 4:\u003c\/b\u003e A Fact Table for Each Process; \u003cb\u003eChapter 5:\u003c\/b\u003e Conformed Dimensions; \u003cb\u003ePart III: Dimension Design\u003c\/b\u003e; \u003cb\u003eChapter 6:\u003c\/b\u003e More on Dimension Tables; \u003cb\u003eChapter 7:\u003c\/b\u003e Hierarchies and Snowflakes; \u003cb\u003eChapter 8:\u003c\/b\u003e More Slow Change Techniques; \u003cb\u003eChapter 9:\u003c\/b\u003e Multi-Value Dimensions and Bridges; \u003cb\u003eChapter 10:\u003c\/b\u003e Recursive Hierarchies and Bridges;\u003cb\u003ePart IV: Fact Table Design\u003c\/b\u003e; \u003cb\u003eChapter 11:\u003c\/b\u003e Transactions, Snapshots and Accumulating Snapshots; \u003cb\u003eChapter 12:\u003c\/b\u003e Factless Fact Tables; \u003cb\u003eChapter 13:\u003c\/b\u003e Type-Specific Stars; \u003cb\u003ePart V: Performance\u003c\/b\u003e; \u003cb\u003eChapter 14:\u003c\/b\u003e Derived Schemas; \u003cb\u003eChapter 15:\u003c\/b\u003e Aggregates; \u003cb\u003ePart VI: Tools and Documentation\u003c\/b\u003e; \u003cb\u003eChapter 16:\u003c\/b\u003e Design and Business Intelligence; \u003cb\u003eChapter 17:\u003c\/b\u003e Design and ETL; \u003cb\u003eChapter 18:\u003c\/b\u003e How to Design and Document a Dimensional Model; \u003cb\u003eIndex\u003c\/b\u003e\u003c\/p\u003e","brand":"McGraw-Hill Education - Europe","offers":[{"title":"Default Title","offer_id":48732175139159,"sku":"9780071744324","price":31.19,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780071744324.jpg?v=1719995840"},{"product_id":"python-for-programmers-9780135224335","title":"Python for Programmers","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e \u003cstrong\u003ePaul Deitel\u003c\/strong\u003e, CEO and Chief Technical Officer of Deitel \u0026amp; Associates, Inc., is a graduate of MIT, where he studied Information Technology. Through Deitel \u0026amp; Associates, Inc., he has delivered hundreds of programming courses worldwide to clients, including Cisco, IBM, Siemens, Sun Microsystems, Dell, Fidelity, NASA at the Kennedy Space Center, the National Severe Storm Laboratory, White Sands Missile Range, Rogue Wave Software, Boeing, SunGard Higher Education, Nortel Networks, Puma, iRobot, Invensys and many more. He and his co-author, Dr. Harvey M. Deitel, are the world's best-selling programming-language textbook\/professional book\/video authors. \u003cstrong\u003eDr. Harvey Deitel\u003c\/strong\u003e, Chairman and Chief Strategy Officer of Deitel \u0026amp; Associates, Inc., has over 50 years of experience in the computer field. Dr. Deitel earned B.S. and M.S. degrees in Electrical Engineering from MIT and a Ph.D. in Mathematics from Boston University. He has extensive college teaching experien\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e“The chapters are clearly written with detailed explanations of the example code. The modular structure, wide range of contemporary data science topics, and code in companion Jupyter notebooks make this a fantastic resource for readers of a variety of backgrounds. Fabulous Big Data chapter—it covers all of the relevant programs and platforms. Great Watson chapter! The chapter provides a great overview of the Watson applications. Also, your translation examples are great because they provide an ‘instant reward’—it’s very satisfying to implement a task and receive results so quickly. Machine Learning is a huge topic, and the chapter serves as a great introduction. I loved the California housing data example—very relevant for business analytics. The chapter was visually stunning.” \u003cbr\u003e \u003ci\u003e—Alison Sanchez, Assistant Professor in Economics, University of San Diego\u003c\/i\u003e \u003cbr\u003e \u003cbr\u003e“A great introduction to Big Data concepts, notably Hadoop, Spark, and IoT. The examples are extremely realistic and practical. The authors do an excellent job of combining programming and data science topics. The material is presented in digestible sections accompanied by engaging interactive examples. Nearly all concepts are accompanied by a worked-out example. A comprehensive overview of object-oriented programming in Python—the use of card image graphics is sure to engage the reader.” \u003cbr\u003e \u003ci\u003e—Garrett Dancik, Eastern Connecticut State University\u003c\/i\u003e \u003cbr\u003e \u003cbr\u003e“Covers some of the most modern Python syntax approaches and introduces community standards for style and documentation. The machine learning chapter does a great job of walking people through the boilerplate code needed for ML in Python. The case studies accomplish this really well. The later examples are so visual. Many of the model evaluation tasks make for really good programming practice. I can see readers feeling really excited about playing with the animations.” \u003cbr\u003e \u003ci\u003e—Elizabeth Wickes, Lecturer, School of Information Sciences, University of Illinois at Urbana-Champaign\u003c\/i\u003e \u003cbr\u003e \u003cbr\u003e“An engaging, highly accessible book that will foster curiosity and motivate beginning data scientists to develop essential foundations in Python programming, statistics, data manipulation, working with APIs, data visualization, machine learning, cloud computing, and more. Great walkthrough of the Twitter APIs—sentiment analysis piece is very useful. I’ve taken several classes that cover natural language processing and this is the first time the tools and concepts have been explained so clearly. I appreciate the discussion of serialization with JSON and pickling and when to use one or the other—with an emphasis on using JSON over pickle—good to know there’s a better, safer way!” \u003cbr\u003e \u003ci\u003e—Jamie Whitacre, Data Science Consultant\u003c\/i\u003e \u003cbr\u003e \u003cbr\u003e“For a while, I have been looking for a book in Data Science using Python that would cover the most relevant technologies. Well, my search is over. A must-have book for any practitioner of this field. The machine learning chapter is a real winner!! The dynamic visualization is fantastic.” \u003cbr\u003e \u003ci\u003e—Ramon Mata-Toledo, Professor, James Madison University\u003c\/i\u003e \u003cbr\u003e \u003cbr\u003e“I like the new combination of topics from computer science, data science, and stats. This is important for building data science programs that are more than just cobbling together math and computer science courses. A book like this may help facilitate expanding our offerings and using Python as a bridge for computer and data science topics. For a data science program that focuses on a single language (mostly), I think Python is probably the way to go.” \u003cbr\u003e \u003ci\u003e—Lance Bryant, Shippensburg University\u003c\/i\u003e \u003cbr\u003e \u003cbr\u003e“You’ll develop applications using industry standard libraries and cloud computing services.” \u003cbr\u003e \u003ci\u003e—Daniel Chen, Data Scientist, Lander Analytics\u003c\/i\u003e \u003cbr\u003e \u003cbr\u003e“Great introduction to Python! This book has my strongest recommendation both as an introduction to Python as well as Data Science.” \u003cbr\u003e \u003ci\u003e—Shyamal Mitra, Senior Lecturer, University of Texas\u003c\/i\u003e \u003cbr\u003e \u003cbr\u003e“IBM Watson is an exciting chapter. The code examples put together a lot of Watson services in a really nifty example.” \u003cbr\u003e \u003ci\u003e—Daniel Chen, Data Scientist, Lander Analytics\u003c\/i\u003e \u003cbr\u003e \u003cbr\u003e“Fun, engaging real-world examples will encourage readers to conduct meaningful data analyses. Provides many of the best explanations of data science concepts I’ve encountered. Introduces the most useful starter machine learning models—does a good job explaining how to choose the best model and what ‘the best’ means. Great overview of all the big data technologies with relevant examples.” \u003cbr\u003e \u003ci\u003e—Jamie Whitacre, Data Science Consultant\u003c\/i\u003e \u003cbr\u003e \u003cbr\u003e“A great introduction to deep learning.” \u003cbr\u003e \u003ci\u003e—Alison Sanchez, University of San Diego\u003c\/i\u003e \u003cbr\u003e \u003cbr\u003e“The best designed Intro to Data Science\/Python book I have seen.” \u003cbr\u003e \u003ci\u003e—Roland DePratti, Central Connecticut State University\u003c\/i\u003e \u003cbr\u003e \u003cbr\u003e“I like the new combination of topics from computer science, data science, and stats.” \u003cbr\u003e \u003ci\u003e—Lance Bryant, Shippensburg University\u003c\/i\u003e \u003cbr\u003e \u003cbr\u003e“The book’s applied approach should engage readers. A fantastic job providing background on various machine learning concepts without burdening the users with too many mathematical details.” \u003cbr\u003e \u003ci\u003e—Garrett Dancik, Assoc. Prof. of Computer Science\/Bioinformatics, Eastern Connecticut State University\u003c\/i\u003e \u003cbr\u003e \u003cbr\u003e“Helps readers leverage the large number of existing libraries to accomplish tasks with minimal code. Concepts are accompanied by rich Python examples that readers can adapt to implement their own solutions to data science problems. I like that cloud services are used.” \u003cbr\u003e \u003ci\u003e—David Koop, Assistant Professor, U-Mass Dartmouth\u003c\/i\u003e \u003cbr\u003e \u003cbr\u003e“I enjoyed the OOP chapter—doctest unit testing is nice because you can have the test in the actual docstring so things are traveling together. The line-by-line explanations of the static and dynamic visualizations of the die rolling example are just great.” \u003cbr\u003e \u003ci\u003e—Daniel Chen, Data Scientist, Lander Analytics\u003c\/i\u003e \u003cbr\u003e \u003cbr\u003e“A lucid exposition of the fundamentals of Python and Data Science. Thanks for pointing out seeding the random number generator for reproducibility. I like the use of dictionary and set comprehensions for succinct programming. ‘List vs. Array Performance: Introducing %timeit’ is convincing on why one should use ndarrays. Good defensive programming. Great section on Pandas Series and DataFrames—one of the clearest expositions that I have seen. The section on data wrangling is excellent. Natural Language Processing is an excellent chapter! I learned a tremendous amount going through it.” \u003cbr\u003e \u003ci\u003e—Shyamal Mitra, Senior Lecturer, University of Texas\u003c\/i\u003e \u003cbr\u003e \u003cbr\u003e“I like the discussion of exceptions and tracebacks. I really liked the Data Mining Twitter chapter; it focused on a real data source and brought in a lot of techniques for analysis (e.g., visualization, NLP). I like that the Python modules helped hide some of the complexity. Word clouds look cool.” \u003cbr\u003e \u003ci\u003e—David Koop, Assistant Professor, U-Mass Dartmouth\u003c\/i\u003e \u003cbr\u003e \u003cbr\u003e“I love the book! The examples are definitely a high point.” \u003cbr\u003e \u003ci\u003e—Dr. Irene Bruno, George Mason University\u003c\/i\u003e \u003cbr\u003e \u003cbr\u003e“I was very excited to see this book. I like its focus on data science and a general purpose language for writing useful data science programs. The data science portion distinguishes this book from most other introductory Python books.” \u003cbr\u003e \u003ci\u003e—Dr. Harvey Siy, University of Nebraska at Omaha\u003c\/i\u003e \u003cbr\u003e \u003cbr\u003e“I’ve learned a lot in this review process, discovering the exciting field of AI. I’ve liked the Deep Learning chapter, which has left me amazed with the things that have already been achieved in this field.” \u003cbr\u003e \u003ci\u003e—José Antonio González Seco, Consultant\u003c\/i\u003e \u003cbr\u003e \u003cbr\u003e“An impressive hands-on approach to programming meant for exploration and experimentation.” \u003cbr\u003e \u003ci\u003e—Elizabeth Wickes, Lecturer, School of Information Sciences, University of Illinois at Urbana-Champaign\u003c\/i\u003e \u003cbr\u003e \u003cbr\u003e“I was impressed at how easy it was to get started with NLP using Python. A meaningful overview of deep learning concepts, using Keras. I like the streaming example.” \u003cbr\u003e \u003ci\u003e—David Koop, Assistant Professor, U-Mass Dartmouth\u003c\/i\u003e \u003cbr\u003e \u003cbr\u003e“Really like the use of f-strings, instead of the older string-formatting methods. Seeing how easy TextBlob is compared to base NLTK was great. I never made word clouds with shapes before, but I can see this being a motivating example for people getting started with NLP. I’m enjoying the case-study chapters in the latter parts of the book. They are really practical. I really enjoyed working through all the Big Data examples, especially the IoT ones.” \u003cbr\u003e \u003ci\u003e—Daniel Chen, Data Scientist, Lander Analytics\u003c\/i\u003e \u003cbr\u003e \u003cbr\u003e“I really liked the live IPython input-output. The thing that I like most about this product is that it is a Deitel \u0026amp; Deitel book (I’m a big fan) that covers Python.” \u003cbr\u003e \u003ci\u003e—Dr. Mark Pauley, University of Nebraska at Omaha \u003c\/i\u003e \u003c\/p\u003e\u003cp\u003e \u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cem\u003ePreface xvii\u003cbr\u003eBefore You Begin xxxiii\u003c\/em\u003e\u003cbr\u003e\u003cstrong\u003eChapter 1: Introduction to Computers and Python 1\u003c\/strong\u003e\u003cbr\u003e1.1 Introduction 2\u003cbr\u003e1.2 A Quick Review of Object Technology Basics 3\u003cbr\u003e1.3 Python 5\u003cbr\u003e1.4 It’s the Libraries! 7\u003cbr\u003e1.5 Test-Drives: Using IPython and Jupyter Notebooks 9\u003cbr\u003e1.6 The Cloud and the Internet of Things 16\u003cbr\u003e1.7 How Big Is Big Data? 17\u003cbr\u003e1.8 Case Study—A Big-Data Mobile Application 24\u003cbr\u003e1.9 Intro to Data Science: Artificial Intelligence—at the Intersection of CS and Data Science 26\u003cbr\u003e1.10 Wrap-Up 29\u003cbr\u003e\u003cstrong\u003eChapter 2: Introduction to Python Programming 31\u003c\/strong\u003e\u003cbr\u003e2.1 Introduction 32\u003cbr\u003e2.2 Variables and Assignment Statements 32\u003cbr\u003e2.3 Arithmetic 33\u003cbr\u003e2.4 Function print and an Intro to Single- and Double-Quoted Strings 36\u003cbr\u003e2.5 Triple-Quoted Strings 38\u003cbr\u003e2.6 Getting Input from the User 39\u003cbr\u003e2.7 Decision Making: The if Statement and Comparison Operators 41\u003cbr\u003e2.8 Objects and Dynamic Typing 45\u003cbr\u003e2.9 Intro to Data Science: Basic Descriptive Statistics 46\u003cbr\u003e2.10 Wrap-Up 48\u003cbr\u003e\u003cstrong\u003eChapter 3: Control Statements 49\u003c\/strong\u003e\u003cbr\u003e3.1 Introduction 50\u003cbr\u003e3.2 Control Statements 50\u003cbr\u003e3.3 if Statement 51\u003cbr\u003e3.4 if...else and if...elif...else Statements 52\u003cbr\u003e3.5 while Statement 55\u003cbr\u003e3.6 for Statement 55\u003cbr\u003e3.7 Augmented Assignments 57\u003cbr\u003e3.8 Sequence-Controlled Iteration; Formatted Strings 58\u003cbr\u003e3.9 Sentinel-Controlled Iteration 59\u003cbr\u003e3.10 Built-In Function range: A Deeper Look 60\u003cbr\u003e3.11 Using Type Decimal for Monetary Amounts 61\u003cbr\u003e3.12 break and continue Statements 64\u003cbr\u003e3.13 Boolean Operators and, or and not 65\u003cbr\u003e3.14 Intro to Data Science: Measures of Central Tendency—Mean, Median and Mode 67\u003cbr\u003e3.15 Wrap-Up 69\u003cbr\u003e\u003cstrong\u003eChapter 4: Functions 71\u003c\/strong\u003e\u003cbr\u003e4.1 Introduction 72\u003cbr\u003e4.2 Defining Functions 72\u003cbr\u003e4.3 Functions with Multiple Parameters 75\u003cbr\u003e4.4 Random-Number Generation 76\u003cbr\u003e4.5 Case Study: A Game of Chance 78\u003cbr\u003e4.6 Python Standard Library 81\u003cbr\u003e4.7 math Module Functions 82\u003cbr\u003e4.8 Using IPython Tab Completion for Discovery 83\u003cbr\u003e4.9 Default Parameter Values 85\u003cbr\u003e4.10 Keyword Arguments 85\u003cbr\u003e4.11 Arbitrary Argument Lists 86\u003cbr\u003e4.12 Methods: Functions That Belong to Objects 87\u003cbr\u003e4.13 Scope Rules 87\u003cbr\u003e4.14 import: A Deeper Look 89\u003cbr\u003e4.15 Passing Arguments to Functions: A Deeper Look 90\u003cbr\u003e4.16 Recursion 93\u003cbr\u003e4.17 Functional-Style Programming 95\u003cbr\u003e4.18 Intro to Data Science: Measures of Dispersion 97\u003cbr\u003e4.19 Wrap-Up 98\u003cbr\u003e\u003cstrong\u003eChapter 5: Sequences: Lists and Tuples 101\u003c\/strong\u003e\u003cbr\u003e5.1 Introduction 102\u003cbr\u003e5.2 Lists 102\u003cbr\u003e5.3 Tuples 106\u003cbr\u003e5.4 Unpacking Sequences 108\u003cbr\u003e5.5 Sequence Slicing 110\u003cbr\u003e5.6 del Statement 112\u003cbr\u003e5.7 Passing Lists to Functions 113\u003cbr\u003e5.8 Sorting Lists 115\u003cbr\u003e5.9 Searching Sequences 116\u003cbr\u003e5.10 Other List Methods 117\u003cbr\u003e5.11 Simulating Stacks with Lists 119\u003cbr\u003e5.12 List Comprehensions 120\u003cbr\u003e5.13 Generator Expressions 121\u003cbr\u003e5.14 Filter, Map and Reduce 122\u003cbr\u003e5.15 Other Sequence Processing Functions 124\u003cbr\u003e5.16 Two-Dimensional Lists 126\u003cbr\u003e5.17 Intro to Data Science: Simulation and Static Visualizations 128\u003cbr\u003e5.18 Wrap-Up 135\u003cbr\u003e\u003cstrong\u003eChapter 6: Dictionaries and Sets 137\u003c\/strong\u003e\u003cbr\u003e6.1 Introduction 138\u003cbr\u003e6.2 Dictionaries 138\u003cbr\u003e6.3 Sets 147\u003cbr\u003e6.4 Intro to Data Science: Dynamic Visualizations 152\u003cbr\u003e6.5 Wrap-Up 158\u003cbr\u003e\u003cstrong\u003eChapter 7: Array-Oriented Programming with NumPy 159\u003c\/strong\u003e\u003cbr\u003e7.1 Introduction 160\u003cbr\u003e7.2 Creating arrays from Existing Data 160\u003cbr\u003e7.3 array Attributes 161\u003cbr\u003e7.4 Filling arrays with Specific Values 163\u003cbr\u003e7.5 Creating arrays from Ranges 164\u003cbr\u003e7.6 List vs. array Performance: Introducing %timeit 165\u003cbr\u003e7.7 array Operators 167\u003cbr\u003e7.8 NumPy Calculation Methods 169\u003cbr\u003e7.9 Universal Functions 170\u003cbr\u003e7.10 Indexing and Slicing 171\u003cbr\u003e7.11 Views: Shallow Copies 173\u003cbr\u003e7.12 Deep Copies 174\u003cbr\u003e7.13 Reshaping and Transposing 175\u003cbr\u003e7.14 Intro to Data Science: pandas Series and DataFrames 177\u003cbr\u003e7.15 Wrap-Up 189\u003cbr\u003e\u003cstrong\u003eChapter 8: Strings: A Deeper Look 191\u003c\/strong\u003e\u003cbr\u003e8.1 Introduction 192\u003cbr\u003e8.2 Formatting Strings 193\u003cbr\u003e8.3 Concatenating and Repeating Strings 196\u003cbr\u003e8.4 Stripping Whitespace from Strings 197\u003cbr\u003e8.5 Changing Character Case 197\u003cbr\u003e8.6 Comparison Operators for Strings 198\u003cbr\u003e8.7 Searching for Substrings 198\u003cbr\u003e8.8 Replacing Substrings 199\u003cbr\u003e8.9 Splitting and Joining Strings 200\u003cbr\u003e8.10 Characters and Character-Testing Methods 202\u003cbr\u003e8.11 Raw Strings 203\u003cbr\u003e8.12 Introduction to Regular Expressions 203\u003cbr\u003e8.13 Intro to Data Science: Pandas, Regular Expressions and Data Munging 210\u003cbr\u003e8.14 Wrap-Up 214\u003cbr\u003e\u003cstrong\u003eChapter 9: Files and Exceptions 217\u003c\/strong\u003e\u003cbr\u003e9.1 Introduction 218\u003cbr\u003e9.2 Files 219\u003cbr\u003e9.3 Text-File Processing 219\u003cbr\u003e9.4 Updating Text Files 222\u003cbr\u003e9.5 Serialization with JSON 223\u003cbr\u003e9.6 Focus on Security: pickle Serialization and Deserialization 226\u003cbr\u003e9.7 Additional Notes Regarding Files 226\u003cbr\u003e9.8 Handling Exceptions 227\u003cbr\u003e9.9 finally Clause 231\u003cbr\u003e9.10 Explicitly Raising an Exception 233\u003cbr\u003e9.11 (Optional) Stack Unwinding and Tracebacks 233\u003cbr\u003e9.12 Intro to Data Science: Working with CSV Files 235\u003cbr\u003e9.13 Wrap-Up 241\u003cbr\u003e\u003cstrong\u003eChapter 10: Object-Oriented Programming 243\u003c\/strong\u003e\u003cbr\u003e10.1 Introduction 244\u003cbr\u003e10.2 Custom Class Account 246\u003cbr\u003e10.3 Controlling Access to Attributes 249\u003cbr\u003e10.4 Properties for Data Access 250\u003cbr\u003e10.5 Simulating “Private” Attributes 256\u003cbr\u003e10.6 Case Study: Card Shuffling and Dealing Simulation 258\u003cbr\u003e10.7 Inheritance: Base Classes and Subclasses 266\u003cbr\u003e10.8 Building an Inheritance Hierarchy; Introducing Polymorphism 267\u003cbr\u003e10.9 Duck Typing and Polymorphism 275\u003cbr\u003e10.10 Operator Overloading 276\u003cbr\u003e10.11 Exception Class Hierarchy and Custom Exceptions 279\u003cbr\u003e10.12 Named Tuples 280\u003cbr\u003e10.13 A Brief Intro to Python 3.7’s New Data Classes 281\u003cbr\u003e10.14 Unit Testing with Docstrings and doctest 287\u003cbr\u003e10.15 Namespaces and Scopes 290\u003cbr\u003e10.16 Intro to Data Science: Time Series and Simple Linear Regression 293\u003cbr\u003e10.17 Wrap-Up 301\u003cbr\u003e\u003cstrong\u003eChapter 11: Natural Language Processing (NLP) 303\u003c\/strong\u003e\u003cbr\u003e11.1 Introduction 304\u003cbr\u003e11.2 TextBlob 305\u003cbr\u003e11.3 Visualizing Word Frequencies with Bar Charts and Word Clouds 319\u003cbr\u003e11.4 Readability Assessment with Textatistic 324\u003cbr\u003e11.5 Named Entity Recognition with spaCy 326\u003cbr\u003e11.6 Similarity Detection with spaCy 327\u003cbr\u003e11.7 Other NLP Libraries and Tools 328\u003cbr\u003e11.8 Machine Learning and Deep Learning Natural Language Applications 328\u003cbr\u003e11.9 Natural Language Datasets 329\u003cbr\u003e11.10 Wrap-Up 330\u003cbr\u003e\u003cstrong\u003eChapter 12: Data Mining Twitter 331\u003c\/strong\u003e\u003cbr\u003e12.1 Introduction 332\u003cbr\u003e12.2 Overview of the Twitter APIs 334\u003cbr\u003e12.3 Creating a Twitter Account 335\u003cbr\u003e12.4 Getting Twitter Credentials—Creating an App 335\u003cbr\u003e12.5 What’s in a Tweet? 337\u003cbr\u003e12.6 Tweepy 340\u003cbr\u003e12.7 Authenticating with Twitter Via Tweepy 341\u003cbr\u003e12.8 Getting Information About a Twitter Account 342\u003cbr\u003e12.9 Introduction to Tweepy Cursors: Getting an Account’s Followers and Friends 344\u003cbr\u003e12.10 Searching Recent Tweets 347\u003cbr\u003e12.11 Spotting Trends: Twitter Trends API 349\u003cbr\u003e12.12 Cleaning\/Preprocessing Tweets for Analysis 353\u003cbr\u003e12.13 Twitter Streaming API 354\u003cbr\u003e12.14 Tweet Sentiment Analysis 359\u003cbr\u003e12.15 Geocoding and Mapping 362\u003cbr\u003e12.16 Ways to Store Tweets 370\u003cbr\u003e12.17 Twitter and Time Series 370\u003cbr\u003e12.18 Wrap-Up 371\u003cbr\u003e\u003cstrong\u003eChapter 13: IBM Watson and Cognitive Computing 373\u003c\/strong\u003e\u003cbr\u003e13.1 Introduction: IBM Watson and Cognitive Computing 374\u003cbr\u003e13.2 IBM Cloud Account and Cloud Console 375\u003cbr\u003e13.3 Watson Services 376\u003cbr\u003e13.4 Additional Services and Tools 379\u003cbr\u003e13.5 Watson Developer Cloud Python SDK 381\u003cbr\u003e13.6 Case Study: Traveler’s Companion Translation App 381\u003cbr\u003e13.7 Watson Resources 394\u003cbr\u003e13.8 Wrap-Up 395\u003cbr\u003e\u003cstrong\u003eChapter 14: Machine Learning: Classification, Regression and Clustering 397\u003c\/strong\u003e\u003cbr\u003e14.1 Introduction to Machine Learning 398\u003cbr\u003e14.2 Case Study: Classification with k-Nearest Neighbors and the Digits Dataset, Part 1 403\u003cbr\u003e14.3 Case Study: Classification with k-Nearest Neighbors and the Digits Dataset, Part 2 413\u003cbr\u003e14.4 Case Study: Time Series and Simple Linear Regression 420\u003cbr\u003e14.5 Case Study: Multiple Linear Regression with the California Housing Dataset 425\u003cbr\u003e14.6 Case Study: Unsupervised Machine Learning, Part 1—Dimensionality Reduction 438\u003cbr\u003e14.7 Case Study: Unsupervised Machine Learning, Part 2—k-Means Clustering 442\u003cbr\u003e14.8 Wrap-Up 455\u003cbr\u003e\u003cstrong\u003eChapter 15: Deep Learning 457\u003c\/strong\u003e\u003cbr\u003e15.1 Introduction 458\u003cbr\u003e15.2 Keras Built-In Datasets 461\u003cbr\u003e15.3 Custom Anaconda Environments 462\u003cbr\u003e15.4 Neural Networks 463\u003cbr\u003e15.5 Tensors 465\u003cbr\u003e15.6 Convolutional Neural Networks for Vision; Multi-Classification with the MNIST Dataset 467\u003cbr\u003e15.7 Visualizing Neural Network Training with TensorBoard 486\u003cbr\u003e15.8 ConvnetJS: Browser-Based Deep-Learning Training and Visualization 489\u003cbr\u003e15.9 Recurrent Neural Networks for Sequences; Sentiment Analysis with the IMDb Dataset 489\u003cbr\u003e15.10 Tuning Deep Learning Models 497\u003cbr\u003e15.11 Convnet Models Pretrained on ImageNet 498\u003cbr\u003e15.12 Wrap-Up 499\u003cbr\u003e\u003cstrong\u003eChapter 16: Big Data: Hadoop, Spark, NoSQL and IoT 501\u003c\/strong\u003e\u003cbr\u003e16.1 Introduction 502\u003cbr\u003e16.2 Relational Databases and Structured Query Language (SQL) 506\u003cbr\u003e16.3 NoSQL and NewSQL Big-Data Databases: A Brief Tour 517\u003cbr\u003e16.4 Case Study: A MongoDB JSON Document Database 520\u003cbr\u003e16.5 Hadoop 530\u003cbr\u003e16.6 Spark 541\u003cbr\u003e16.7 Spark Streaming: Counting Twitter Hashtags Using the pyspark-notebook Docker Stack 551\u003cbr\u003e16.8 Internet of Things and Dashboards 560\u003cbr\u003e16.9 Wrap-Up 571\u003cbr\u003e\u003cem\u003eIndex 573\u003c\/em\u003e\u003c\/p\u003e","brand":"Pearson Education (US)","offers":[{"title":"Default Title","offer_id":48732340191575,"sku":"9780135224335","price":999.99,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780135224335.jpg?v=1719996479"},{"product_id":"microsoft-azure-data-solutions-an-introduction-9780137252503","title":"Microsoft Azure Data Solutions  An Introduction","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp class=\"MsoNormal\" style=\"margin:\" calibri sans-serif font-size:=\"\" font-family:=\"\"\u003e\u003cb\u003eDaniel A. Seara\u003c\/b\u003e is an experienced software developer. He has more than 20 years as a technical instructor, developer, and development consultant. Daniel has worked as a software consultant in a wide range of companies in Argentina, Spain, and Peru. He has been asked by Peruvian Microsoft Consulting Services to help several companies in their migration path to .NET development. Daniel was Argentina's Microsoft regional director for four years and was the first nominated global regional director, a position he held for two years. He also was the manager of the Desarrollador Cinco Estrellas I (Five Star Developer) program, one of the most successful training projects in Latin America. Daniel held Visual Basic MVP status for more than 10 years, as well as SharePoint Server MVP status from 2008 until 2014. Additionally, Daniel is the founder and dean of Universidad. NET, the most-visited Spanish-\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1. Understanding Azure Data Solutions    \u003cbr\u003e    2. Implementing Azure Data Storage Solutions    \u003cbr\u003e    3. Managing and Developing Data Processing for Azure Data Solutions    \u003cbr\u003e    4. Monitoring and Optimizing Azure Data Solutions    \u003cbr\u003e\u003c\/p\u003e","brand":"Pearson Education (US)","offers":[{"title":"Default Title","offer_id":48732341240151,"sku":"9780137252503","price":32.29,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780137252503.jpg?v=1719996482"},{"product_id":"mathematics-of-big-data-9780262038393","title":"Mathematics of Big Data","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e","brand":"MIT Press Ltd","offers":[{"title":"Default Title","offer_id":48733448765783,"sku":"9780262038393","price":72.2,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780262038393.jpg?v=1720000119"},{"product_id":"az-password-book-9780593435823","title":"AZ Password Book","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eThis large-format, alphabetized password book is organized by tabs per letter, making it easy, fast, and safe to store and locate important login information of all kinds!\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cul\u003e\n\u003cli\u003e\n\u003cb\u003eIndividual tabs for each letter—\u003c\/b\u003eno more tabs cramming multiple letters into the same space! Perfect for faster lookups and better organization.\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eRemovable sticker to go incognito!\u003c\/b\u003e Don''t want text on the cover sharing that it’s a password book? Peel it off!\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eBonus security tips\u003c\/b\u003e to encourage maximized online safety. What to do (and what not to do) to stay ahead of scammers. \u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eLarge trim size \u003c\/b\u003efor extra space to record over 400 accounts, including important notes, password changes, and non-traditional records such as crypto logins.\u003c\/li\u003e\n\u003c\/ul\u003e","brand":"Random House USA Inc","offers":[{"title":"Default Title","offer_id":48735773163863,"sku":"9780593435823","price":8.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780593435823.jpg?v=1723810332"},{"product_id":"a-handson-introduction-to-data-science-9781108472449","title":"A HandsOn Introduction to Data Science","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis book introduces the field of data science in a practical and accessible manner, using a hands-on approach that assumes no prior knowledge of the subject. The foundational ideas and techniques of data science are provided independently from technology, allowing students to easily develop a firm understanding of the subject without a strong technical background, as well as being presented with material that will have continual relevance even after tools and technologies change. Using popular data science tools such as Python and R, the book offers many examples of real-life applications, with practice ranging from small to big data. A suite of online material for both instructors and students provides a strong supplement to the book, including datasets, chapter slides, solutions, sample exams and curriculum suggestions. This entry-level textbook is ideally suited to readers from a range of disciplines wishing to build a practical, working knowledge of data science.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e'Chirag's extensive experience as a teacher shines through in this textbook, which lives up to its promise to be a 'hands on' introduction to data science. Students have a chance to apply their learning to real-life examples from diverse fields, with hands-on examples that build on basic techniques and utilize tools of data science practice throughout the book. I am particularly pleased to see him weave human issues into his approach, putting principles ahead of particular tools and pointing to ethical challenges at various stages of working with data to help his audience develop an appreciation of ways context and interpretation shape data practices. He exposes students to a more nuanced perspective in which human as well as machine input shapes data science outcomes. It is an awareness that we all will need if we are to use data appropriately to tackle the complex challenges we face today.' Theresa Dirndorfer Anderson\u003cbr\u003e'Dr. Shah has written a fabulous introduction to data science for a broad audience. His book offers many learning opportunities, including explanations of core principles, thought-provoking conceptual questions, and hands-on examples and exercises. It will help readers gain proficiency in this important area and quickly start deriving insights from data.' Ryen W. White, Microsoft Research AI\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePart I. Introduction: 1. Introduction; 2. Data; 3. Techniques; Part II. Tools: 4. UNIX; 5. Python; 6. R; 7. MySQL; Part III. Machine Learning: 8. Machine learning introduction and regression; 9. Supervised learning; 10. Unsupervised learning; Part IV. Applications and Evaluations: 11. Hands-on with solving data problems; 12. Data collection, experimentation and evaluation.","brand":"Cambridge University Press","offers":[{"title":"Default Title","offer_id":48738302984535,"sku":"9781108472449","price":41.79,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781108472449.jpg?v=1723811904"},{"product_id":"computational-approaches-to-the-network-science-of-teams-9781108498548","title":"Computational Approaches to the Network Science","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eBusiness operations in large organizations today involve massive, interactive, and layered networks of teams and personnel collaborating across hierarchies and countries on complex tasks. To optimize productivity, businesses need to know: what communication patterns do high-performing teams have in common? Is it possible to predict a team''s performance before it starts work on a project? How can productive team behavior be fostered? This comprehensive review for researchers and practitioners in data mining and social networks surveys recent progress in the emerging field of network science of teams. Focusing on the underlying social network structure, the authors present models and algorithms characterizing, predicting, optimizing, and explaining team performance, along with key applications, open challenges, and future trends.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e'This is a timely book for team science, with a unique perspective that uses computational approaches to study the network effect on team performance. The book has a nice balance of theory, algorithms, and empirical studies. The authors possess years of experience in the field.' Charu Aggarwal, IBM Research AI\u003cbr\u003e'A comprehensive study that pushes forward our understanding of and ability to forecast and design team performance - a critical, yet complex human-subject phenomenon to which this book brings in-depth technical rigor.' Leman Akoglu, Carnegie Mellon University\u003cbr\u003e'This pioneering book is essential to technologists, data scientists, and researchers alike, offering a modern, computational approach to the science of teaming and how to manage the convergence of people, information, and technology in networked organizations.' Norbou Buchler, US Army Data and Analysis Center\u003cbr\u003e'Li and Tong have provided a thorough and insightful exploration of current research on teams in networks, linking computational techniques with results from the social sciences. A pleasure to read.' Sucheta Soundarajan, Syracuse University\u003cbr\u003e'This brief volume is a valuable resource for managers, but managers with a strong background in data science, and for other technologists involved in designing systems that support user interactions … The added value of this book is provided by the mathematical formalisms used, which encode characteristics of the computational challenges discussed … The topical focus results in a unique volume that might lead interested readers to discover new research avenues … Recommended' J. Brzezinski, Choice\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1. Introduction; 2. Team performance characterization; 3. Team performance prediction; 4. Team performance optimization; 5. Team performance explanation; 6. Human agent teaming; 7. Conclusion and future work.","brand":"Cambridge University Press","offers":[{"title":"Default Title","offer_id":48738315829591,"sku":"9781108498548","price":41.79,"currency_code":"GBP","in_stock":true}]},{"product_id":"fundamentals-of-database-systems-global-edition-9781292097619","title":"Fundamentals of Database Systems Global Edition","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cul\u003e\n\u003cli\u003e\n\u003cb\u003ePart 1: Introduction to Databases\u003c\/b\u003e \u003c\/li\u003e\n\u003cli\u003eChapter 1: Databases and Database Users \u003c\/li\u003e\n\u003cli\u003eChapter 2: Database Systems Concepts and Architecture \u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003ePart 2: Conceptual Data Modeling and Database Design\u003c\/b\u003e \u003c\/li\u003e\n\u003cli\u003eChapter 3: Data Modeling Using the Entity Relationship (ER) Model \u003c\/li\u003e\n\u003cli\u003eChapter 4: The Enhanced Entity Relationship (EER) Model \u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003ePart 3: The Relational Data Model and SQL\u003c\/b\u003e \u003c\/li\u003e\n\u003cli\u003eChapter 5: The Relational Data Model and Relational Database Constraints \u003c\/li\u003e\n\u003cli\u003eChapter 6: Basic SQL \u003c\/li\u003e\n\u003cli\u003eChapter 7: More SQL: Complex Queries, Triggers, Views, and Schema Modification \u003c\/li\u003e\n\u003cli\u003eChapter 8: The Relational Algebra and Relational Calculus \u003c\/li\u003e\n\u003cli\u003eChapter 9: Relational Database Design by ER- and EER-to-Relational Mapping \u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003ePart 4: Database Programming Techniques\u003c\/b\u003e \u003c\/li\u003e\n\u003cli\u003eChapter 10: Introduction to SQL Programming Techniques \u003c\/li\u003e\n\u003cli\u003eChapter 11: Web Database Programming Using PHP \u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003ePart 5: Object, Object-Relational, and XML: Concepts, Models, Languages, and Standards\u003c\/b\u003e \u003c\/li\u003e\n\u003cli\u003eChapter 12: Object and Object-Relational Databases \u003c\/li\u003e\n\u003cli\u003eChapter 13: XLM: Extensible Markup Language \u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003ePart 6: Database Design Theory and Normalization\u003c\/b\u003e \u003c\/li\u003e\n\u003cli\u003eChapter 14: Basics of Functional Dependencies and Normalization for Relational Databases \u003c\/li\u003e\n\u003cli\u003eChapter 15: Relational Database Design Algorithms and Further Dependencies \u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003ePart 7: File Structures, Hashing, Indexing, and Physical Database Design\u003c\/b\u003e \u003c\/li\u003e\n\u003cli\u003eChapter 16: Disc Storage, Basic File Structures, Hashing, and Modern Storage Architectures \u003c\/li\u003e\n\u003cli\u003eChapter 17: Indexing Structures for Files and Physical Database Design \u003c\/li\u003e\n\u003cli\u003e\u003cb\u003ePart 8: Query Processing and Optimization \u003c\/b\u003e\u003c\/li\u003e\n\u003cli\u003eChapter 18: Strategies for Query Processing \u003c\/li\u003e\n\u003cli\u003eChapter 19: Query Optimization \u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003ePart 9: Transaction Processing, Concurrency Control, and Recovering\u003c\/b\u003e \u003c\/li\u003e\n\u003cli\u003eChapter 20: Introduction to Transaction Processing Concepts and Theory \u003c\/li\u003e\n\u003cli\u003eChapter 21: Concurrency Control Techniques \u003c\/li\u003e\n\u003cli\u003eChapter 22: Database Recovery Techniques \u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003ePart 10: Distributed Databases, NOSQL Systems, Cloud Computing, and Big Data\u003c\/b\u003e \u003c\/li\u003e\n\u003cli\u003eChapter 23: Distributed Database Concepts \u003c\/li\u003e\n\u003cli\u003eChapter 24: NOSQL Databases and Big Data Storage Systems \u003c\/li\u003e\n\u003cli\u003eChapter 25: Big Data Technologies Based on MapReduce and Hadoop \u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003ePart 11: Advanced Database Models, Systems, and Applications\u003c\/b\u003e \u003c\/li\u003e\n\u003cli\u003eChapter 26: Enhanced Data Models: Introduction to Active, Temporal, Spatial, Multimedia, and Deductive Databases \u003c\/li\u003e\n\u003cli\u003eChapter 27: Introduction to Information Retrieval and Web Search \u003c\/li\u003e\n\u003cli\u003eChapter 28: Data Mining Concepts \u003c\/li\u003e\n\u003cli\u003eChapter 29: Overview of Data Warehousing and OLAP \u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003ePart 12: Additional Database Topics: Security\u003c\/b\u003e \u003c\/li\u003e\n\u003cli\u003eChapter 30: Database Security \u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eAppendix A: \u003c\/b\u003eAlternative Diagrammatic Notations for ER Models \u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eAppendix B: \u003c\/b\u003eParameters of Disks \u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eAppendix C: \u003c\/b\u003eOverview of the QBE 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You will learn how to turn data lakes into business assets.\u003c\/div\u003e\u003cdiv\u003e\u003cbr\u003e\u003c\/div\u003e\u003cdiv\u003eThe data science technology stack demonstrated in \u003ci\u003ePractical Data Science \u003c\/i\u003eis built from components in general use in the industry. Data scientist Andreas Vermeulen demonstrates in detail how to build and provision a technology stack to yield repeatable results. 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Analytics: AI for Data Driven Marketing4.3\tCustomer Data Visualization \u0026amp; Information Management4.4\tMapping Customer Journey through big data analytics             5.\tImproving Experiences and Customer Satisfaction with AI5.1\tAI and Product Life Cycle Management (PLM)5.2\tOpportunities and Challenges of applying AI for PLM5.3\tAI and granular personalization5.4\tUse of AI to provide each segment of a target with tailored content             6.\tValue Creation \u0026amp; Value Capture with Artificial Intelligence6.1\tRole of AI in optimizing Pricing6.2\tOptimizing marketing value, retention and loyalty6.3\tXR on value co-creation and customer engagement6.4\tCreating value with data analytics6.5\tCustomer Value Modelling6.6\tMarketing intelligence for optimal marketing return6.7\tCreating value with data analytics             7.\tReliable \u0026amp; Profitable AI driven Distribution7.1\tUsing AI for Distribution Process Management7.2\tSmart Distribution7.3\tPrediction of consumer behavior and improving lead generation7.4\tOptimizing sales territory design with AI7.5\tAI based delivery system7.6\tAI integrated Logistics, inventory management, warehousing and transportation             8. \tArtificial Intelligence driven Promotions and Social Networking8.1\tNetwork Modelling, Visualization and Analyzing Tools8.2\tRole of Centrality in Social Networks: Influencer Marketing8.3\tSentiment Analysis and Public Opinion Mining8.4\tReview Mining and Rating8.5\tBig Data \u0026amp; scalability in Social Networks8.6\tAI powered Chatbots and conversational experiences8.7\tPropensity modelling for advertisement targeting and lead scoring8.8\tAdvertising Optimization \u0026amp; Viral Effects8.9\tFake News, Misinformation \u0026amp; Rumor Detection            9.\tOptimizing the future of Digital Marketing with A.I.9.1\tEnhancing Interactive User Experience with AI9.2\tContent Creation \u0026amp; Curation with AI9.3\tAligning marketing metrics with business goals9.4\tWeb analytics for digital marketing             10.\tEthics of Artificial Intelligence for Marketing10.1\tDark side of AI in Marketing10.1.1      Consumers’ data protection rights10.1.2       Concerns about AI-enabled marketing decisions 10.1.3      Legal Concerns and Compliance issues10.2\tPiracy, Security and Consumerism10.3\tEthical, Moral \u0026amp; Societal Challenges of AI             11.\tCase Studies on applications of AI11.1\tAI driven cyber security and privacy11.2\tApplications of AI in health care11.3\tApplications of AI in tourism11.4\tApplications of AI in manufacturing11.5\tApplications of AI in finance\u003cbr\u003e","brand":"APress","offers":[{"title":"Default Title","offer_id":48739670819159,"sku":"9781484298091","price":42.49,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781484298091.jpg?v=1720052866"},{"product_id":"kubeflow-for-machine-learning-9781492050124","title":"Kubeflow for 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      The text is extracted directly, word-for-word, from the online course so you can highlight important points and take notes in the “Your Chapter Notes” section.\u003c\/p\u003e \u003cp\u003e·         Headings with the exact page correlations provide a quick reference to the online course for your classroom discussions and exam preparation.\u003c\/p\u003e \u003cp\u003e·         An icon system directs you to the online curriculum to take full advantage of the images embedded within the Networking Academy online course interface and reminds you to perform the labs, Class Activities, interactive activities, Packet Tracer activities, watch videos, and take the chapter quizzes and exams.\u003c\/p\u003e \u003cp\u003e\u003cbr\u003e\u003c\/p\u003e \u003cp\u003eThe Course Booklet is a basic, economical paper-based resource to help you succeed with the Cisco Networking Academy online course.\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003eChapter 0\u003c\/b\u003e Course Introduction 1\u003c\/p\u003e \u003cp\u003e0.0 Welcome to CCNA: Cybersecurity Operations 1\u003c\/p\u003e \u003cp\u003e    0.0.1 Message to the Student 1\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1\u003c\/b\u003e Cybersecurity and the Security Operations Center 5\u003c\/p\u003e \u003cp\u003e1.0 Introduction 5\u003c\/p\u003e \u003cp\u003e1.1 The Danger 5\u003c\/p\u003e \u003cp\u003e    1.1.1 War Stories 5\u003c\/p\u003e \u003cp\u003e        1.1.1.1 Hijacked People 5\u003c\/p\u003e \u003cp\u003e        1.1.1.2 Ransomed Companies 5\u003c\/p\u003e \u003cp\u003e        1.1.1.3 Targeted Nations 6\u003c\/p\u003e \u003cp\u003e        1.1.1.4 Lab - Installing the CyberOps Workstation Virtual Machine 6\u003c\/p\u003e \u003cp\u003e        1.1.1.5 Lab - Cybersecurity Case Studies 6\u003c\/p\u003e \u003cp\u003e    1.1.2 Threat Actors 6\u003c\/p\u003e \u003cp\u003e        1.1.2.1 Amateurs 6\u003c\/p\u003e \u003cp\u003e        1.1.2.2 Hacktivists 7\u003c\/p\u003e \u003cp\u003e        1.1.2.3 Financial Gain 7\u003c\/p\u003e \u003cp\u003e        1.1.2.4 Trade Secrets and Global Politics 7\u003c\/p\u003e \u003cp\u003e        1.1.2.5 How Secure is the Internet of Things? 7\u003c\/p\u003e \u003cp\u003e        1.1.2.6 Lab - Learning the Details of Attacks 7\u003c\/p\u003e \u003cp\u003e    1.1.3 Threat Impact 8\u003c\/p\u003e \u003cp\u003e        1.1.3.1 PII and PHI 8\u003c\/p\u003e \u003cp\u003e        1.1.3.2 Lost Competitive Advantage 8\u003c\/p\u003e \u003cp\u003e        1.1.3.3 Politics and National Security 8\u003c\/p\u003e \u003cp\u003e        1.1.3.4 Lab - Visualizing the Black Hats 9\u003c\/p\u003e \u003cp\u003e1.2 Fighters in the War Against Cybercrime 9\u003c\/p\u003e \u003cp\u003e    1.2.1 The Modern Security Operations Center 9\u003c\/p\u003e \u003cp\u003e        1.2.1.1 Elements of a SOC 9\u003c\/p\u003e \u003cp\u003e        1.2.1.2 People in the SOC 9\u003c\/p\u003e \u003cp\u003e        1.2.1.3 Process in the SOC 10\u003c\/p\u003e \u003cp\u003e        1.2.1.4 Technologies in the SOC 10\u003c\/p\u003e \u003cp\u003e        1.2.1.5 Enterprise and Managed Security 10\u003c\/p\u003e \u003cp\u003e        1.2.1.6 Security vs. Availability 11\u003c\/p\u003e \u003cp\u003e        1.2.1.7 Activity - Identify the SOC Terminology 11\u003c\/p\u003e \u003cp\u003e    1.2.2 Becoming a Defender 11\u003c\/p\u003e \u003cp\u003e        1.2.2.1 Certifications 11\u003c\/p\u003e \u003cp\u003e        1.2.2.2 Further Education 12\u003c\/p\u003e \u003cp\u003e        1.2.2.3 Sources of Career Information 12\u003c\/p\u003e \u003cp\u003e        1.2.2.4 Getting Experience 13\u003c\/p\u003e \u003cp\u003e        1.2.2.5 Lab - Becoming a Defender 13\u003c\/p\u003e \u003cp\u003e1.3 Summary 13\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2\u003c\/b\u003e Windows Operating System 17\u003c\/p\u003e \u003cp\u003e2.0 Introduction 17\u003c\/p\u003e \u003cp\u003e2.1 Windows Overview 17\u003c\/p\u003e \u003cp\u003e    2.1.1 Windows History 17\u003c\/p\u003e \u003cp\u003e        2.1.1.1 Disk Operating System 17\u003c\/p\u003e \u003cp\u003e        2.1.1.2 Windows Versions 18\u003c\/p\u003e \u003cp\u003e        2.1.1.3 Windows GUI 19\u003c\/p\u003e \u003cp\u003e        2.1.1.4 Operating System Vulnerabilities 19\u003c\/p\u003e \u003cp\u003e    2.1.2 Windows Architecture and Operations 20\u003c\/p\u003e \u003cp\u003e        2.1.2.1 Hardware Abstraction Layer 20\u003c\/p\u003e \u003cp\u003e        2.1.2.2 User Mode and Kernel Mode 21\u003c\/p\u003e \u003cp\u003e        2.1.2.3 Windows File Systems 21\u003c\/p\u003e \u003cp\u003e        2.1.2.4 Windows Boot Process 23\u003c\/p\u003e \u003cp\u003e        2.1.2.5 Windows Startup and Shutdown 24\u003c\/p\u003e \u003cp\u003e        2.1.2.6 Processes, Threads, and Services 25\u003c\/p\u003e \u003cp\u003e        2.1.2.7 Memory Allocation and Handles 25\u003c\/p\u003e \u003cp\u003e        2.1.2.8 The Windows Registry 26\u003c\/p\u003e \u003cp\u003e        2.1.2.9 Activity - Identify the Windows Registry Hive 27\u003c\/p\u003e \u003cp\u003e        2.1.2.10 Lab - Exploring Processes, Threads, Handles, and Windows Registry 27\u003c\/p\u003e \u003cp\u003e2.2 Windows Administration 27\u003c\/p\u003e \u003cp\u003e    2.2.1 Windows Configuration and Monitoring 27\u003c\/p\u003e \u003cp\u003e        2.2.1.1 Run as Administrator 27\u003c\/p\u003e \u003cp\u003e        2.2.1.2 Local Users and Domains 27\u003c\/p\u003e \u003cp\u003e        2.2.1.3 CLI and PowerShell 28\u003c\/p\u003e \u003cp\u003e        2.2.1.4 Windows Management Instrumentation 29\u003c\/p\u003e \u003cp\u003e        2.2.1.5 The net Command 30\u003c\/p\u003e \u003cp\u003e        2.2.1.6 Task Manager and Resource Monitor 30\u003c\/p\u003e \u003cp\u003e        2.2.1.7 Networking 31\u003c\/p\u003e \u003cp\u003e        2.2.1.8 Accessing Network Resources 33\u003c\/p\u003e \u003cp\u003e        2.2.1.9 Windows Server 33\u003c\/p\u003e \u003cp\u003e        2.2.1.10 Lab - Create User Accounts 34\u003c\/p\u003e \u003cp\u003e        2.2.1.11 Lab - Using Windows PowerShell 34\u003c\/p\u003e \u003cp\u003e        2.2.1.12 Lab - Windows Task Manager 34\u003c\/p\u003e \u003cp\u003e        2.2.1.13 Lab - Monitor and Manage System Resources in Windows 34\u003c\/p\u003e \u003cp\u003e    2.2.2 Windows Security 34\u003c\/p\u003e \u003cp\u003e        2.2.2.1 The netstat Command 34\u003c\/p\u003e \u003cp\u003e        2.2.2.2 Event Viewer 35\u003c\/p\u003e \u003cp\u003e        2.2.2.3 Windows Update Management 35\u003c\/p\u003e \u003cp\u003e        2.2.2.4 Local Security Policy 35\u003c\/p\u003e \u003cp\u003e        2.2.2.5 Windows Defender 36\u003c\/p\u003e \u003cp\u003e        2.2.2.6 Windows Firewall 37\u003c\/p\u003e \u003cp\u003e        2.2.2.7 Activity - Identify the Windows Command 37\u003c\/p\u003e \u003cp\u003e        2.2.2.8 Activity - Identify the Windows Tool 37\u003c\/p\u003e \u003cp\u003e2.3 Summary 37\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3\u003c\/b\u003e Linux Operating System 41\u003c\/p\u003e \u003cp\u003e3.0 Introduction 41\u003c\/p\u003e \u003cp\u003e3.1 Linux Overview 41\u003c\/p\u003e \u003cp\u003e    3.1.1 Linux Basics 41\u003c\/p\u003e \u003cp\u003e        3.1.1.1 What is Linux? 41\u003c\/p\u003e \u003cp\u003e        3.1.1.2 The Value of Linux 42\u003c\/p\u003e \u003cp\u003e        3.1.1.3 Linux in the SOC 42\u003c\/p\u003e \u003cp\u003e        3.1.1.4 Linux Tools 43\u003c\/p\u003e \u003cp\u003e    3.1.2 Working in the Linux Shell 43\u003c\/p\u003e \u003cp\u003e        3.1.2.1 The Linux Shell 43\u003c\/p\u003e \u003cp\u003e        3.1.2.2 Basic Commands 43\u003c\/p\u003e \u003cp\u003e        3.1.2.3 File and Directory Commands 44\u003c\/p\u003e \u003cp\u003e        3.1.2.4 Working with Text Files 44\u003c\/p\u003e \u003cp\u003e        3.1.2.5 The Importance of Text Files in Linux 44\u003c\/p\u003e \u003cp\u003e        3.1.2.6 Lab - Working with Text Files in the CLI 45\u003c\/p\u003e \u003cp\u003e        3.1.2.7 Lab - Getting Familiar with the Linux Shell 45\u003c\/p\u003e \u003cp\u003e    3.1.3 Linux Servers and Clients 45\u003c\/p\u003e \u003cp\u003e        3.1.3.1 An Introduction to Client-Server Communications 45\u003c\/p\u003e \u003cp\u003e        3.1.3.2 Servers, Services, and Their Ports 45\u003c\/p\u003e \u003cp\u003e        3.1.3.3 Clients 45\u003c\/p\u003e \u003cp\u003e        3.1.3.4 Lab - Linux Servers 45\u003c\/p\u003e \u003cp\u003e3.2 Linux Administration 46\u003c\/p\u003e \u003cp\u003e    3.2.1 Basic Server Administration 46\u003c\/p\u003e \u003cp\u003e        3.2.1.1 Service Configuration Files 46\u003c\/p\u003e \u003cp\u003e        3.2.1.2 Hardening Devices 46\u003c\/p\u003e \u003cp\u003e        3.2.1.3 Monitoring Service Logs 47\u003c\/p\u003e \u003cp\u003e        3.2.1.4 Lab - Locating Log Files 48\u003c\/p\u003e \u003cp\u003e    3.2.2 The Linux File System 48\u003c\/p\u003e \u003cp\u003e        3.2.2.1 The File System Types in Linux 48\u003c\/p\u003e \u003cp\u003e        3.2.2.2 Linux Roles and File Permissions 49\u003c\/p\u003e \u003cp\u003e        3.2.2.3 Hard Links and Symbolic Links 50\u003c\/p\u003e \u003cp\u003e        3.2.2.4 Lab - Navigating the Linux Filesystem and Permission Settings 50\u003c\/p\u003e \u003cp\u003e3.3 Linux Hosts 51\u003c\/p\u003e \u003cp\u003e    3.3.1 Working with the Linux GUI 51\u003c\/p\u003e \u003cp\u003e        3.3.1.1 X Window System 51\u003c\/p\u003e \u003cp\u003e        3.3.1.2 The Linux GUI 51\u003c\/p\u003e \u003cp\u003e    3.3.2 Working on a Linux Host 52\u003c\/p\u003e \u003cp\u003e        3.3.2.1 Installing and Running Applications on a Linux Host 52\u003c\/p\u003e \u003cp\u003e        3.3.2.2 Keeping the System Up To Date 52\u003c\/p\u003e \u003cp\u003e        3.3.2.3 Processes and Forks 52\u003c\/p\u003e \u003cp\u003e        3.3.2.4 Malware on a Linux Host 53\u003c\/p\u003e \u003cp\u003e        3.3.2.5 Rootkit Check 54\u003c\/p\u003e \u003cp\u003e        3.3.2.6 Piping Commands 54\u003c\/p\u003e \u003cp\u003e        3.3.2.7 Video Demonstration - Applications, Rootkits, and Piping Commands 55\u003c\/p\u003e \u003cp\u003e3.4 Summary 55\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4\u003c\/b\u003e Network Protocols and Services 59\u003c\/p\u003e \u003cp\u003e4.0 Introduction 59\u003c\/p\u003e \u003cp\u003e4.1 Network Protocols 59\u003c\/p\u003e \u003cp\u003e    4.1.1 Network Communications Process 59\u003c\/p\u003e \u003cp\u003e        4.1.1.1 Views of the Network 59\u003c\/p\u003e \u003cp\u003e        4.1.1.2 Client-Server Communications 60\u003c\/p\u003e \u003cp\u003e        4.1.1.3 A Typical Session: Student 60\u003c\/p\u003e \u003cp\u003e        4.1.1.4 A Typical Session: Gamer 61\u003c\/p\u003e \u003cp\u003e        4.1.1.5 A Typical Session: Surgeon 61\u003c\/p\u003e \u003cp\u003e        4.1.1.6 Tracing the Path 62\u003c\/p\u003e \u003cp\u003e        4.1.1.7 Lab - Tracing a Route 62\u003c\/p\u003e \u003cp\u003e    4.1.2 Communications Protocols 62\u003c\/p\u003e \u003cp\u003e        4.1.2.1 What are Protocols? 62\u003c\/p\u003e \u003cp\u003e        4.1.2.2 Network Protocol Suites 63\u003c\/p\u003e \u003cp\u003e        4.1.2.3 The TCP\/IP Protocol Suite 63\u003c\/p\u003e \u003cp\u003e        4.1.2.4 Format, Size, and Timing 64\u003c\/p\u003e \u003cp\u003e        4.1.2.5 Unicast, Multicast, and Broadcast 64\u003c\/p\u003e \u003cp\u003e        4.1.2.6 Reference Models 65\u003c\/p\u003e \u003cp\u003e        4.1.2.7 Three Addresses 65\u003c\/p\u003e \u003cp\u003e        4.1.2.8 Encapsulation 65\u003c\/p\u003e \u003cp\u003e        4.1.2.9 Scenario: Sending and Receiving a Web Page 66\u003c\/p\u003e \u003cp\u003e        4.1.2.10 Lab - Introduction to Wireshark 67\u003c\/p\u003e \u003cp\u003e4.2 Ethernet and Internet Protocol (IP) 67\u003c\/p\u003e \u003cp\u003e    4.2.1 Ethernet 67\u003c\/p\u003e \u003cp\u003e        4.2.1.1 The Ethernet Protocol 67\u003c\/p\u003e \u003cp\u003e        4.2.1.2 The Ethernet Frame 68\u003c\/p\u003e \u003cp\u003e        4.2.1.3 MAC Address Format 68\u003c\/p\u003e \u003cp\u003e        4.2.1.4 Activity - Ethernet Frame Fields 68\u003c\/p\u003e \u003cp\u003e    4.2.2 IPv4 68\u003c\/p\u003e \u003cp\u003e        4.2.2.1 IPv4 Encapsulation 68\u003c\/p\u003e \u003cp\u003e        4.2.2.2 IPv4 Characteristics 69\u003c\/p\u003e \u003cp\u003e        4.2.2.3 Activity - IPv4 Characteristics 70\u003c\/p\u003e \u003cp\u003e        4.2.2.4 The IPv4 Packet 70\u003c\/p\u003e \u003cp\u003e        4.2.2.5 Video Demonstration - Sample IPv4 Headers in Wireshark 70\u003c\/p\u003e \u003cp\u003e    4.2.3 IPv4 Addressing Basics 70\u003c\/p\u003e \u003cp\u003e        4.2.3.1 IPv4 Address Notation 70\u003c\/p\u003e \u003cp\u003e        4.2.3.2 IPv4 Host Address Structure 70\u003c\/p\u003e \u003cp\u003e        4.2.3.3 IPv4 Subnet Mask and Network Address 71\u003c\/p\u003e \u003cp\u003e        4.2.3.4 Subnetting Broadcast Domains 71\u003c\/p\u003e \u003cp\u003e        4.2.3.5 Video Demonstration - Network, Host, and Broadcast Addresses 72\u003c\/p\u003e \u003cp\u003e    4.2.4 Types of IPv4 Addresses 72\u003c\/p\u003e \u003cp\u003e        4.2.4.1 IPv4 Address Classes and Default Subnet Masks 72\u003c\/p\u003e \u003cp\u003e        4.2.4.2 Reserved Private Addresses 73\u003c\/p\u003e \u003cp\u003e    4.2.5 The Default Gateway 73\u003c\/p\u003e \u003cp\u003e        4.2.5.1 Host Forwarding Decision 73\u003c\/p\u003e \u003cp\u003e        4.2.5.2 Default Gateway 74\u003c\/p\u003e \u003cp\u003e        4.2.5.3 Using the Default Gateway 74\u003c\/p\u003e \u003cp\u003e    4.2.6 IPv6 75\u003c\/p\u003e \u003cp\u003e        4.2.6.1 Need for IPv6 75\u003c\/p\u003e \u003cp\u003e        4.2.6.2 IPv6 Size and Representation 75\u003c\/p\u003e \u003cp\u003e        4.2.6.3 IPv6 Address Formatting 75\u003c\/p\u003e \u003cp\u003e        4.2.6.4 IPv6 Prefix Length 76\u003c\/p\u003e \u003cp\u003e        4.2.6.5 Activity - IPv6 Address Notation 76\u003c\/p\u003e \u003cp\u003e        4.2.6.6 Video Tutorial - Layer 2 and Layer 3 Addressing 76\u003c\/p\u003e \u003cp\u003e4.3 Connectivity Verification 76\u003c\/p\u003e \u003cp\u003e    4.3.1 ICMP 76\u003c\/p\u003e \u003cp\u003e        4.3.1.1 ICMPv4 Messages 76\u003c\/p\u003e \u003cp\u003e        4.3.1.2 ICMPv6 RS and RA Messages 77\u003c\/p\u003e \u003cp\u003e    4.3.2 Ping and Traceroute Utilities 78\u003c\/p\u003e \u003cp\u003e        4.3.2.1 Ping - Testing the Local Stack 78\u003c\/p\u003e \u003cp\u003e        4.3.2.2 Ping - Testing Connectivity to the Local LAN 79\u003c\/p\u003e \u003cp\u003e        4.3.2.3 Ping - Testing Connectivity to Remote Host 79\u003c\/p\u003e \u003cp\u003e        4.3.2.4 Traceroute - Testing the Path 80\u003c\/p\u003e \u003cp\u003e        4.3.2.5 ICMP Packet Format 80\u003c\/p\u003e \u003cp\u003e4.4 Address Resolution Protocol 81\u003c\/p\u003e \u003cp\u003e    4.4.1 MAC and IP 81\u003c\/p\u003e \u003cp\u003e        4.4.1.1 Destination on Same Network 81\u003c\/p\u003e \u003cp\u003e        4.4.1.2 Destination on Remote Network 82\u003c\/p\u003e \u003cp\u003e    4.4.2 ARP 82\u003c\/p\u003e \u003cp\u003e        4.4.2.1 Introduction to ARP 82\u003c\/p\u003e \u003cp\u003e        4.4.2.2 ARP Functions 82\u003c\/p\u003e \u003cp\u003e        4.4.2.3 Video - ARP Operation - ARP Request 83\u003c\/p\u003e \u003cp\u003e        4.4.2.4 Video - ARP Operation - ARP Reply 84\u003c\/p\u003e \u003cp\u003e        4.4.2.5 Video - ARP Role in Remote Communication 84\u003c\/p\u003e \u003cp\u003e        4.4.2.6 Removing Entries from an ARP Table 85\u003c\/p\u003e \u003cp\u003e        4.4.2.7 ARP Tables on Networking Devices 85\u003c\/p\u003e \u003cp\u003e        4.4.2.8 Lab - Using Wireshark to Examine Ethernet Frames 85\u003c\/p\u003e \u003cp\u003e    4.4.3 ARP Issues 85\u003c\/p\u003e \u003cp\u003e        4.4.3.1 ARP Broadcasts 85\u003c\/p\u003e \u003cp\u003e        4.4.3.2 ARP Spoofing 86\u003c\/p\u003e \u003cp\u003e4.5 The Transport Layer 86\u003c\/p\u003e \u003cp\u003e    4.5.1 Transport Layer Characteristics 86\u003c\/p\u003e \u003cp\u003e        4.5.1.1 Transport Layer Protocol Role in Network Communication 86\u003c\/p\u003e \u003cp\u003e        4.5.1.2 Transport Layer Mechanisms 87\u003c\/p\u003e \u003cp\u003e        4.5.1.3 TCP Local and Remote Ports 87\u003c\/p\u003e \u003cp\u003e        4.5.1.4 Socket Pairs 88\u003c\/p\u003e \u003cp\u003e        4.5.1.5 TCP vs UDP 88\u003c\/p\u003e \u003cp\u003e        4.5.1.6 TCP and UDP Headers 89\u003c\/p\u003e \u003cp\u003e        4.5.1.7 Activity - Compare TCP and UDP Characteristics 90\u003c\/p\u003e \u003cp\u003e    4.5.2 Transport Layer Operation 90\u003c\/p\u003e \u003cp\u003e        4.5.2.1 TCP Port Allocation 90\u003c\/p\u003e \u003cp\u003e        4.5.2.2 A TCP Session Part I: Connection Establishment and Termination 91\u003c\/p\u003e \u003cp\u003e        4.5.2.3 Video Demonstration - TCP 3-Way Handshake 92\u003c\/p\u003e \u003cp\u003e        4.5.2.4 Lab - Using Wireshark to Observe the TCP 3-Way Handshake 92\u003c\/p\u003e \u003cp\u003e        4.5.2.5 Activity - TCP Connection and Termination Process 92\u003c\/p\u003e \u003cp\u003e        4.5.2.6 A TCP Session Part II: Data Transfer 92\u003c\/p\u003e \u003cp\u003e        4.5.2.7 Video Demonstration - Sequence Numbers and Acknowledgments 94\u003c\/p\u003e \u003cp\u003e        4.5.2.8 Video Demonstration - Data Loss and Retransmission 94\u003c\/p\u003e \u003cp\u003e        4.5.2.9 A UDP Session 94\u003c\/p\u003e \u003cp\u003e        4.5.2.10 Lab - Exploring Nmap 95\u003c\/p\u003e \u003cp\u003e4.6 Network Services 95\u003c\/p\u003e \u003cp\u003e    4.6.1 DHCP 95\u003c\/p\u003e \u003cp\u003e        4.6.1.1 DHCP Overview 95\u003c\/p\u003e \u003cp\u003e        4.6.1.2 DHCPv4 Message Format 96\u003c\/p\u003e \u003cp\u003e    4.6.2 DNS 97\u003c\/p\u003e \u003cp\u003e        4.6.2.1 DNS Overview 97\u003c\/p\u003e \u003cp\u003e        4.6.2.2 The DNS Domain Hierarchy 97\u003c\/p\u003e \u003cp\u003e        4.6.2.3 The DNS Lookup Process 97\u003c\/p\u003e \u003cp\u003e        4.6.2.4 DNS Message Format 98\u003c\/p\u003e \u003cp\u003e        4.6.2.5 Dynamic DNS 99\u003c\/p\u003e \u003cp\u003e        4.6.2.6 The WHOIS Protocol 99\u003c\/p\u003e \u003cp\u003e        4.6.2.7 Lab - Using Wireshark to Examine a UDP DNS Capture 100\u003c\/p\u003e \u003cp\u003e    4.6.3 NAT 100\u003c\/p\u003e \u003cp\u003e        4.6.3.1 NAT Overview 100\u003c\/p\u003e \u003cp\u003e        4.6.3.2 NAT-Enabled Routers 100\u003c\/p\u003e \u003cp\u003e        4.6.3.3 Port Address Translation 100\u003c\/p\u003e \u003cp\u003e    4.6.4 File Transfer and Sharing Services 101\u003c\/p\u003e \u003cp\u003e        4.6.4.1 FTP and TFTP 101\u003c\/p\u003e \u003cp\u003e        4.6.4.2 SMB 102\u003c\/p\u003e \u003cp\u003e        4.6.4.3 Lab - Using Wireshark to Examine TCP and UDP Captures 102\u003c\/p\u003e \u003cp\u003e    4.6.5 Email 102\u003c\/p\u003e \u003cp\u003e        4.6.5.1 Email Overview 102\u003c\/p\u003e \u003cp\u003e        4.6.5.2 SMTP 102\u003c\/p\u003e \u003cp\u003e        4.6.5.3 POP3 103\u003c\/p\u003e \u003cp\u003e        4.6.5.4 IMAP 103\u003c\/p\u003e \u003cp\u003e    4.6.6 HTTP 103\u003c\/p\u003e \u003cp\u003e        4.6.6.1 HTTP Overview 103\u003c\/p\u003e \u003cp\u003e        4.6.6.2 The HTTP URL 104\u003c\/p\u003e \u003cp\u003e        4.6.6.3 The HTTP Protocol 104\u003c\/p\u003e \u003cp\u003e        4.6.6.4 HTTP Status Codes 105\u003c\/p\u003e \u003cp\u003e        4.6.6.5 Lab - Using Wireshark to Examine HTTP and HTTPS Traffic 105\u003c\/p\u003e \u003cp\u003e4.7 Summary 105\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5\u003c\/b\u003e Network Infrastructure 109\u003c\/p\u003e \u003cp\u003e5.0 Introduction 109\u003c\/p\u003e \u003cp\u003e5.1 Network Communication Devices 109\u003c\/p\u003e \u003cp\u003e    5.1.1 Network Devices 109\u003c\/p\u003e \u003cp\u003e        5.1.1.1 End Devices 109\u003c\/p\u003e \u003cp\u003e        5.1.1.2 Video Tutorial - End Devices 109\u003c\/p\u003e \u003cp\u003e        5.1.1.3 Routers 110\u003c\/p\u003e \u003cp\u003e        5.1.1.4 Activity - Match Layer 2 and Layer 3 Addressing 110\u003c\/p\u003e \u003cp\u003e        5.1.1.5 Router Operation 110\u003c\/p\u003e \u003cp\u003e        5.1.1.6 Routing Information 111\u003c\/p\u003e \u003cp\u003e        5.1.1.7 Video Tutorial - Static and Dynamic Routing 112\u003c\/p\u003e \u003cp\u003e        5.1.1.8 Hubs, Bridges, LAN Switches 112\u003c\/p\u003e \u003cp\u003e        5.1.1.9 Switching Operation 113\u003c\/p\u003e \u003cp\u003e        5.1.1.10 Video Tutorial - MAC Address Tables on Connected Switches 114\u003c\/p\u003e \u003cp\u003e        5.1.1.11 VLANs 114\u003c\/p\u003e \u003cp\u003e        5.1.1.12 STP 114\u003c\/p\u003e \u003cp\u003e        5.1.1.13 Multilayer Switching 115\u003c\/p\u003e \u003cp\u003e    5.1.2 Wireless Communications 116\u003c\/p\u003e \u003cp\u003e        5.1.2.1 Video Tutorial - Wireless Communications 116\u003c\/p\u003e \u003cp\u003e        5.1.2.2 Protocols and Features 116\u003c\/p\u003e \u003cp\u003e        5.1.2.3 Wireless Network Operations 117\u003c\/p\u003e \u003cp\u003e        5.1.2.4 The Client to AP Association Process 118\u003c\/p\u003e \u003cp\u003e        5.1.2.5 Activity - Order the Steps in the Client and AP Association Process 119\u003c\/p\u003e \u003cp\u003e        5.1.2.6 Wireless Devices - AP, LWAP, WLC 119\u003c\/p\u003e \u003cp\u003e        5.1.2.7 Activity - Identify the LAN Device 119\u003c\/p\u003e \u003cp\u003e5.2 Network Security Infrastructure 120\u003c\/p\u003e \u003cp\u003e    5.2.1 Security Devices 120\u003c\/p\u003e \u003cp\u003e        5.2.1.1 Video Tutorial - Security Devices 120\u003c\/p\u003e \u003cp\u003e        5.2.1.2 Firewalls 120\u003c\/p\u003e \u003cp\u003e        5.2.1.3 Firewall Type Descriptions 120\u003c\/p\u003e \u003cp\u003e        5.2.1.4 Packet Filtering Firewalls 121\u003c\/p\u003e \u003cp\u003e        5.2.1.5 Stateful Firewalls 121\u003c\/p\u003e \u003cp\u003e        5.2.1.6 Next-Generation Firewalls 121\u003c\/p\u003e \u003cp\u003e        5.2.1.7 Activity - Identify the Type of Firewall 122\u003c\/p\u003e \u003cp\u003e        5.2.1.8 Intrusion Protection and Detection Devices 122\u003c\/p\u003e \u003cp\u003e        5.2.1.9 Advantages and Disadvantages of IDS and IPS 122\u003c\/p\u003e \u003cp\u003e        5.2.1.10 Types of IPS 123\u003c\/p\u003e \u003cp\u003e        5.2.1.11 Specialized Security Appliances 124\u003c\/p\u003e \u003cp\u003e        5.2.1.12 Activity - Compare IDS and IPS Characteristics 125\u003c\/p\u003e \u003cp\u003e    5.2.2 Security Services 125\u003c\/p\u003e \u003cp\u003e        5.2.2.1 Video Tutorial - Security Services 125\u003c\/p\u003e \u003cp\u003e        5.2.2.2 Traffic Control with ACLs 125\u003c\/p\u003e \u003cp\u003e        5.2.2.3 ACLs: Important Features 126\u003c\/p\u003e \u003cp\u003e        5.2.2.4 Packet Tracer - ACL Demonstration 126\u003c\/p\u003e \u003cp\u003e        5.2.2.5 SNMP 126\u003c\/p\u003e \u003cp\u003e        5.2.2.6 NetFlow 127\u003c\/p\u003e \u003cp\u003e        5.2.2.7 Port Mirroring 127\u003c\/p\u003e \u003cp\u003e        5.2.2.8 Syslog Servers 128\u003c\/p\u003e \u003cp\u003e        5.2.2.9 NTP 128\u003c\/p\u003e \u003cp\u003e        5.2.2.10 AAA Servers 129\u003c\/p\u003e \u003cp\u003e        5.2.2.11 VPN 130\u003c\/p\u003e \u003cp\u003e        5.2.2.12 Activity - Identify the Network Security Device or Service 130\u003c\/p\u003e \u003cp\u003e5.3 Network Representations 130\u003c\/p\u003e \u003cp\u003e    5.3.1 Network Topologies 130\u003c\/p\u003e \u003cp\u003e        5.3.1.1 Overview of Network Components 130\u003c\/p\u003e \u003cp\u003e        5.3.1.2 Physical and Logical Topologies 131\u003c\/p\u003e \u003cp\u003e        5.3.1.3 WAN Topologies 131\u003c\/p\u003e \u003cp\u003e        5.3.1.4 LAN Topologies 131\u003c\/p\u003e \u003cp\u003e        5.3.1.5 The Three-Layer Network Design Model 132\u003c\/p\u003e \u003cp\u003e        5.3.1.6 Video Tutorial - Three-Layer Network Design 132\u003c\/p\u003e \u003cp\u003e        5.3.1.7 Common Security Architectures 133\u003c\/p\u003e \u003cp\u003e        5.3.1.8 Activity - Identify the Network Topology 134\u003c\/p\u003e \u003cp\u003e        5.3.1.9 Activity - Identify the Network Design Terminology 134\u003c\/p\u003e \u003cp\u003e        5.3.1.10 Packet Tracer - Identify Packet Flow 134\u003c\/p\u003e \u003cp\u003e5.4 Summary 134\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6\u003c\/b\u003e Principles of Network Security 137\u003c\/p\u003e \u003cp\u003e6.0 Introduction 137\u003c\/p\u003e \u003cp\u003e6.1 Attackers and Their Tools 137\u003c\/p\u003e \u003cp\u003e    6.1.1 Who is Attacking Our Network? 137\u003c\/p\u003e \u003cp\u003e        6.1.1.1 Threat, Vulnerability, and Risk 137\u003c\/p\u003e \u003cp\u003e        6.1.1.2 Hacker vs. Threat Actor 138\u003c\/p\u003e \u003cp\u003e        6.1.1.3 Evolution of Threat Actors 138\u003c\/p\u003e \u003cp\u003e        6.1.1.4 Cybercriminals 139\u003c\/p\u003e \u003cp\u003e        6.1.1.5 Cybersecurity Tasks 139\u003c\/p\u003e \u003cp\u003e        6.1.1.6 Cyber Threat Indicators 139\u003c\/p\u003e \u003cp\u003e        6.1.1.7 Activity - What Color is my Hat? 140\u003c\/p\u003e \u003cp\u003e    6.1.2 Threat Actor Tools 140\u003c\/p\u003e \u003cp\u003e        6.1.2.1 Introduction of Attack Tools 140\u003c\/p\u003e \u003cp\u003e        6.1.2.2 Evolution of Security Tools 140\u003c\/p\u003e \u003cp\u003e        6.1.2.3 Categories of Attacks 141\u003c\/p\u003e \u003cp\u003e        6.1.2.4 Activity - Classify Hacking Tools 141\u003c\/p\u003e \u003cp\u003e6.2 Common Threats and Attacks 141\u003c\/p\u003e \u003cp\u003e    6.2.1 Malware 141\u003c\/p\u003e \u003cp\u003e        6.2.1.1 Types of Malware 141\u003c\/p\u003e \u003cp\u003e        6.2.1.2 Viruses 141\u003c\/p\u003e \u003cp\u003e        6.2.1.3 Trojan Horses 141\u003c\/p\u003e \u003cp\u003e        6.2.1.4 Trojan Horse Classification 142\u003c\/p\u003e \u003cp\u003e        6.2.1.5 Worms 142\u003c\/p\u003e \u003cp\u003e        6.2.1.6 Worm Components 143\u003c\/p\u003e \u003cp\u003e        6.2.1.7 Ransomware 143\u003c\/p\u003e \u003cp\u003e        6.2.1.8 Other Malware 144\u003c\/p\u003e \u003cp\u003e        6.2.1.9 Common Malware Behaviors 144\u003c\/p\u003e \u003cp\u003e        6.2.1.10 Activity - Identify the Malware Type 145\u003c\/p\u003e \u003cp\u003e        6.2.1.11 Lab - Anatomy of Malware 145\u003c\/p\u003e \u003cp\u003e    6.2.2 Common Network Attacks 145\u003c\/p\u003e \u003cp\u003e        6.2.2.1 Types of Network Attacks 145\u003c\/p\u003e \u003cp\u003e        6.2.2.2 Reconnaissance Attacks 145\u003c\/p\u003e \u003cp\u003e        6.2.2.3 Sample Reconnaissance Attacks 146\u003c\/p\u003e \u003cp\u003e        6.2.2.4 Access Attacks 146\u003c\/p\u003e \u003cp\u003e        6.2.2.5 Types of Access Attacks 147\u003c\/p\u003e \u003cp\u003e        6.2.2.6 Social Engineering Attacks 147\u003c\/p\u003e \u003cp\u003e        6.2.2.7 Phishing Social Engineering Attacks 148\u003c\/p\u003e \u003cp\u003e        6.2.2.8 Strengthening the Weakest Link 149\u003c\/p\u003e \u003cp\u003e        6.2.2.9 Lab - Social Engineering 149\u003c\/p\u003e \u003cp\u003e        6.2.2.10 Denial of Service Attacks 149\u003c\/p\u003e \u003cp\u003e        6.2.2.11 DDoS Attacks 149\u003c\/p\u003e \u003cp\u003e        6.2.2.12 Example DDoS Attack 150\u003c\/p\u003e \u003cp\u003e        6.2.2.13 Buffer Overflow Attack 150\u003c\/p\u003e \u003cp\u003e        6.2.2.14 Evasion Methods 151\u003c\/p\u003e \u003cp\u003e        6.2.2.15 Activity - Identify the Types of Network Attack 151\u003c\/p\u003e \u003cp\u003e        6.2.2.16 Activity - Components of a DDoS Attack 151\u003c\/p\u003e \u003cp\u003e6.3 Summary 152\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7\u003c\/b\u003e Network Attacks: A Deeper Look 155\u003c\/p\u003e \u003cp\u003e7.0 Introduction 155\u003c\/p\u003e \u003cp\u003e7.1 Attackers and Their Tools 155\u003c\/p\u003e \u003cp\u003e    7.1.1 Who is Attacking Our Network? 155\u003c\/p\u003e \u003cp\u003e        7.1.1.1 Network Security Topology 155\u003c\/p\u003e \u003cp\u003e        7.1.1.2 Monitoring the Network 156\u003c\/p\u003e \u003cp\u003e        7.1.1.3 Network Taps 156\u003c\/p\u003e \u003cp\u003e        7.1.1.4 Traffic Mirroring and SPAN 156\u003c\/p\u003e \u003cp\u003e    7.1.2 Introduction to Network Monitoring Tools 157\u003c\/p\u003e \u003cp\u003e        7.1.2.1 Network Security Monitoring Tools 157\u003c\/p\u003e \u003cp\u003e        7.1.2.2 Network Protocol Analyzers 157\u003c\/p\u003e \u003cp\u003e        7.1.2.3 NetFlow 158\u003c\/p\u003e \u003cp\u003e        7.1.2.4 SIEM 159\u003c\/p\u003e \u003cp\u003e        7.1.2.5 SIEM Systems 159\u003c\/p\u003e \u003cp\u003e        7.1.2.6 Activity - Identify the Network Monitoring Tool 159\u003c\/p\u003e \u003cp\u003e        7.1.2.7 Packet Tracer - Logging Network Activity 159\u003c\/p\u003e \u003cp\u003e7.2 Attacking the Foundation 160\u003c\/p\u003e \u003cp\u003e    7.2.1 IP Vulnerabilities and Threats 160\u003c\/p\u003e \u003cp\u003e        7.2.1.1 IPv4 and IPv6 160\u003c\/p\u003e \u003cp\u003e        7.2.1.2 The IPv4 Packet Header 160\u003c\/p\u003e \u003cp\u003e        7.2.1.3 The IPv6 Packet Header 161\u003c\/p\u003e \u003cp\u003e        7.2.1.4 IP Vulnerabilities 161\u003c\/p\u003e \u003cp\u003e        7.2.1.5 ICMP Attacks 162\u003c\/p\u003e \u003cp\u003e        7.2.1.6 DoS Attacks 163\u003c\/p\u003e \u003cp\u003e        7.2.1.7 Amplification and Reflection Attacks 163\u003c\/p\u003e \u003cp\u003e        7.2.1.8 DDoS Attacks 163\u003c\/p\u003e \u003cp\u003e        7.2.1.9 Address Spoofing Attacks 164\u003c\/p\u003e \u003cp\u003e        7.2.1.10 Activity - Identify the IP Vulnerability 164\u003c\/p\u003e \u003cp\u003e        7.2.1.11 Lab - Observing a DDoS Attack 164\u003c\/p\u003e \u003cp\u003e    7.2.2 TCP and UDP Vulnerabilities 165\u003c\/p\u003e \u003cp\u003e        7.2.2.1 TCP 165\u003c\/p\u003e \u003cp\u003e        7.2.2.2 TCP Attacks 165\u003c\/p\u003e \u003cp\u003e        7.2.2.3 UDP and UDP Attacks 166\u003c\/p\u003e \u003cp\u003e        7.2.2.4 Lab - Observing TCP Anomalies 166\u003c\/p\u003e \u003cp\u003e7.3 Attacking What We Do 167\u003c\/p\u003e \u003cp\u003e    7.3.1 IP Services 167\u003c\/p\u003e \u003cp\u003e        7.3.1.1 ARP Vulnerabilities 167\u003c\/p\u003e \u003cp\u003e        7.3.1.2 ARP Cache Poisoning 167\u003c\/p\u003e \u003cp\u003e        7.3.1.3 DNS Attacks 168\u003c\/p\u003e \u003cp\u003e        7.3.1.4 DNS Tunneling 169\u003c\/p\u003e \u003cp\u003e        7.3.1.5 DHCP 169\u003c\/p\u003e \u003cp\u003e        7.3.1.6 Lab - Exploring DNS Traffic 170\u003c\/p\u003e \u003cp\u003e    7.3.2 Enterprise Services 170\u003c\/p\u003e \u003cp\u003e        7.3.2.1 HTTP and HTTPS 170\u003c\/p\u003e \u003cp\u003e        7.3.2.2 Email 173\u003c\/p\u003e \u003cp\u003e        7.3.2.3 Web-Exposed Databases 174\u003c\/p\u003e \u003cp\u003e        7.3.2.4 Lab - Attacking a MySQL Database 176\u003c\/p\u003e \u003cp\u003e        7.3.2.5 Lab - Reading Server Logs 176\u003c\/p\u003e \u003cp\u003e        7.3.2.6 Lab - Reading Server Logs 176\u003c\/p\u003e \u003cp\u003e7.4 Summary 176\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8\u003c\/b\u003e Protecting the Network 179\u003c\/p\u003e \u003cp\u003e8.0 Introduction 179\u003c\/p\u003e \u003cp\u003e8.1 Understanding Defense 179\u003c\/p\u003e \u003cp\u003e    8.1.1 Defense-in-Depth 179\u003c\/p\u003e \u003cp\u003e        8.1.1.1 Assets, Vulnerabilities, Threats 179\u003c\/p\u003e \u003cp\u003e        8.1.1.2 Identify Assets 179\u003c\/p\u003e \u003cp\u003e        8.1.1.3 Identify Vulnerabilities 180\u003c\/p\u003e \u003cp\u003e        8.1.1.4 Identify Threats 181\u003c\/p\u003e \u003cp\u003e        8.1.1.5 Security Onion and Security Artichoke Approaches 181\u003c\/p\u003e \u003cp\u003e    8.1.2 Security Policies 182\u003c\/p\u003e \u003cp\u003e        8.1.2.1 Business Policies 182\u003c\/p\u003e \u003cp\u003e        8.1.2.2 Security Policy 182\u003c\/p\u003e \u003cp\u003e        8.1.2.3 BYOD Policies 183\u003c\/p\u003e \u003cp\u003e        8.1.2.4 Regulatory and Standard Compliance 184\u003c\/p\u003e \u003cp\u003e8.2 Access Control 184\u003c\/p\u003e \u003cp\u003e    8.2.1 Access Control Concepts 184\u003c\/p\u003e \u003cp\u003e        8.2.1.1 Communications Security: CIA 184\u003c\/p\u003e \u003cp\u003e        8.2.1.2 Access Control Models 185\u003c\/p\u003e \u003cp\u003e        8.2.1.3 Activity - Identify the Access Control Model 185\u003c\/p\u003e \u003cp\u003e    8.2.2 AAA Usage and Operation 185\u003c\/p\u003e \u003cp\u003e        8.2.2.1 AAA Operation 185\u003c\/p\u003e \u003cp\u003e        8.2.2.2 AAA Authentication 186\u003c\/p\u003e \u003cp\u003e        8.2.2.3 AAA Accounting Logs 187\u003c\/p\u003e \u003cp\u003e        8.2.2.4 Activity - Identify the Characteristic of AAA 187\u003c\/p\u003e \u003cp\u003e8.3 Threat Intelligence 187\u003c\/p\u003e \u003cp\u003e    8.3.1 Information Sources 187\u003c\/p\u003e \u003cp\u003e        8.3.1.1 Network Intelligence Communities 187\u003c\/p\u003e \u003cp\u003e        8.3.1.2 Cisco Cybersecurity Reports 188\u003c\/p\u003e \u003cp\u003e        8.3.1.3 Security Blogs and Podcasts 188\u003c\/p\u003e \u003cp\u003e    8.3.2 Threat Intelligence Services 188\u003c\/p\u003e \u003cp\u003e        8.3.2.1 Cisco Talos 188\u003c\/p\u003e \u003cp\u003e        8.3.2.2 FireEye 189\u003c\/p\u003e \u003cp\u003e        8.3.2.3 Automated Indicator Sharing 189\u003c\/p\u003e \u003cp\u003e        8.3.2.4 Common Vulnerabilities and Exposures Database 189\u003c\/p\u003e \u003cp\u003e        8.3.2.5 Threat Intelligence Communication Standards 189\u003c\/p\u003e \u003cp\u003e        8.3.2.6 Activity - Identify the Threat Intelligence Information Source 190\u003c\/p\u003e \u003cp\u003e8.4 Summary 190\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9\u003c\/b\u003e Cryptography and the Public Key Infrastructure 193\u003c\/p\u003e \u003cp\u003e9.0 Introduction 193\u003c\/p\u003e \u003cp\u003e9.1 Cryptography 193\u003c\/p\u003e \u003cp\u003e    9.1.1 What is Cryptography? 193\u003c\/p\u003e \u003cp\u003e        9.1.1.1 Securing Communications 193\u003c\/p\u003e \u003cp\u003e        9.1.1.2 Cryptology 194\u003c\/p\u003e \u003cp\u003e        9.1.1.3 Cryptography - Ciphers 195\u003c\/p\u003e \u003cp\u003e        9.1.1.4 Cryptanalysis - Code Breaking 195\u003c\/p\u003e \u003cp\u003e        9.1.1.5 Keys 196\u003c\/p\u003e \u003cp\u003e        9.1.1.6 Lab - Encrypting and Decrypting Data Using OpenSSL 197\u003c\/p\u003e \u003cp\u003e        9.1.1.7 Lab - Encrypting and Decrypting Data Using a Hacker Tool 197\u003c\/p\u003e \u003cp\u003e        9.1.1.8 Lab - Examining Telnet and SSH in Wireshark 197\u003c\/p\u003e \u003cp\u003e    9.1.2 Integrity and Authenticity 197\u003c\/p\u003e \u003cp\u003e        9.1.2.1 Cryptographic Hash Functions 197\u003c\/p\u003e \u003cp\u003e        9.1.2.2 Cryptographic Hash Operation 198\u003c\/p\u003e \u003cp\u003e        9.1.2.3 MD5 and SHA 198\u003c\/p\u003e \u003cp\u003e        9.1.2.4 Hash Message Authentication Code 199\u003c\/p\u003e \u003cp\u003e        9.1.2.5 Lab - Hashing Things Out 200\u003c\/p\u003e \u003cp\u003e    9.1.3 Confidentiality 200\u003c\/p\u003e \u003cp\u003e        9.1.3.1 Encryption 200\u003c\/p\u003e \u003cp\u003e        9.1.3.2 Symmetric Encryption 200\u003c\/p\u003e \u003cp\u003e        9.1.3.3 Symmetric Encryption Algorithms 201\u003c\/p\u003e \u003cp\u003e        9.1.3.4 Asymmetric Encryption Algorithms 202\u003c\/p\u003e \u003cp\u003e        9.1.3.5 Asymmetric Encryption - Confidentiality 202\u003c\/p\u003e \u003cp\u003e        9.1.3.6 Asymmetric Encryption - Authentication 203\u003c\/p\u003e \u003cp\u003e        9.1.3.7 Asymmetric Encryption - Integrity 203\u003c\/p\u003e \u003cp\u003e        9.1.3.8 Diffie-Hellman 204\u003c\/p\u003e \u003cp\u003e        9.1.3.9 Activity - Classify the Encryption Algorithms 204\u003c\/p\u003e \u003cp\u003e9.2 Public Key Infrastructure 204\u003c\/p\u003e \u003cp\u003e    9.2.1 Public Key Cryptography 204\u003c\/p\u003e \u003cp\u003e        9.2.1.1 Using Digital Signatures 204\u003c\/p\u003e \u003cp\u003e        9.2.1.2 Digital Signatures for Code Signing 206\u003c\/p\u003e \u003cp\u003e        9.2.1.3 Digital Signatures for Digital Certificates 206\u003c\/p\u003e \u003cp\u003e        9.2.1.4 Lab - Create a Linux Playground 206\u003c\/p\u003e \u003cp\u003e    9.2.2 Authorities and the PKI Trust System 206\u003c\/p\u003e \u003cp\u003e        9.2.2.1 Public Key Management 206\u003c\/p\u003e \u003cp\u003e        9.2.2.2 The Public Key Infrastructure 207\u003c\/p\u003e \u003cp\u003e        9.2.2.3 The PKI Authorities System 207\u003c\/p\u003e \u003cp\u003e        9.2.2.4 The PKI Trust System 208\u003c\/p\u003e \u003cp\u003e        9.2.2.5 Interoperability of Different PKI Vendors 208\u003c\/p\u003e \u003cp\u003e        9.2.2.6 Certificate Enrollment, Authentication, and Revocation 209\u003c\/p\u003e \u003cp\u003e        9.2.2.7 Lab - Certificate Authority Stores 209\u003c\/p\u003e \u003cp\u003e    9.2.3 Applications and Impacts of Cryptography 210\u003c\/p\u003e \u003cp\u003e        9.2.3.1 PKI Applications 210\u003c\/p\u003e \u003cp\u003e        9.2.3.2 Encrypting Network Transactions 210\u003c\/p\u003e \u003cp\u003e        9.2.3.3 Encryption and Security Monitoring 211\u003c\/p\u003e \u003cp\u003e9.3 Summary 212\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10\u003c\/b\u003e Endpoint Security and Analysis 215\u003c\/p\u003e \u003cp\u003e10.0 Introduction 215\u003c\/p\u003e \u003cp\u003e10.1 Endpoint Protection 215\u003c\/p\u003e \u003cp\u003e    10.1.1 Antimalware Protection 215\u003c\/p\u003e \u003cp\u003e        10.1.1.1 Endpoint Threats 215\u003c\/p\u003e \u003cp\u003e        10.1.1.2 Endpoint Security 216\u003c\/p\u003e \u003cp\u003e        10.1.1.3 Host-Based Malware Protection 216\u003c\/p\u003e \u003cp\u003e        10.1.1.4 Network-Based Malware Protection 217\u003c\/p\u003e \u003cp\u003e        10.1.1.5 Cisco Advanced Malware Protection (AMP) 218\u003c\/p\u003e \u003cp\u003e        10.1.1.6 Activity - Identify Antimalware Terms and Concepts 218\u003c\/p\u003e \u003cp\u003e    10.1.2 Host-Based Intrusion Protection 218\u003c\/p\u003e \u003cp\u003e        10.1.2.1 Host-Based Firewalls 218\u003c\/p\u003e \u003cp\u003e        10.1.2.2 Host-Based Intrusion Detection 219\u003c\/p\u003e \u003cp\u003e        10.1.2.3 HIDS Operation 220\u003c\/p\u003e \u003cp\u003e        10.1.2.4 HIDS Products 220\u003c\/p\u003e \u003cp\u003e        10.1.2.5 Activity - Identify the Host-Based Intrusion Protection Terminology 220\u003c\/p\u003e \u003cp\u003e    10.1.3 Application Security 221\u003c\/p\u003e \u003cp\u003e        10.1.3.1 Attack Surface 221\u003c\/p\u003e \u003cp\u003e        10.1.3.2 Application Blacklisting and Whitelisting 221\u003c\/p\u003e \u003cp\u003e        10.1.3.3 System-Based Sandboxing 222\u003c\/p\u003e \u003cp\u003e        10.1.3.4 Video Demonstration - Using a Sandbox to Launch Malware 222\u003c\/p\u003e \u003cp\u003e10.2 Endpoint Vulnerability Assessment 222\u003c\/p\u003e \u003cp\u003e    10.2.1 Network and Server Profiling 222\u003c\/p\u003e \u003cp\u003e        10.2.1.1 Network Profiling 222\u003c\/p\u003e \u003cp\u003e        10.2.1.2 Server Profiling 223\u003c\/p\u003e \u003cp\u003e        10.2.1.3 Network Anomaly Detection 223\u003c\/p\u003e \u003cp\u003e        10.2.1.4 Network Vulnerability Testing 224\u003c\/p\u003e \u003cp\u003e        10.2.1.5 Activity - Identify the Elements of Network Profiling 225\u003c\/p\u003e \u003cp\u003e    10.2.2 Common Vulnerability Scoring System (CVSS) 225\u003c\/p\u003e \u003cp\u003e        10.2.2.1 CVSS Overview 225\u003c\/p\u003e \u003cp\u003e        10.2.2.2 CVSS Metric Groups 225\u003c\/p\u003e \u003cp\u003e        10.2.2.3 CVSS Base Metric Group 226\u003c\/p\u003e \u003cp\u003e        10.2.2.4 The CVSS Process 226\u003c\/p\u003e \u003cp\u003e        10.2.2.5 CVSS Reports 227\u003c\/p\u003e \u003cp\u003e        10.2.2.6 Other Vulnerability Information Sources 227\u003c\/p\u003e \u003cp\u003e        10.2.2.7 Activity - Identify CVSS Metrics 228\u003c\/p\u003e \u003cp\u003e    10.2.3 Compliance Frameworks 228\u003c\/p\u003e \u003cp\u003e        10.2.3.1 Compliance Regulations 228\u003c\/p\u003e \u003cp\u003e        10.2.3.2 Overview of Regulatory Standards 228\u003c\/p\u003e \u003cp\u003e        10.2.3.3 Activity - Identify Regulatory Standards 229\u003c\/p\u003e \u003cp\u003e    10.2.4 Secure Device Management 230\u003c\/p\u003e \u003cp\u003e        10.2.4.1 Risk Management 230\u003c\/p\u003e \u003cp\u003e        10.2.4.2 Activity - Identify the Risk Response 231\u003c\/p\u003e \u003cp\u003e        10.2.4.3 Vulnerability Management 231\u003c\/p\u003e \u003cp\u003e        10.2.4.4 Asset Management 231\u003c\/p\u003e \u003cp\u003e        10.2.4.5 Mobile Device Management 232\u003c\/p\u003e \u003cp\u003e        10.2.4.6 Configuration Management 232\u003c\/p\u003e \u003cp\u003e        10.2.4.7 Enterprise Patch Management 233\u003c\/p\u003e \u003cp\u003e        10.2.4.8 Patch Management Techniques 233\u003c\/p\u003e \u003cp\u003e        10.2.4.9 Activity - Identify Device Management Activities 234\u003c\/p\u003e \u003cp\u003e    10.2.5 Information Security Management Systems 234\u003c\/p\u003e \u003cp\u003e        10.2.5.1 Security Management Systems 234\u003c\/p\u003e \u003cp\u003e        10.2.5.2 ISO-27001 234\u003c\/p\u003e \u003cp\u003e        10.2.5.3 NIST Cybersecurity Framework 234\u003c\/p\u003e \u003cp\u003e        10.2.5.4 Activity - Identify the ISO 27001 Activity Cycle 235\u003c\/p\u003e \u003cp\u003e        10.2.5.5 Activity - Identify the Stages in the NIST Cybersecurity Framework 235\u003c\/p\u003e \u003cp\u003e10.3 Summary 235\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 11\u003c\/b\u003e Security Monitoring 239\u003c\/p\u003e \u003cp\u003e11.0 Introduction 239\u003c\/p\u003e \u003cp\u003e11.1 Technologies and Protocols 239\u003c\/p\u003e \u003cp\u003e    11.1.1 Monitoring Common Protocols 239\u003c\/p\u003e \u003cp\u003e        11.1.1.1 Syslog and NTP 239\u003c\/p\u003e \u003cp\u003e        11.1.1.2 NTP 240\u003c\/p\u003e \u003cp\u003e        11.1.1.3 DNS 240\u003c\/p\u003e \u003cp\u003e        11.1.1.4 HTTP and HTTPS 241\u003c\/p\u003e \u003cp\u003e        11.1.1.5 Email Protocols 241\u003c\/p\u003e \u003cp\u003e        11.1.1.6 ICMP 242\u003c\/p\u003e \u003cp\u003e        11.1.1.7 Activity - Identify the Monitored Protocol 242\u003c\/p\u003e \u003cp\u003e    11.1.2 Security Technologies 242\u003c\/p\u003e \u003cp\u003e        11.1.2.1 ACLs 242\u003c\/p\u003e \u003cp\u003e        11.1.2.2 NAT and PAT 242\u003c\/p\u003e \u003cp\u003e        11.1.2.3 Encryption, Encapsulation, and Tunneling 243\u003c\/p\u003e \u003cp\u003e        11.1.2.4 Peer-to-Peer Networking and Tor 243\u003c\/p\u003e \u003cp\u003e        11.1.2.5 Load Balancing 244\u003c\/p\u003e \u003cp\u003e        11.1.2.6 Activity - Identify the Impact of the Technology on Security and Monitoring 244\u003c\/p\u003e \u003cp\u003e11.2 Log Files 244\u003c\/p\u003e \u003cp\u003e    11.2.1 Types of Security Data 244\u003c\/p\u003e \u003cp\u003e        11.2.1.1 Alert Data 244\u003c\/p\u003e \u003cp\u003e        11.2.1.2 Session and Transaction Data 245\u003c\/p\u003e \u003cp\u003e        11.2.1.3 Full Packet Captures 245\u003c\/p\u003e \u003cp\u003e        11.2.1.4 Statistical Data 246\u003c\/p\u003e \u003cp\u003e        11.2.1.5 Activity - Identify Types of Network Monitoring Data 246\u003c\/p\u003e \u003cp\u003e    11.2.2 End Device Logs 246\u003c\/p\u003e \u003cp\u003e        11.2.2.1 Host Logs 246\u003c\/p\u003e \u003cp\u003e        11.2.2.2 Syslog 247\u003c\/p\u003e \u003cp\u003e        11.2.2.3 Server Logs 248\u003c\/p\u003e \u003cp\u003e        11.2.2.4 Apache Webserver Access Logs 248\u003c\/p\u003e \u003cp\u003e        11.2.2.5 IIS Access Logs 249\u003c\/p\u003e \u003cp\u003e        11.2.2.6 SIEM and Log Collection 249\u003c\/p\u003e \u003cp\u003e        11.2.2.7 Activity - Identify Information in Logged Events 250\u003c\/p\u003e \u003cp\u003e    11.2.3 Network Logs 250\u003c\/p\u003e \u003cp\u003e        11.2.3.1 Tcpdump 250\u003c\/p\u003e \u003cp\u003e        11.2.3.2 NetFlow 250\u003c\/p\u003e \u003cp\u003e        11.2.3.3 Application Visibility and Control 251\u003c\/p\u003e \u003cp\u003e        11.2.3.4 Content Filter Logs 251\u003c\/p\u003e \u003cp\u003e        11.2.3.5 Logging from Cisco Devices 252\u003c\/p\u003e \u003cp\u003e        11.2.3.6 Proxy Logs 252\u003c\/p\u003e \u003cp\u003e        11.2.3.7 NextGen IPS 253\u003c\/p\u003e \u003cp\u003e        11.2.3.8 Activity - Identify the Security Technology from the Data Description 254\u003c\/p\u003e \u003cp\u003e        11.2.3.9 Activity - Identify the NextGen IPS Event Type 254\u003c\/p\u003e \u003cp\u003e        11.2.3.10 Packet Tracer - Explore a NetFlow Implementation 254\u003c\/p\u003e \u003cp\u003e        11.2.3.11 Packet Tracer - Logging from Multiple Sources 254\u003c\/p\u003e \u003cp\u003e11.3 Summary 254\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 12\u003c\/b\u003e Intrusion Data Analysis 257\u003c\/p\u003e \u003cp\u003e12.0 Introduction 257\u003c\/p\u003e \u003cp\u003e12.1 Evaluating Alerts 257\u003c\/p\u003e \u003cp\u003e    12.1.1 Sources of Alerts 257\u003c\/p\u003e \u003cp\u003e        12.1.1.1 Security Onion 257\u003c\/p\u003e \u003cp\u003e        12.1.1.2 Detection Tools for Collecting Alert Data 257\u003c\/p\u003e \u003cp\u003e        12.1.1.3 Analysis Tools 258\u003c\/p\u003e \u003cp\u003e        12.1.1.4 Alert Generation 259\u003c\/p\u003e \u003cp\u003e        12.1.1.5 Rules and Alerts 260\u003c\/p\u003e \u003cp\u003e        12.1.1.6 Snort Rule Structure 260\u003c\/p\u003e \u003cp\u003e        12.1.1.7 Lab - Snort and Firewall Rules 261\u003c\/p\u003e \u003cp\u003e    12.1.2 Overview of Alert Evaluation 262\u003c\/p\u003e \u003cp\u003e        12.1.2.1 The Need for Alert Evaluation 262\u003c\/p\u003e \u003cp\u003e        12.1.2.2 Evaluating Alerts 262\u003c\/p\u003e \u003cp\u003e        12.1.2.3 Deterministic Analysis and Probabilistic Analysis 263\u003c\/p\u003e \u003cp\u003e        12.1.2.4 Activity - Identify Deterministic and Probabilistic Scenarios 264\u003c\/p\u003e \u003cp\u003e        12.1.2.5 Activity - Identify the Alert Classification 264\u003c\/p\u003e \u003cp\u003e12.2 Working with Network Security Data 264\u003c\/p\u003e \u003cp\u003e    12.2.1 A Common Data Platform 264\u003c\/p\u003e \u003cp\u003e        12.2.1.1 ELSA 264\u003c\/p\u003e \u003cp\u003e        12.2.1.2 Data Reduction 264\u003c\/p\u003e \u003cp\u003e        12.2.1.3 Data Normalization 265\u003c\/p\u003e \u003cp\u003e        12.2.1.4 Data Archiving 265\u003c\/p\u003e \u003cp\u003e        12.2.1.5 Lab - Convert Data into a Universal Format 266\u003c\/p\u003e \u003cp\u003e        12.2.1.6 Investigating Process or API Calls 266\u003c\/p\u003e \u003cp\u003e    12.2.2 Investigating Network Data 266\u003c\/p\u003e \u003cp\u003e        12.2.2.1 Working in Sguil 266\u003c\/p\u003e \u003cp\u003e        12.2.2.2 Sguil Queries 267\u003c\/p\u003e \u003cp\u003e        12.2.2.3 Pivoting from Sguil 267\u003c\/p\u003e \u003cp\u003e        12.2.2.4 Event Handling in Sguil 268\u003c\/p\u003e \u003cp\u003e        12.2.2.5 Working in ELSA 268\u003c\/p\u003e \u003cp\u003e        12.2.2.6 Queries in ELSA 269\u003c\/p\u003e \u003cp\u003e        12.2.2.7 Investigating Process or API Calls 269\u003c\/p\u003e \u003cp\u003e        12.2.2.8 Investigating File Details 270\u003c\/p\u003e \u003cp\u003e        12.2.2.9 Lab - Regular Expression Tutorial 270\u003c\/p\u003e \u003cp\u003e        12.2.2.10 Lab - Extract an Executable from a PCAP 270\u003c\/p\u003e \u003cp\u003e    12.2.3 Enhancing the Work of the Cybersecurity Analyst 270\u003c\/p\u003e \u003cp\u003e        12.2.3.1 Dashboards and Visualizations 270\u003c\/p\u003e \u003cp\u003e        12.2.3.2 Workflow Management 271\u003c\/p\u003e \u003cp\u003e12.3 Digital Forensics 271\u003c\/p\u003e \u003cp\u003e    12.3.1 Evidence Handling and Attack Attribution 271\u003c\/p\u003e \u003cp\u003e        12.3.1.1 Digital Forensics 271\u003c\/p\u003e \u003cp\u003e        12.3.1.2 The Digital Forensics Process 272\u003c\/p\u003e \u003cp\u003e        12.3.1.3 Types of Evidence 272\u003c\/p\u003e \u003cp\u003e        12.3.1.4 Evidence Collection Order 273\u003c\/p\u003e \u003cp\u003e        12.3.1.5 Chain of Custody 273\u003c\/p\u003e \u003cp\u003e        12.3.1.6 Data Integrity and Preservation 274\u003c\/p\u003e \u003cp\u003e        12.3.1.7 Attack Attribution 274\u003c\/p\u003e \u003cp\u003e        12.3.1.8 Activity - Identify the Type of Evidence 275\u003c\/p\u003e \u003cp\u003e        12.3.1.9 Activity - Identify the Forensic Technique Terminology 275\u003c\/p\u003e \u003cp\u003e12.4 Summary 275\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 13\u003c\/b\u003e Incident Response and Handling 277\u003c\/p\u003e \u003cp\u003e13.0 Introduction 277\u003c\/p\u003e \u003cp\u003e13.1 Incident Response Models 277\u003c\/p\u003e \u003cp\u003e    13.1.1 The Cyber Kill Chain 277\u003c\/p\u003e \u003cp\u003e        13.1.1.1 Steps of the Cyber Kill Chain 277\u003c\/p\u003e \u003cp\u003e        13.1.1.2 Reconnaissance 278\u003c\/p\u003e \u003cp\u003e        13.1.1.3 Weaponization 278\u003c\/p\u003e \u003cp\u003e        13.1.1.4 Delivery 278\u003c\/p\u003e \u003cp\u003e        13.1.1.5 Exploitation 279\u003c\/p\u003e \u003cp\u003e        13.1.1.6 Installation 279\u003c\/p\u003e \u003cp\u003e        13.1.1.7 Command and Control 279\u003c\/p\u003e \u003cp\u003e        13.1.1.8 Actions on Objectives 279\u003c\/p\u003e \u003cp\u003e        13.1.1.9 Activity - Identify the Kill Chain Step 279\u003c\/p\u003e \u003cp\u003e    13.1.2 The Diamond Model of Intrusion 280\u003c\/p\u003e \u003cp\u003e        13.1.2.1 Diamond Model Overview 280\u003c\/p\u003e \u003cp\u003e        13.1.2.2 Pivoting Across the Diamond Model 280\u003c\/p\u003e \u003cp\u003e        13.1.2.3 The Diamond Model and the Cyber Kill Chain 281\u003c\/p\u003e \u003cp\u003e        13.1.2.4 Activity - Identify the Diamond Model Features 282\u003c\/p\u003e \u003cp\u003e    13.1.3 The VERIS Schema 282\u003c\/p\u003e \u003cp\u003e        13.1.3.1 What is the VERIS Schema? 282\u003c\/p\u003e \u003cp\u003e        13.1.3.2 Create a VERIS Record 282\u003c\/p\u003e \u003cp\u003e        13.1.3.3 Top-Level and Second-Level Elements 283\u003c\/p\u003e \u003cp\u003e        13.1.3.4 The VERIS Community Database 285\u003c\/p\u003e \u003cp\u003e        13.1.3.5 Activity - Apply the VERIS Schema to an Incident 285\u003c\/p\u003e \u003cp\u003e13.2 Incident Handling 285\u003c\/p\u003e \u003cp\u003e    13.2.1 CSIRTs 285\u003c\/p\u003e \u003cp\u003e        13.2.1.1 CSIRT Overview 285\u003c\/p\u003e \u003cp\u003e        13.2.1.2 Types of CSIRTs 286\u003c\/p\u003e \u003cp\u003e        13.2.1.3 CERT 286\u003c\/p\u003e \u003cp\u003e        13.2.1.4 Activity - Match the CSIRT with the CSIRT Goal 287\u003c\/p\u003e \u003cp\u003e    13.2.2 NIST 800-61r2 287\u003c\/p\u003e \u003cp\u003e        13.2.2.1 Establishing an Incident Response Capability 287\u003c\/p\u003e \u003cp\u003e        13.2.2.2 Incident Response Stakeholders 288\u003c\/p\u003e \u003cp\u003e        13.2.2.3 NIST Incident Response Life Cycle 288\u003c\/p\u003e \u003cp\u003e        13.2.2.4 Preparation 289\u003c\/p\u003e \u003cp\u003e        13.2.2.5 Detection and Analysis 290\u003c\/p\u003e \u003cp\u003e        13.2.2.6 Containment, Eradication, and Recovery 291\u003c\/p\u003e \u003cp\u003e        13.2.2.7 Post-Incident Activities 293\u003c\/p\u003e \u003cp\u003e        13.2.2.8 Incident Data Collection and Retention 294\u003c\/p\u003e \u003cp\u003e        13.2.2.9 Reporting Requirements and Information Sharing 295\u003c\/p\u003e \u003cp\u003e        13.2.2.10 Activity - Identify the Incident Response Plan Elements 296\u003c\/p\u003e \u003cp\u003e        13.2.2.11 Activity - Identify the Incident Handling Term 296\u003c\/p\u003e \u003cp\u003e        13.2.2.12 Activity - Identify the Incident Handling Step 296\u003c\/p\u003e \u003cp\u003e        13.2.2.13 Lab - Incident Handling 296\u003c\/p\u003e \u003cp\u003e13.3 Summary 296\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e9781587134371   TOC   3\/7\/2018\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e","brand":"Pearson Education (US)","offers":[{"title":"Default Title","offer_id":48740551426391,"sku":"9781587134371","price":35.04,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781587134371.jpg?v=1720054997"},{"product_id":"graph-powered-machine-learning-9781617295645","title":"Graph-Powered Machine Learning","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eAt its core, machine learning is about efficiently identifying patterns and relationships in data. Many tasks, such as finding associations among terms so you can make accurate search recommendations or locating individuals within a social network who have similar interests, are naturally expressed as graphs.\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003eGraph-Powered Machine Learning introduces you to graph technology concepts, highlighting the role of graphs in machine learning and big data platforms. You’ll get an in-depth look at techniques including data source modeling, algorithm design, link analysis, classification, and clustering. As you master the core concepts, you’ll explore three end-to-end projects that illustrate architectures, best design practices,\u003c\/p\u003e \u003cp\u003eoptimization approaches, and common pitfalls.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eKey Features\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e·   The lifecycle of a machine learning project\u003c\/p\u003e \u003cp\u003e·   Three end-to-end applications\u003c\/p\u003e \u003cp\u003e·   Graphs in big data platforms\u003c\/p\u003e \u003cp\u003e·   Data source modeling\u003c\/p\u003e \u003cp\u003e·   Natural language processing, recommendations, and relevant search\u003c\/p\u003e \u003cp\u003e·   Optimization methods\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003eReaders comfortable with machine learning basics.\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cb\u003eAbout the technology \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBy organizing and analyzing your data as graphs, your applications work more fluidly with graph-centric algorithms like nearest neighbor or page rank where it’s important to quickly identify and exploit relevant relationships. Modern graph data stores, like Neo4j or Amazon Neptune, are readily available tools that support graph-powered machine learning.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e\u003c\/b\u003e \u003c\/p\u003e \u003cp\u003e\u003cb\u003eAlessandro Negro \u003c\/b\u003eis a Chief Scientist at GraphAware. With extensive experience in software development, software architecture, and data management, he has been a speaker at many conferences, such as Java One, Oracle Open World, and Graph Connect. He holds a Ph.D. in Computer Science and has authored several publications on graph-based machine learning.\u003c\/p\u003e","brand":"Manning Publications","offers":[{"title":"Default Title","offer_id":48740643733847,"sku":"9781617295645","price":43.19,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781617295645.jpg?v=1723812315"},{"product_id":"practical-data-migration-9781780175140","title":"Practical Data Migration","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis book is for executives, practitioners, and project managers who are tasked with the movement of data from old systems to a new repository. It is designed as a practical guide and uses a series of steps developed in real-life situations that will get you from an empty new system to one that is populated, working and backed by the user population.  This new edition is updated to take account of changes in technology and the maturing of the market for data migration services, with two brand new chapters. It guarantees to get the dirty old data out of your legacy systems and transform it into clean new data for your new system.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003eFor any practitioner faced with the challenge of delivering a successful data migration, this book is an absolute necessity. -- Dylan Jones * Founder, Data Migration Pro *\u003cbr\u003eThe book Practical Data Migration by Johny Morris is essential reading for any practitioner facing a challenging data migration project. The book covers the most important theoretical aspects but is at the same time very practice-oriented and pragmatic. PDMv3 strategy is the basis for being able to complete projects in time and to budget. -- Dr Andreas Martens * Executive Director, qurix Technology GmbH *\u003cbr\u003ePDMv3 provides a valuable resource for project managers or IT practitioners seeking structured and consistent approaches to data migration, whether with regards to strategy, governance, data analysis, migration design, or testing. PDMv3 guidance, advice, and methodology, where followed, enhances the professionalism of anyone undertaking a data migration role. -- Dr David Hannell MBCS CITP * Writer, Research Analyst, Project Manager *\u003cbr\u003eThroughout this book, Johny Morris provides practical experience-based guidance to both executive and practitioner on how to overcome the challenges of delivering a successful data migration. This edition updated to cover Waterfall, Agile and blended approaches brings this work fully up to date. An absolute ‘must read’ for any individual or team before starting or rescuing a data migration. -- Ian Chapman MBCS CITP * Enterprise Data Architect, Expertechnix Ltd \u0026amp; Committee Member, DAMA UK *\u003cbr\u003eJohny Morris continues to provide clear and honest guidance to anyone who finds themselves having to struggle with the complexities of large-scale data migration. The PDM approach has proved invaluable in guiding the data migration team at Guy’s \u0026amp; St Thomas’ NHS Foundation Trust. -- Dr Tito Castillo MBCS CITP * Founder, Agile Health Informatics Ltd \u0026amp; Associate Vice Chair (Standards), BCS Health \u0026amp; Care Executive *\u003cbr\u003eDigital transformation are impacting all organisation and a big part of the challenge is migrating from your current IT platforms and services. This easy to read book is the must have ‘how to’ guide on data migration for executives, data professionals and practitioners. It provides an accessible and easy to read guide that covers the many forms and dimensions to data migration programmes, from someone who has spent years on the sharp side of PDM. -- Jason B Perkins * Head of Data \u0026amp; Analytics Architecture, BT *\u003cbr\u003eThis is one of very few books I’ve read that I have thoroughly enjoyed from start to finish. Johny Morris’ style of writing is one I really adapt well too, a great mix of theory and experience with highlighted summary, risks and advice passages throughout. Over many years I have stressed the importance that PDM activity is owned and driven by the business, something that is overlooked when technology solutions are designed, tested and implemented.  A ‘must have’ book for any BA or IT professional’s library! -- Mark Thompson * Lead Business Analyst (Banking Division), Close Brothers *\u003cbr\u003eMy first comments on reading Practical Data Migration were ‘AMEN’ and ‘YES’. The key differentiator of PDM from many other methodologies is the word ‘Practical’. Theory has a simple goal of migrating data from a tired old system to a shiny new one. Practical covers ‘user enhancements’. -- Sean Barker FBCS CEng * Retired (previously with BAE Systems) *\u003cbr\u003eThis book is THE authoritative guide to data migration principles and practices and an essential ‘go-to’ guide for anyone in business or IT involved in data migration projects. It is refreshingly jargon-free, and provides a systematic and proven methodology to address both the technical and cultural challenges of data migration. -- Nigel Turner * Principal Consultant, Global Data Strategy Ltd *\u003cbr\u003eWhether this is your first or twenty-first data migration, PDMv3 will gives practical and experienced advice to improve the success of your data migration project. Johny Morris highlights useful tips, real-life experience and golden rules. An essential read for any manager or practitioner. -- Mark Dodd CITP * Think Smart Limited *\u003cbr\u003eData migrations are infrequent, and expertise in this challenging but important field is rare. This new edition of a well-established book, updated to reflect technical advances, is indispensable not only for members of project teams working on data migration but also for their managers. -- Mike Andersson MBCS * Director, Andstrom Consulting \u0026amp; Vice Chair (Standards), BCS Health and Care Executive *\u003cbr\u003ePDMv3 is the perfect guide to data migration operations! Its comprehensive coverage of data migration procedures and themes is incredibly structured and fluid. It is clear, concise, and deliberate in its delivery of what you need to know to move your data from one system to another successfully. -- Kudzai Muchenje * Regional Operations Analyst, African Development Bank Group *\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eINTRODUCTION\u003c\/p\u003e\u003cp\u003e \u003c\/p\u003e\u003cp\u003eSECTION 1: EXECUTIVE OVERVIEW\u003cbr\u003e1 Data migration: what's all the fuss\u003cbr\u003e2 Golden rules and super smart tasks\u003cbr\u003e3 PDMv3 overview \u003cbr\u003e4 Creating a data migration strategy\u003c\/p\u003e\u003cp\u003e \u003c\/p\u003e\u003cp\u003eSECTION 2: TOOLS AND TECHNIQUES\u003cbr\u003e5 Project initiation\u003c\/p\u003e\u003cp\u003e6 Key data stakeholder management and demilitarised zone\u003cbr\u003e7 Landscape analysis\u003cbr\u003e8 Business transformation plan\u003cbr\u003e9 Data quality rules\u003cbr\u003e10 Gap analysis and mapping\u003cbr\u003e11 Migration design and execution\u003cbr\u003e12 Legacy decommissioning\u003c\/p\u003e\u003cp\u003e13 Waterfall versus Agile\u003c\/p\u003e\u003cp\u003e \u003c\/p\u003e\u003cp\u003eSECTION 3: FAILING DATA MIGRATION PROJECTS \u003cbr\u003e14 Rescuing failing data migration projects\u003c\/p\u003e\u003cp\u003e \u003c\/p\u003e\u003cp\u003eAPPENDICES \u003cbr\u003eA1 Data migration strategy checklist\u003c\/p\u003e\u003cp\u003eA2 Fields on a DQR document\/form\u003cbr\u003eA3 Mapping example\u003c\/p\u003e\u003cp\u003eA4 PDMv3 process flow\u003c\/p\u003e\u003cbr\u003e\u003cp\u003e\u003c\/p\u003e","brand":"BCS Learning \u0026 Development Limited","offers":[{"title":"Default Title","offer_id":48740982882647,"sku":"9781780175140","price":999.99,"currency_code":"GBP","in_stock":false}]},{"product_id":"principles-of-data-management-facilitating-information-sharing-9781780175911","title":"Principles of Data Management: Facilitating","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cstrong\u003eData is a valuable corporate asset and its effective management is vital to an organisation’s success and survival. With this book you will learn to master the key principles of data management and use them to implement best practices in your organization.\u003c\/strong\u003e\u003c\/p\u003e \u003cp\u003eThis professional guide covers all the key areas of data management, including database development and corporate data modelling. It is business-focused, providing the knowledge and techniques required to successfully implement the data management function.\u003c\/p\u003e \u003cp\u003eThis fully updated new edition provides new chapters on the most important data topics such as big data, artificial intelligence, linked data and concept systems. Principles of Data Management is fully aligned with syllabus for the BCS Professional Certificate in Data Management Essentials, making this the go-to text to unlocking the value of your data.\u003c\/p\u003e \u003cul\u003e\n\u003cli\u003eIdeal for business managers and all involved in the development of information systems as well as data management professionals\u003c\/li\u003e\n\u003cli\u003eComprehensive and descriptive view of data management\u003c\/li\u003e\n\u003cli\u003eSuitable for all levels, from beginners to advanced learners\u003c\/li\u003e\n\u003cli\u003eMust-read for anyone involved in the development of systems to manage data\u003c\/li\u003e\n\u003c\/ul\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003eThis book is an excellent guide to understanding data management theory and techniques. It works at all levels: from beginner to advanced, and from reference source to the practicalities of implementation. I would highly recommend to anyone wanting to get to grips with data management, regardless of experience in the field. -- Ian Wallis, Managing Director, Data Strategists Ltd\u003cbr\u003eKeith has developed a broad and thorough understanding of all aspects of data management over many years, so is without doubt one of the authorities on data management. This updated book includes reference to a number of new techniques as well as refining existing guidance on data modelling and database structures. Keith clearly explains both the importance of planning and analysis of databases and repositories and an explanation of key techniques to achieve this. A ‘must buy’ for the bookshelf of any data management practitioner. -- Julian Schwarzenbach, Chair of the BCS Data Management Specialist Group\u003cbr\u003eThis book provides a comprehensive and descriptive view of data management within a database setting. This is a must read for anyone involved in the development of systems to manage data. This book is as useful as it is interesting. It covers everything you need to know about getting the most out of your data management processes and architecture. -- Ian Rush, Data \u0026amp; Process Advantage Ltd\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePart 1: Preliminaries Chapter 1 Data and the enterprise Chapter 2 Databases and their development Chapter 3 What is data management?  Part 2: Data Administration Chapter 4 Corporate data modelling Chapter 5 Data definition and naming Chapter 6 Metadata Chapter 7 Data quality Chapter 8 Data accessibility Chapter 9 Master data management  Part 3: Database and Repository Administration Chapter 10 Database administration Chapter 11 Repository administration  Part 4: The Data Management Environment Chapter 12 The use of packaged application software Chapter 13 Distributed data and databases Chapter 14 Business intelligence Chapter 15 Object orientation Chapter 16 Multimedia Chapter 17 Integrating data and web technology Chapter 18 Linked data Chapter 19 Concept systems Chapter 20 Big data and artificial intelligence  Appendices Appendix A Comparison of data modelling notations Appendix B Generic data models Appendix C HTML and XML Appendix D Techniques and skills for data management Appendix E Data strategy Appendix F International standards for data management Appendix G The BCS Data Management Essentials syllabus","brand":"BCS Learning \u0026 Development Limited","offers":[{"title":"Default Title","offer_id":48740983538007,"sku":"9781780175911","price":33.24,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781780175911.jpg?v=1720056212"},{"product_id":"forensic-computing-9781846283970","title":"Forensic Computing","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eIn the second edition of this very successful book, Tony Sammes and Brian Jenkinson show how the contents of computer systems can be recovered, even when hidden or subverted by criminals. Equally important, they demonstrate how to insure that computer evidence is admissible in court. Updated to meet ACPO 2003 guidelines, Forensic Computing: A Practitioner's Guide offers: methods for recovering evidence information from computer systems; principles of password protection and data encryption; evaluation procedures used in circumventing a system’s internal security safeguards, and full search and seizure protocols for experts and police officers.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eFrom the reviews of the second edition:\u003c\/p\u003e \u003cp\u003e\"This book was the product of an ‘arms race’. … It is now listed as the standard text around which all the Forensic Computing courses at Cranfield and some other universities are based. … It is filled with good practical advice and is especially good on interpreting partition tables. … All in all this is a useful … guide to the discipline. … Truly the forensic computing expert is living in interesting times.\" (Alikelman, June, 2009)\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eForensic Computing\u003c\/p\u003e \u003cp\u003eUnderstanding Information\u003c\/p\u003e \u003cp\u003eIT Systems Concepts\u003c\/p\u003e \u003cp\u003ePC Hardware and Inside The Box\u003c\/p\u003e \u003cp\u003eDisk Geometry\u003c\/p\u003e \u003cp\u003eThe New Technology File System\u003c\/p\u003e \u003cp\u003eThe Treatment of PCs\u003c\/p\u003e \u003cp\u003eThe Treatment of Electronic Organisers\u003c\/p\u003e \u003cp\u003eLooking Ahead (Just a little bit more)\u003c\/p\u003e \u003cp\u003eAppendices: Common Character Codes; Some Common File Format Signatures; A Typical Set of POST codes; Typical BIOS Beep Codes and Error Messages; Disk Partition Table Types; Ezxtended Partitions; Registers and Order Code for the INtel 8086; NFTS Boot Sector and BIOS Parameter Block; MFT Header and Attribute Maps; The Relationship Between CHS and LBA Addressing; Alternate Data Streams - a Brief Explanation\u003c\/p\u003e","brand":"Springer London Ltd","offers":[{"title":"Default Title","offer_id":48742167576919,"sku":"9781846283970","price":999.99,"currency_code":"GBP","in_stock":false}]},{"product_id":"murachs-mysql-3rd-edition-9781943872368","title":"Murach's MySQL, 3rd Edition","description":"","brand":"Mike Murach \u0026 Associates Inc.","offers":[{"title":"Default Title","offer_id":48742870417751,"sku":"9781943872367","price":50.14,"currency_code":"GBP","in_stock":true}]},{"product_id":"knowledge-graphs-and-big-data-processing-9783030531980","title":"Knowledge Graphs and Big Data Processing","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis open access book is part of the LAMBDA Project (Learning, Applying, Multiplying Big Data Analytics), funded by the European Union, GA No. 809965. Data Analytics involves applying algorithmic processes to derive insights. Nowadays it is used in many industries to allow organizations and companies to make better decisions as well as to verify or disprove existing theories or models. The term data analytics is often used interchangeably with intelligence, statistics, reasoning, data mining, knowledge discovery, and others. \u003c\/p\u003e  \u003cp\u003eThe goal of this book is to introduce some of the definitions, methods, tools, frameworks, and solutions for big data processing, starting from the process of information extraction and knowledge representation, via knowledge processing and analytics to visualization, sense-making, and practical applications. Each chapter in this book addresses some pertinent aspect of the data processing chain, with a specific focus on understanding Enterprise Knowledge Graphs, Semantic Big Data Architectures, and Smart Data Analytics solutions.\u003c\/p\u003e  \u003cp\u003eThis book is addressed to graduate students from technical disciplines, to professional audiences following continuous education short courses, and to researchers from diverse areas following self-study courses. Basic skills in computer science, mathematics, and statistics are required.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eFoundations.- \u003c\/b\u003eChapter 1. Ecosystem of Big Data.- Chapter 2. Knowledge Graphs: The Layered Perspective.- Chapter 3. Big Data Outlook, Tools, and Architectures.- \u003cb\u003eArchitecture.-\u003c\/b\u003e Chapter 4. Creation of Knowledge Graphs.- Chapter 5. Federated Query Processing.- Chapter 6. Reasoning in Knowledge Graphs: An Embeddings Spotlight.- \u003cb\u003eMethods and Solutions.-\u003c\/b\u003e Chapter 7. Scalable Knowledge Graph Processing using SANSA.- Chapter 8. Context-Based Entity Matching for Big Data.- \u003cb\u003eApplications.-\u003c\/b\u003e Chapter 9. Survey on Big Data Applications.- Chapter 10. Case Study from the Energy Domain.","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":48743039205719,"sku":"9783030531980","price":34.99,"currency_code":"GBP","in_stock":true}]},{"product_id":"an-introduction-to-design-science-9783030781347","title":"An Introduction to Design Science","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis book is an introductory text on design science, intended to support both graduate students and researchers in structuring, undertaking and presenting design science work. It builds on established design science methods as well as recent work on presenting design science studies and ethical principles for design science, and also offers novel instruments for visualizing the results, both in the form of process diagrams and through a canvas format. While the book does not presume any prior knowledge of design science, it provides readers with a thorough understanding of the subject and enables them to delve into much deeper detail, thanks to extensive sections on further reading.\u003c\/p\u003e  \u003cp\u003eDesign science in information systems and technology aims to create novel artifacts in the form of models, methods, and systems that support people in developing, using and maintaining IT solutions. This work focuses on design science as applied to information systems and technology, but it also includes examples from, and perspectives of, other fields of human practice.\u003c\/p\u003e  \u003cp\u003eChapter 1 provides an overview of design science and outlines its ties with empirical research. Chapter 2 discusses the various types and forms of knowledge that can be used and produced by design science research, while Chapter 3 presents a brief overview of common empirical research strategies and methods. Chapter 4 introduces a methodological framework for supporting researchers in doing design science research as well as in presenting their results. This framework includes five core activities, which are described in detail in Chapters 5 to 9. Chapter 10 discusses how to communicate design science results, while Chapter 11 compares the proposed methodological framework with methods for systems development and shows how they can be combined. Chapter 12 discusses how design science relates to research paradigms, in particular to positivism and interpretivism, and Chapter 13 discusses ethical issues and principles for design science research. The new Chapter 14 showcases a study on digital health consultations and illustrates the whole process in one comprehensive example. Also added to this 2\u003csup\u003end\u003c\/sup\u003e edition are a number of sections on practical guidelines for carrying out basic design science tasks, a discussion on design thinking and its relationship to design science, and the description of artefact classifications. Eventually, both the references in each chapter and the companion web site were updated to reflect recent findings.\u003c\/p\u003e\u003cbr\u003e\u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1 Introduction.- 2 Knowledge Types and Forms.- 3 Research Strategies and Methods.- 4 A Method Framework for Design Science Research.- 5 Explicate Problem.- 6 Define Requirements.- 7 Design and Develop Artefact.- 8 Demonstrate Artefact.- 9 Evaluate Artefact.- 10 Communicate Artefact Knowledge.- 11 Systems Development and the Method Framework for Design Science Research.- 12 Research Paradigms.- 13 Ethics and Design Science. 14 Digital Consultations — a Case Study.","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":48743049560407,"sku":"9783030781347","price":47.69,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783030781347.jpg?v=1720063888"},{"product_id":"information-systems-reengineering-integration-and-normalization-heterogeneous-database-connectivity-9783030795832","title":"Information Systems Reengineering, Integration","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eDatabase technology is an important subject in Computer Science. Every large company and nation needs a database to store information. The technology has evolved from file systems in the 60’s, to Hierarchical and Network databases in the 70’s, to relational databases in the 80’s, object-oriented databases in the 90’s, and to XML documents and NoSQL today. As a result, there is a need to reengineer and update old databases into new databases. This book presents solutions for this task.\u003c\/p\u003eIn this fourth edition, Chapter 9 - Heterogeneous Database Connectivity (HDBC) offers a database gateway platform for companies to communicate with each other not only with their data, but also via their database. The ability of sharing a database can contribute to the applications of Big Data and surveys for decision support systems. The HDBC gateway solution collects input from the database, transfers the data into its middleware storage, converts it into a common data format such as XML documents, and then distributes them to the users. HDBC transforms the common data into the target database to meet the user’s requirements, acting like a voltage transformer hub. The voltage transformer converts the voltage to a voltage required by the users. Similarly, HDBC transforms the database to the target database required by the users.\u003cbr\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eThis book covers reengineering for data conversion, integration for combining databases and merging databases and expert system rules, normalization for eliminating duplicate data from the database, and above all, HDBC connects all legacy databases to one target database for the users.\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eThe authors provide a forum for readers to ask questions and the answers are given by the authors and the other readers on the Internet.\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e \u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003ePreface.- Information Systems Reengineering, Integration and Normalization.- Database and Expert System Technology.- Schema Transition.- Data Conversion.- Database Program Translation.- Schema Integration.- Database and Expert-Systems Integration.- Data Normalization.- Heterogeneous Database Connectivity.- Conclusion.\u003c\/p\u003e","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":48743050248535,"sku":"9783030795832","price":48.99,"currency_code":"GBP","in_stock":true}]},{"product_id":"data-driven-engineering-design-9783030881801","title":"Data-Driven Engineering Design","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis book addresses the emerging paradigm of data-driven engineering design. In the big-data era, data is becoming a strategic asset for global manufacturers. This book shows how the power of data can be leveraged to drive the engineering design process, in particular, the early-stage design.\u003c\/p\u003e\u003cp\u003eBased on novel combinations of standing design methodology and the emerging data science, the book presents a collection of theoretically sound and practically viable design frameworks, which are intended to address a variety of critical design activities including conceptual design, complexity management, smart customization, smart product design, product service integration, and so forth. In addition, it includes a number of detailed case studies to showcase the application of data-driven engineering design. The book concludes with a set of promising research questions that warrant further investigation.\u003c\/p\u003e\u003cp\u003eGiven its scope, the book will appeal to a broad readership, including postgraduate students, researchers, lecturers, and practitioners in the field of engineering design.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eData-driven Engineering Design.- User-Generated Content Analysis for Customer Needs Elicitation.- Data-driven Conceptual Design.- Management of Constraints, Complexities, and Contradictions in the Data Era.- Blockchain-based Data-driven Smart Customisation.- Data-driven Design of Smart Product.- Data-driven Smart Product Service System.- Digital Twin for Data-driven Engineering Design.- Enabling Technologies of Data-driven Engineering Design.","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":48743056179543,"sku":"9783030881801","price":49.99,"currency_code":"GBP","in_stock":true}]},{"product_id":"sql-server-database-programming-with-java-concepts-designs-and-implementations-9783030926878","title":"SQL Server Database Programming with Java:","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis textbook covers both fundamental and advanced Java database programming techniques for beginning and experienced students as well as programmers (courses related to database programming in Java with Apache NetBeans IDE 12 environment). A sample SQL Server 2019 Express database, CSE_DEPT, is created and implemented in all example projects throughout this textbook. \u003cbr\u003eOver 40 real sample database programming projects are covered in this textbook with detailed illustrations and explanations to help students understand the key techniques and programming technologies. Chapters include homework and selected solutions to strengthen and improve students’ learning and understanding for topics they study in the classroom. Both Java desktop and Web applications with SQL Server database programming techniques are discussed and analyzed. Some updated Java techniques, such as Java Server Pages (JSP), Java Server Faces (JSF), Java Web Service (JWS), JavaServer Pages Standard Tag Library (JSTL), JavaBeans and Java API for XML Web Services (JAX-WS) are also discussed and implemented in the real projects developed in this textbook.\u003cbr\u003e\u003cbr\u003eThis textbook targets mainly advanced-level students in computer science, but it also targets entry-level students in computer science and information system. Programmers, software engineers and researchers will also find this textbook useful as a reference for their projects.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003e\u003c\/b\u003e\u003c\/p\u003e","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":48743057916247,"sku":"9783030926878","price":42.74,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783030926878.jpg?v=1723812627"},{"product_id":"neuromorphic-computing-principles-and-organization-9783030925277","title":"Neuromorphic Computing Principles and","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis book focuses on neuromorphic computing principles and organization and how to build fault-tolerant scalable hardware for large and medium scale spiking neural networks with learning capabilities. In addition, the book describes in a comprehensive way the organization and how to design a spike-based neuromorphic system to perform network of spiking neurons communication, computing, and adaptive learning for emerging AI applications. The book begins with an overview of neuromorphic computing systems and explores the fundamental concepts of artificial neural networks. Next, we discuss artificial neurons and how they have evolved in their representation of biological neuronal dynamics. Afterward, we discuss implementing these neural networks in neuron models, storage technologies, inter-neuron communication networks, learning, and various design approaches. Then, comes the fundamental design principle to build an efficient neuromorphic system in hardware. The challenges that need to be solved toward building a spiking neural network architecture with many synapses are discussed. Learning in neuromorphic computing systems and the major emerging memory technologies that promise neuromorphic computing are then given.\u003cp\u003e\u003c\/p\u003eA particular chapter of this book is dedicated to the circuits and architectures used for communication in neuromorphic systems. In particular, the Network-on-Chip fabric is introduced for receiving and transmitting spikes following the Address Event Representation (AER) protocol and the memory accessing method. In addition, the interconnect design principle is covered to help understand the overall concept of on-chip and off-chip communication. Advanced on-chip interconnect technologies, including si-photonic three-dimensional interconnects and fault-tolerant routing algorithms, are also given. The book also covers the main threats of reliability and discusses several recovery methods for multicore neuromorphic systems. This is important for reliable processing in several embedded neuromorphic applications. A reconfigurable design approach that supports multiple target applications via dynamic reconfigurability, network topology independence, and network expandability is also described in the subsequent chapters. The book ends with a case study about a real hardware-software design of a reliable three-dimensional digital neuromorphic processor geared explicitly toward the 3D-ICs biological brain’s three-dimensional structure. The platform enables high integration density and slight spike delay of spiking networks and features a scalable design. We present methods for fault detection and recovery in a neuromorphic system as well.\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003eNeuromorphic Computing Principles and Organization is an excellent resource for researchers, scientists, graduate students, and hardware-software engineers dealing with the ever-increasing demands on fault-tolerance, scalability, and low power consumption. It is also an excellent resource for teaching advanced undergraduate and graduate students about the fundamentals concepts, organization, and actual hardware-software design of reliable neuromorphic systems with learning and fault-tolerance capabilities.\u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1 Introduction to Neuromorphic Computing Systems.- 2 Neuromorphic System Design Fundamentals.- 3 Learning in Neuromorphic Systems.- 4 Emerging Memory Devices for Neuromorphic Systems.- 5 Communication Networks for Neuromorphic Systems.- 6 Fault-Tolerant Neuromorphic System Design.- 7 Reconfigurable Neuromorphic Computing System.- 8 Case Study: Real Hardware-Software Design of 3D-NoC-based Neuromorphic System.- 9 Survey of Neuromorphic Systems.\u003cp\u003e\u003c\/p\u003e","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":48743058669911,"sku":"9783030925277","price":49.49,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783030925277.jpg?v=1720063929"},{"product_id":"sql-server-database-programming-with-java-concepts-designs-and-implementations-9783030926861","title":"SQL Server Database Programming with Java:","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis textbook covers both fundamental and advanced Java database programming techniques for beginning and experienced students as well as programmers (courses related to database programming in Java with Apache NetBeans IDE 12 environment). A sample SQL Server 2019 Express database, CSE_DEPT, is created and implemented in all example projects throughout this textbook. \u003cbr\u003eOver 40 real sample database programming projects are covered in this textbook with detailed illustrations and explanations to help students understand the key techniques and programming technologies. Chapters include homework and selected solutions to strengthen and improve students’ learning and understanding for topics they study in the classroom. Both Java desktop and Web applications with SQL Server database programming techniques are discussed and analyzed. Some updated Java techniques, such as Java Server Pages (JSP), Java Server Faces (JSF), Java Web Service (JWS), JavaServer Pages Standard Tag Library (JSTL), JavaBeans and Java API for XML Web Services (JAX-WS) are also discussed and implemented in the real projects developed in this textbook.\u003cbr\u003e\u003cbr\u003eThis textbook targets mainly advanced-level students in computer science, but it also targets entry-level students in computer science and information system. Programmers, software engineers and researchers will also find this textbook useful as a reference for their projects.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003e\u003c\/b\u003e\u003c\/p\u003e","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":48743058866519,"sku":"9783030926861","price":42.74,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783030926861.jpg?v=1720063929"},{"product_id":"designing-data-spaces-the-ecosystem-approach-to-competitive-advantage-9783030939748","title":"Designing Data Spaces: The Ecosystem Approach to","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis open access book provides a comprehensive view on data ecosystems and platform economics from methodical and technological foundations up to reports from practical implementations and applications in various industries.\u003c\/p\u003e  \u003cp\u003eTo this end, the book is structured in four parts: Part I “Foundations and Contexts” provides a general overview about building, running, and governing data spaces and an introduction to the IDS and GAIA-X projects. Part II “Data Space Technologies” subsequently details various implementation aspects of IDS and GAIA-X, including eg data usage control, the usage of blockchain technologies, or semantic data integration and interoperability. Next, Part III describes various “Use Cases and Data Ecosystems” from various application areas such as agriculture, healthcare, industry, energy, and mobility. Part IV eventually offers an overview of several “Solutions and Applications”, eg including products and experiences from companies like Google, SAP, Huawei, T-Systems, Innopay and many more.\u003c\/p\u003e  \u003cp\u003eOverall, the book provides professionals in industry with an encompassing overview of the technological and economic aspects of data spaces, based on the International Data Spaces and Gaia-X initiatives. It presents implementations and business cases and gives an outlook to future developments. In doing so, it aims at proliferating the vision of a social data market economy based on data spaces which embrace trust and data sovereignty.\u003cbr\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePart I: Foundations and Context.- 1. The Evolution of Data Spaces.- 2. How to Build, Run, and Govern Data Spaces.- 3. International Data Spaces in a Nutshell.- 4. Role of Gaia-X in the European Data Space Ecosystem.- 5. Legal Aspects of IDS: Data Sovereignty—What Does It Imply?.- 6. Tokenomics: Decentralized Incentivization in the Context of Data Spaces.- Part II: Data Space Technologies.- 7. The IDS Information Model: A Semantic Vocabulary for Sovereign Data Exchange.- 8. Data Usage Control.- 9. Building Trust in Data Spaces.- 10. Blockchain Technology and International Data Spaces.- 11. Federated Data Integration in Data Spaces.- 12. Semantic Integration and Interoperability.- 13. Data Ecosystems: A New Dimension of Value Creation Using AI and Machine Learning.- 14. IDS as a Foundation for Open Data Ecosystems.- 15. Defining Platform Research Infrastructure as a Service (PRIaaS) for Future Scientific Data Infrastructure.- Part III: Use Cases and Data Ecosystems.- 16. Silicon Economy: Logistics as the Natural Data Ecosystem.- 17. Agricultural Data Space.- 18. Medical Data Spaces in Healthcare Data Ecosystems.- 19. Industrial Data Spaces.- 20. Energy Data Space.- 21. Mobility Data Space.- Part IV: Solutions and Applications.- 22. Data Sharing Spaces: The BDVA Perspective.- 23. Data Platform Solutions.- 24. FIWARE for Data Spaces.- 25. Sovereign Cloud Technologies for Scalable Data Spaces.- 26. Data Space Based on Mass Customization Model.- 27. Huawei and International Data Spaces.- International Collaboration Between Data Spaces and Carrier\u003cp\u003e\u003c\/p\u003e  Networks.- 29. From Linear Supply Chains to Open Supply Ecosystems.- 30. Data Spaces: First Applications in Mobility and Industry.- 31. Competition, Security, and Transparency: Data in Connected Vehicles.- Data Space Functionality.- The Energy Data Space: The Path to a European Approach for Energy.","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":48743059980631,"sku":"9783030939748","price":44.99,"currency_code":"GBP","in_stock":true}]},{"product_id":"thinking-data-science-a-data-science-practitioner-s-guide-9783031023620","title":"Thinking Data Science: A Data Science","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis definitive guide to Machine Learning projects answers the problems an aspiring or experienced data scientist frequently has: Confused on what technology to use for your ML development? Should I use GOFAI, ANN\/DNN or Transfer Learning? Can I rely on AutoML for model development? What if the client provides me Gig and Terabytes of data for developing analytic models? How do I handle high-frequency dynamic datasets? This book provides the practitioner with a consolidation of the entire data science process in a single “Cheat Sheet”.\u003c\/p\u003eThe challenge for a data scientist is to extract meaningful information from huge datasets that will help to create better strategies for businesses. Many Machine Learning algorithms and Neural Networks are designed to do analytics on such datasets. For a data scientist, it is a daunting decision as to which algorithm to use for a given dataset. Although there is no single answer to this question, a systematic approach to problem solving is necessary. This book describes the various ML algorithms conceptually and defines\/discusses a process in the selection of ML\/DL models. The consolidation of available algorithms and techniques for designing efficient ML models is the key aspect of this book. \u003ci\u003eThinking Data Science\u003c\/i\u003e will help practising data scientists, academicians, researchers, and students who want to build ML models using the appropriate algorithms and architectures, whether the data be small or big.\u003cp\u003e\u003c\/p\u003e\u003cp\u003e  \u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1. Data Science Process2. Dimensionality Reduction - Creating Manageable Training Datasets3. Classical Algorithms - Overview4. Regression Analysis5. Decision Tree6. Ensemble - Bagging and Boosting7. K-Nearest Neighbors8. Naive Bayes9. Support Vector Machines: A supervised learning algorithm for Classification and Regression10. Clustering Overview11. Centroid-based Clustering12. Connectivity-based Clustering13. Gaussian Mixture Model14. Density-based15. BIRCH16. CLARANS17. Affinity Propagation Clustering18. STING19. CLIQUE20. Artificial Neural Networks21. ANN-based Applications22. Automated Tools23. Data Scientist’s Ultimate Workflow","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":48743065157975,"sku":"9783031023620","price":41.24,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783031023620.jpg?v=1720063957"},{"product_id":"the-data-driven-organization-using-data-for-the-success-of-your-company-9783031206061","title":"The Data-driven Organization: Using Data for the","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eData has become an indispensable success factor for every company. However, the road towards a data-driven organization is paved with numerous challenges. This book presents a process model for the path to a data-driven company and provides recommendations for the design of all relevant fields of action: Which structures need to be created? Which systems and processes have proven beneficial? How can the quality of the data be ensured and what requirements exist for a data-driven organization in the areas of governance and communication? And last but not least: How can employees be brought along on the journey and what implications does the data-driven organization have for our corporate culture? The book presents an orientation and action framework for the strategic and operational design of a data-driven organization and is valuable for managers who are involved in data management in companies and organizations.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eForeword\u003cp\u003e1 Background and drivers of the data-driven organization\u003c\/p\u003e  \u003cp\u003e2 Characteristics of the data-driven organization\u003c\/p\u003e  \u003cp\u003e3 Challenges and barriers of the data-driven organization\u003c\/p\u003e  \u003cp\u003e4 Process Mode for Data Management\u003c\/p\u003e  \u003cp\u003e5 Process model for implementing the data-driven organization\u003c\/p\u003e  \u003cp\u003e6 Closing words\u003c\/p\u003e","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":48743075610967,"sku":"9783031206061","price":31.34,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783031206061.jpg?v=1720064003"},{"product_id":"guide-to-teaching-data-science-an-interdisciplinary-approach-9783031247576","title":"Guide to Teaching Data Science: An","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eData science is a new field that touches on almost every domain of our lives, and thus it is taught in a variety of environments. Accordingly, the book is suitable for teachers and lecturers in all educational frameworks: K-12, academia and industry.\u003c\/p\u003e\u003cp\u003eThis book aims at closing a significant gap in the literature on the \u003ci\u003epedagogy \u003c\/i\u003eof data science. While there are many articles and white papers dealing with the curriculum of data science (i.e., what to teach?), the pedagogical aspect of the field (i.e., how to teach?) is almost neglected. At the same time, the importance of the pedagogical aspects of data science increases as more and more programs are currently open to a variety of people.\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eThis book provides a variety of pedagogical discussions and specific teaching methods and frameworks, as well as includes exercises, and guidelines related to many data science concepts (e.g., data thinking and the data science workflow), main machine learning algorithms and concepts (e.g., KNN, SVM, Neural Networks, performance metrics, confusion matrix, and biases) and data science professional topics (e.g., ethics, skills and research approach).\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eProfessor \u003cb\u003eOrit Hazzan\u003c\/b\u003e is a faculty member at the Technion’s Department of Education in Science and Technology since October 2000. Her research focuses on computer science, software engineering and data science education. Within this framework, she studies the cognitive and social processes on the individual, the team and the organization levels, in all kinds of organizations.\u003c\/p\u003e\u003cp\u003eDr. \u003cb\u003eKoby Mike\u003c\/b\u003e is a Ph.D. graduate from the Technion's Department of Education in Science and Technology under the supervision of Professor Orit Hazzan. He continued his post-doc research on data science education at the Bar-Ilan University, and obtained a B.Sc. and an M.Sc. in Electrical Engineering from Tel Aviv University.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePart A - Overview of Data Science and Data Science Education1. Introductiona. How to use this bookb. Chapter overviews2. What is data science3. Introduction to data science educationa. Curriculum initiativesb. Data science education research4. Data science thinkinga. Computational thinkingb. Statistical thinkingc. Data thinkingd. Data literacyPart B - Challenges of Data Science Education5. The pedagogical challenge of data science education6. Data science education and the variety of learnersa. Data science as 21st century skillsb. Prerequisite knowledge for data sciencec. Data science for K-12d. Data science for undergraduatese. Data science for researchers: graduate studentsf. Data science for researchers: senior researchersg. Data science for industry7. The interdisciplinarity challengea. Multidisciplinarity, interdisciplinarity and transdisciplinarityb. Integration of the data domainc. Interdisciplinary pedagogyd. Interdisciplinary PCK (Pedagogical Content Knowledge)e. Interdisciplinary PBL (Project Based Learning)8. Data science skillsa. Professional skillsb. Soft skillsc. Research skillsPart C - Data science Teaching frameworks9. Teacher Preparation - the Method for Teaching Data Science course10. Data Science for Social Sciencea. Interdisciplinary CS1b. Machine learning for social science and digital humanities11. Conclusion","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":48743077216599,"sku":"9783031247576","price":52.24,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783031247576.jpg?v=1720064009"},{"product_id":"cinderellas-stick-a-fairy-tale-for-digital-preservation-9783319984872","title":"Cinderella's Stick: A Fairy Tale for Digital","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis book explains the main problems related to digital preservation using examples based on a modern version of the well-known Cinderella fairy tale. Digital preservation is the endeavor to protect digital material against loss, corruption, hardware\/software technology changes, and changes in the knowledge of the community.\u003cbr\u003eΤhe structure of the book is modular, with each chapter consisting of two parts: the episode and the technical background. The episodes narrate the story in chronological order, exactly as in a fairy tale. In addition to the story itself, each episode is related to one or more digital preservation problems, which are discussed in the technical background section of the chapter. To reveal a more general and abstract formulation of these problems, the notion of pattern is used. Each pattern has a name, a summary of the problem, a narrative describing an attempt to solve the problem, an explanation of what could have been done to avoid or alleviate this problem, some lessons learned, and lastly, links to related patterns discussed in other chapters.\u003cbr\u003eThe book is intended for anyone wanting to understand the problems related to digital preservation, even if they lack the technical background. It explains the technical details at an introductory level, provides references to the main approaches (or solutions) currently available for tackling related problems, and is rounded out by questions and exercises appropriate for computer engineers and scientists. In addition, the book's website, maintained by the authors, presents the contents of Cinderella's “real USB stick,” and includes links to various tools and updates.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e“‘Cinderella’s Stick’ is an excellent book for all readers in research libraries. It provides the right concepts in a very smart and innovative way, and it underlines that the amount of digital information that we alone produce is immense and the challenges of fragility are here to stay.” (Giannis Tsakonas, Liber Quarterly, Vol. 29(1), 2019)\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e1 A Few Words About Digital Preservation And Book Overview.- 2 The Fairy Tale Of Cinderella.- 3 Daphne (A Modern Cinderella).- 4 Reading the Contents of the USB Stick.- 5 First Contact with the Contents of the USB Stick.- 6 The File Poem.html: On Reading Characters.- 7 The File MyPlace.png: On Getting the Provenance of a Digital Object.- 8 The File todo.csv – On Understanding Data Values.- 9 The File destroyAll.exe: On Executing Proprietary Software.- 10 The File Mymusic.class: On Decompiling Software.- 11 The File yyy.java: On Compiling And Running Software.- 12 The File myFriendsBook.war: On Running Web Applications.- 13 The File roulette.BAS: On Running Obsolete Software.- 14 The Folder myExperiment: On Verifying and Reproducing Data.- 15 The File MyContacts.con: On Reading Unknown Digital Resources.- 16 The File SecretMeeting.Txt: On Authenticity Checking.- 17 The Personal Archive Of Robert: On Preservation Planning.- 18 The Meta-Pattern: Toward a Common Umbrella.- 19 How Robert Eventually Found Daphne.- 20 Daphne’s Dream.- 21 Epilogue.\u003c\/p\u003e","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":48743112147287,"sku":"9783319984872","price":999.99,"currency_code":"GBP","in_stock":false}]},{"product_id":"data-matching-concepts-and-techniques-for-record-linkage-entity-resolution-and-duplicate-detection-9783642430015","title":"Data Matching: Concepts and Techniques for Record","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eData matching (also known as record or data linkage, entity resolution, object identification, or field matching) is the task of identifying, matching and merging records that correspond to the same entities from several databases or even within one database. Based on research in various domains including applied statistics, health informatics, data mining, machine learning, artificial intelligence, database management, and digital libraries, significant advances have been achieved over the last decade in all aspects of the data matching process, especially on how to improve the accuracy of data matching, and its scalability to large databases.\u003c\/p\u003e\u003cp\u003ePeter Christen’s book is divided into three parts: Part I, “Overview”, introduces the subject by presenting several sample applications and their special challenges, as well as a general overview of a generic data matching process. Part II, “Steps of the Data Matching Process”, then details its main steps like pre-processing, indexing, field and record comparison, classification, and quality evaluation. Lastly, part III, “Further Topics”, deals with specific aspects like privacy, real-time matching, or matching unstructured data. Finally, it briefly describes the main features of many research and open source systems available today.\u003c\/p\u003eBy providing the reader with a broad range of data matching concepts and techniques and touching on all aspects of the data matching process, this book helps researchers as well as students specializing in data quality or data matching aspects to familiarize themselves with recent research advances and to identify open research challenges in the area of data matching. To this end, each chapter of the book includes a final section that provides pointers to further background and research material. Practitioners will better understand the current state of the art in data matching as well as the internal workings and limitations of current systems. Especially, they will learn that it is often not feasible to simply implement an existing off-the-shelf data matching system without substantial adaption and customization. Such practical considerations are discussed for each of the major steps in the data matching process.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\u003ci\u003e\"The book is very well organized and exceptionally well written. Because of the depth, amount, and quality of the material that is covered, I would expect this book to be one of the standard references in future years.\"\u003c\/i\u003e William E. 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It first presents the overview of data deduplication including its theoretical basis, basic workflow, application scenarios and its key technologies, and then the book focuses on each key technology of the deduplication to provide an insight into the evolution of the technology over the years including chunking algorithms, indexing schemes, fragmentation reduced schemes, rewriting algorithm and security solution. In particular, the state-of-the-art solutions and the newly proposed solutions are both elaborated. At the end of the book, the author discusses the fundamental trade-offs in each of deduplication design choices and propose an open-source deduplication prototype. The book with its fundamental theories and complete survey can guide the beginners, students and practitioners working on data deduplication in storage system. 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She holds a master's degree in computer science from New York University, where she specialized in machine learning.  \u003cbr\u003e \u003c\/div\u003e \u003cdiv\u003e  \u003cbr\u003e \u003c\/div\u003e \u003cdiv\u003e  \u003cb\u003eWah Loon Keng\u003c\/b\u003e is an AI engineer at Machine Zone, where he applies deep reinforcement learning to industrial problems. He has a background in both theoretical physics and computer science. \u003c\/div\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e“This book provides an accessible introduction to deep reinforcement learning covering the mathematical concepts behind popular algorithms as well as their practical implementation. I think the book will be a valuable resource for anyone looking to apply deep reinforcement learning in practice.” \u003cbr\u003e \u003ci\u003e–Volodymyr Mnih, lead developer of DQN\u003c\/i\u003e \u003cbr\u003e \u003cbr\u003e“An excellent book to quickly develop expertise in the theory, language, and practical implementation of deep reinforcement learning algorithms. A limpid exposition which uses familiar notation; all the most recent techniques explained with concise, readable code, and not a page wasted in irrelevant detours: it is the perfect way to develop a solid foundation on the topic.” \u003cbr\u003e \u003ci\u003e–Vincent Vanhoucke, principal scientist, Google\u003c\/i\u003e \u003cbr\u003e \u003cbr\u003e“As someone who spends their days trying to make deep reinforcement learning methods more useful for the general public, I can say that Laura and Keng’s book is a welcome addition to the literature. It provides both a readable introduction to the fundamental concepts in reinforcement learning as well as intuitive explanations and code for many of the major algorithms in the field. I imagine this will become an invaluable resource for individuals interested in learning about deep reinforcement learning for years to come.” \u003cbr\u003e \u003ci\u003e–Arthur Juliani, senior machine learning engineer, Unity Technologies\u003c\/i\u003e \u003cbr\u003e \u003cbr\u003e“Until now, the only way to get to grips with deep reinforcement learning was to slowly accumulate knowledge from dozens of different sources. Finally, we have a book bringing everything together in one place.” \u003cbr\u003e \u003ci\u003e–Matthew Rahtz, ML researcher, ETH Zürich\u003c\/i\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eForeword xix\u003cbr\u003ePreface xxi\u003cbr\u003eAcknowledgments xxv\u003cbr\u003eAbout the Authors xxvii\u003c\/i\u003e \u003cbr\u003e \u003cbr\u003e \u003cb\u003eChapter 1: Introduction to Reinforcement Learning 1\u003c\/b\u003e \u003cbr\u003e1.1 Reinforcement Learning 1 \u003cbr\u003e1.2 Reinforcement Learning as MDP 6 \u003cbr\u003e1.3 Learnable Functions in Reinforcement Learning 9 \u003cbr\u003e1.4 Deep Reinforcement Learning Algorithms 11 \u003cbr\u003e1.5 Deep Learning for Reinforcement Learning 17 \u003cbr\u003e1.6 Reinforcement Learning and Supervised Learning 19 \u003cbr\u003e1.7 Summary 21 \u003cbr\u003e \u003cbr\u003e  \u003cb\u003e  \u003c\/b\u003e\u003cb\u003e  Part I: Policy-Based and Value-Based Algorithms 23  \u003c\/b\u003e    \u003cb\u003e\u003cbr\u003e  \u003c\/b\u003e\u003cb\u003eChapter 2: REINFORCE 25\u003c\/b\u003e \u003cbr\u003e2.1 Policy 26 \u003cbr\u003e2.2 The Objective Function 26 \u003cbr\u003e2.3 The Policy Gradient 27 \u003cbr\u003e2.4 Monte Carlo Sampling 30 \u003cbr\u003e2.5 REINFORCE Algorithm 31 \u003cbr\u003e2.6 Implementing REINFORCE 33 \u003cbr\u003e2.7 Training a REINFORCE Agent 44 \u003cbr\u003e2.8 Experimental Results 47 \u003cbr\u003e2.9 Summary 51 \u003cbr\u003e2.10 Further Reading 51 \u003cbr\u003e2.11 History 51 \u003cbr\u003e \u003cbr\u003e \u003cb\u003eChapter 3: SARSA 53\u003c\/b\u003e \u003cbr\u003e3.1 The Q- and V-Functions 54 \u003cbr\u003e3.2 Temporal Difference Learning 56 \u003cbr\u003e3.3 Action Selection in SARSA 65 \u003cbr\u003e3.4 SARSA Algorithm 67 \u003cbr\u003e3.5 Implementing SARSA 69 \u003cbr\u003e3.6 Training a SARSA Agent 74 \u003cbr\u003e3.7 Experimental Results 76 \u003cbr\u003e3.8 Summary 78 \u003cbr\u003e3.9 Further Reading 79 \u003cbr\u003e3.10 History 79 \u003cbr\u003e \u003cbr\u003e \u003cb\u003eChapter 4: Deep Q-Networks (DQN) 81\u003c\/b\u003e \u003cbr\u003e4.1 Learning the Q-Function in DQN 82 \u003cbr\u003e4.2 Action Selection in DQN 83 \u003cbr\u003e4.3 Experience Replay 88 \u003cbr\u003e4.4 DQN Algorithm 89 \u003cbr\u003e4.5 Implementing DQN 91 \u003cbr\u003e4.6 Training a DQN Agent 96 \u003cbr\u003e4.7 Experimental Results 99 \u003cbr\u003e4.8 Summary 101 \u003cbr\u003e4.9 Further Reading 102 \u003cbr\u003e4.10 History 102 \u003cbr\u003e \u003cb\u003e\u003cbr\u003eChapter 5: Improving DQN 103\u003c\/b\u003e \u003cbr\u003e5.1 Target Networks 104 \u003cbr\u003e5.2 Double DQN 106 \u003cbr\u003e5.3 Prioritized Experience Replay (PER) 109 \u003cbr\u003e5.4 Modified DQN Implementation 112 \u003cbr\u003e5.5 Training a DQN Agent to Play Atari Games 123 \u003cbr\u003e5.6 Experimental Results 128 \u003cbr\u003e5.7 Summary 132 \u003cbr\u003e5.8 Further Reading 132 \u003cbr\u003e \u003cb\u003e\u003cbr\u003ePart II: Combined Methods 133\u003cbr\u003e\u003cbr\u003eChapter 6: Advantage Actor-Critic (A2C) 135\u003c\/b\u003e \u003cbr\u003e6.1 The Actor 136 \u003cbr\u003e6.2 The Critic 136 \u003cbr\u003e6.3 A2C Algorithm 141 \u003cbr\u003e6.4 Implementing A2C 143 \u003cbr\u003e6.5 Network Architecture 148 \u003cbr\u003e6.6 Training an A2C Agent 150 \u003cbr\u003e6.7 Experimental Results 157 \u003cbr\u003e6.8 Summary 161 \u003cbr\u003e6.9 Further Reading 162 \u003cbr\u003e6.10 History 162 \u003cbr\u003e \u003cbr\u003e \u003cb\u003eChapter 7: Proximal Policy Optimization (PPO) 165\u003c\/b\u003e \u003cbr\u003e7.1 Surrogate Objective 165 \u003cbr\u003e7.2 Proximal Policy Optimization (PPO) 174 \u003cbr\u003e7.3 PPO Algorithm 177 \u003cbr\u003e7.4 Implementing PPO 179 \u003cbr\u003e7.5 Training a PPO Agent 182 \u003cbr\u003e7.6 Experimental Results 188 \u003cbr\u003e7.7 Summary 192 \u003cbr\u003e7.8 Further Reading 192 \u003cbr\u003e \u003cbr\u003e \u003cb\u003eChapter 8: Parallelization Methods 195\u003c\/b\u003e \u003cbr\u003e8.1 Synchronous Parallelization 196 \u003cbr\u003e8.2 Asynchronous Parallelization 197 \u003cbr\u003e8.3 Training an A3C Agent 200 \u003cbr\u003e8.4 Summary 203 \u003cbr\u003e8.5 Further Reading 204 \u003cbr\u003e \u003cbr\u003e \u003cb\u003eChapter 9: Algorithm Summary 205\u003cbr\u003e\u003cbr\u003ePart III: Practical Details 207\u003cbr\u003e\u003cbr\u003eChapter 10: Getting Deep RL to Work 209\u003c\/b\u003e \u003cbr\u003e10.1 Software Engineering Practices 209 \u003cbr\u003e10.2 Debugging Tips 218 \u003cbr\u003e10.3 Atari Tricks 228 \u003cbr\u003e10.4 Deep RL Almanac 231 \u003cbr\u003e10.5 Summary 238 \u003cbr\u003e \u003cbr\u003e \u003cb\u003eChapter 11: SLM Lab 239\u003c\/b\u003e \u003cbr\u003e11.1 Algorithms Implemented in SLM Lab 239 \u003cbr\u003e11.2 Spec File 241 \u003cbr\u003e11.3 Running SLM Lab 246 \u003cbr\u003e11.4 Analyzing Experiment Results 247 \u003cbr\u003e11.5 Summary 249 \u003cbr\u003e \u003cbr\u003e \u003cb\u003eChapter 12: Network Architectures 251\u003c\/b\u003e \u003cbr\u003e12.1 Types of Neural Networks 251 \u003cbr\u003e12.2 Guidelines for Choosing a Network Family 256 \u003cbr\u003e12.3 The Net API 262 \u003cbr\u003e12.4 Summary 271 \u003cbr\u003e12.5 Further Reading 271 \u003cbr\u003e \u003cbr\u003e \u003cb\u003eChapter 13: Hardware 273\u003c\/b\u003e \u003cbr\u003e13.1 Computer 273 \u003cbr\u003e13.2 Data Types 278 \u003cbr\u003e13.3 Optimizing Data Types in RL 280 \u003cbr\u003e13.4 Choosing Hardware 285 \u003cbr\u003e13.5 Summary 285 \u003cbr\u003e \u003cb\u003e\u003cbr\u003ePart IV: Environment Design 287\u003cbr\u003e\u003cbr\u003eChapter 14: States 289\u003c\/b\u003e \u003cbr\u003e14.1 Examples of States 289 \u003cbr\u003e14.2 State Completeness 296 \u003cbr\u003e14.3 State Complexity 297 \u003cbr\u003e14.4 State Information Loss 301 \u003cbr\u003e14.5 Preprocessing 306 \u003cbr\u003e14.6 Summary 313 \u003cbr\u003e \u003cbr\u003e \u003cb\u003eChapter 15: Actions 315\u003c\/b\u003e \u003cbr\u003e15.1 Examples of Actions 315 \u003cbr\u003e15.2 Action Completeness 318 \u003cbr\u003e15.3 Action Complexity 319 \u003cbr\u003e15.4 Summary 323 \u003cbr\u003e15.5 Further Reading: Action Design in Everyday Things 324 \u003cbr\u003e \u003cbr\u003e \u003cb\u003eChapter 16: Rewards 327\u003c\/b\u003e \u003cbr\u003e16.1 The Role of Rewards 327 \u003cbr\u003e16.2 Reward Design Guidelines 328 \u003cbr\u003e16.3 Summary 332 \u003cbr\u003e \u003cbr\u003e \u003cb\u003eChapter 17: Transition Function 333\u003c\/b\u003e \u003cbr\u003e17.1 Feasibility Checks 333 \u003cbr\u003e17.2 Reality Check 335 \u003cbr\u003e17.3 Summary 337 \u003cbr\u003e \u003cbr\u003e \u003cb\u003eEpilogue 338\u003cbr\u003e\u003cbr\u003eAppendix A: Deep Reinforcement Learning Timeline 343\u003cbr\u003e\u003cbr\u003eAppendix B: Example Environments 345\u003c\/b\u003e \u003cbr\u003eB.1 Discrete Environments 346 \u003cbr\u003eB.2 Continuous Environments 350 \u003cbr\u003e \u003cbr\u003e \u003ci\u003eReferences 353\u003cbr\u003eIndex 363\u003c\/i\u003e","brand":"Pearson Education (US)","offers":[{"title":"Default Title","offer_id":48864172376407,"sku":"9780135172384","price":34.19,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780135172384.jpg?v=1722270729"},{"product_id":"information-privacy-engineering-and-privacy-by-design-9780135302156","title":"Information Privacy Engineering and Privacy by","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eDr. William Stallings\u003c\/b\u003e has made a unique contribution to understanding the broad sweep of technical developments in computer security, computer networking, and computer architecture. He has authored 18 textbooks and, counting revised editions, a total of 70 books on various aspects of these subjects. His writings have appeared in numerous ACM and IEEE publications, including the \u003ci\u003eProceedings of the IEE\u003c\/i\u003eE and \u003ci\u003eACM Computing Reviews\u003c\/i\u003e. He has 13 times received the award for the best computer science textbook of the year from the Text and Academic Authors Association. \u003cbr\u003e \u003cbr\u003eWith more than 30 years in the field, he has been a technical contributor, a technical manager, and an executive with several high-technology firms. He has designed and implemented both TCP\/IP-based and OSI-based protocol suites on a variety of computers and operating systems, ranging from microcomputers to mainframes. Currently he is an independent consultant whose clients have included computer and \u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cul\u003e\n\u003cli\u003ePart I: Planning for Privacy\u003c\/li\u003e\n\u003cli\u003e1. Information Privacy Concepts\u003c\/li\u003e\n\u003cli\u003e2. Security Governance and Management\u003c\/li\u003e\n\u003cli\u003e3. Risk Assessment \u003c\/li\u003e\n\u003cli\u003ePart II: Privacy Threats\u003c\/li\u003e\n\u003cli\u003e4. Information Storage and Processing\u003c\/li\u003e\n\u003cli\u003e5. Information Collection and Dissemination\u003c\/li\u003e\n\u003cli\u003e6. Intrusion and Interference \u003c\/li\u003e\n\u003cli\u003ePart III: Information Privacy Technology\u003c\/li\u003e\n\u003cli\u003e7. Basic Privacy Controls\u003c\/li\u003e\n\u003cli\u003e8. Privacy Enhancing Technology\u003c\/li\u003e\n\u003cli\u003e9. Data Loss Prevention\u003c\/li\u003e\n\u003cli\u003e10. Online Privacy\u003c\/li\u003e\n\u003cli\u003e11. Detection of Conflicts In Security Policies\u003c\/li\u003e\n\u003cli\u003e12. Privacy Evaluation \u003c\/li\u003e\n\u003cli\u003ePart IV: Information Privacy Regulations\u003c\/li\u003e\n\u003cli\u003e13. GDPR\u003c\/li\u003e\n\u003cli\u003e14. U.S. Privacy Laws and Regulations\u003c\/li\u003e\n\u003c\/ul\u003e","brand":"Pearson Education (US)","offers":[{"title":"Default Title","offer_id":48864172998999,"sku":"9780135302156","price":49.39,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780135302156.jpg?v=1722270733"},{"product_id":"tsql-fundamentals-9780138102104","title":"TSQL Fundamentals","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cstrong\u003eItzik Ben-Gan\u003c\/strong\u003e is a mentor with and co-founder of SolidQ. A Microsoft Data Platform MVP since 1999, Itzik has taught numerous training events around the world focused on T-SQL querying, query tuning, and programming. Itzik is the author of several books about T-SQL. He has written many articles for SQL Server Pro as well as articles and white papers for MSDN and The SolidQ Journal. Itzik's speaking engagements include Tech-Ed, SQL PASS, SQL Server Connections, presentations to various SQL Server user groups, and SolidQ events. \u003cbr\u003e \u003cbr\u003eItzik is a subject-matter expert within SolidQ for its T-SQL related activities. He authored SolidQ's Advanced T-SQL and T-SQL Fundamentals courses and delivers them regularly worldwide. You can learn more about Itzik at \u003ca data-cke-saved-href=\"http:\/\/tsql.solidq.com\/\" href=\"http:\/\/tsql.solidq.com\/\" target=\"_blank\"\u003ehttp:\/\/tsql.solidq.com\/\u003c\/a\u003e.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eCHAPTER 1: Background to T-SQL querying and programming\u003c\/p\u003e \u003cp\u003eCHAPTER 2: Single-table queries\u003c\/p\u003e \u003cp\u003eCHAPTER 3: Joins\u003c\/p\u003e \u003cp\u003eCHAPTER 4: Subqueries\u003c\/p\u003e \u003cp\u003eCHAPTER 5: Table expressions\u003c\/p\u003e \u003cp\u003eCHAPTER 6: Set operators\u003c\/p\u003e \u003cp\u003eCHAPTER 7: T-SQL for data analysis\u003c\/p\u003e \u003cp\u003eCHAPTER 8: Data modification\u003c\/p\u003e \u003cp\u003eCHAPTER 9: Temporal tables\u003c\/p\u003e \u003cp\u003eCHAPTER 10: Transactions and concurrency\u003c\/p\u003e \u003cp\u003eCHAPTER 11: SQL Graph\u003c\/p\u003e \u003cp\u003eCHAPTER 12: Programmable objects\u003c\/p\u003e \u003cp\u003eAppendix: Getting started\u003c\/p\u003e","brand":"Pearson Education (US)","offers":[{"title":"Default Title","offer_id":48864177717591,"sku":"9780138102104","price":32.29,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780138102104.jpg?v=1722270757"},{"product_id":"the-big-rbook-9781119632726","title":"The Big RBook","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003eIntroduces professionals and scientists to statistics and machine learning using the programming language R\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWritten by and for practitioners, this book provides an overall introduction to R, focusing on tools and methods commonly used in data science, and placing emphasis on practice and business use. It covers a wide range of topics in a single volume, including big data, databases, statistical machine learning, data wrangling, data visualization, and the reporting of results. The topics covered are all important for someone with a science\/math background that is looking to quickly learn several practical technologies to enter or transition to the growing field of data science.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eThe Big R-Book for Professionals: From Data Science to Learning Machines and Reporting with R\u003c\/i\u003e includes nine parts, starting with an introduction to the subject and followed by an overview of R and elements of statistics. The third part revolves around data, while the fourth focuse\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eForeword xxv\u003c\/p\u003e \u003cp\u003eAbout the Author xxvii\u003c\/p\u003e \u003cp\u003eAcknowledgements xxix\u003c\/p\u003e \u003cp\u003ePreface xxxi\u003c\/p\u003e \u003cp\u003eAbout the Companion Site xxxv\u003c\/p\u003e \u003cp\u003e\u003cb\u003eI Introduction 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 The Big Picture with Kondratiev and Kardashev 3\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 The Scientific Method and Data 7\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Conventions 11\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eII Starting with R and Elements of Statistics 19\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 The Basics of R 21\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Getting Started with R 23\u003c\/p\u003e \u003cp\u003e4.2 Variables 26\u003c\/p\u003e \u003cp\u003e4.3 Data Types 28\u003c\/p\u003e \u003cp\u003e4.3.1 The Elementary Types 28\u003c\/p\u003e \u003cp\u003e4.3.2 Vectors 29\u003c\/p\u003e \u003cp\u003e4.3.3 Accessing Data from a Vector 29\u003c\/p\u003e \u003cp\u003e4.3.4 Matrices 32\u003c\/p\u003e \u003cp\u003e4.3.5 Arrays 38\u003c\/p\u003e \u003cp\u003e4.3.6 Lists 41\u003c\/p\u003e \u003cp\u003e4.3.7 Factors 45\u003c\/p\u003e \u003cp\u003e4.3.8 Data Frames 49\u003c\/p\u003e \u003cp\u003e4.3.9 Strings or the Character-type 54\u003c\/p\u003e \u003cp\u003e4.4 Operators 57\u003c\/p\u003e \u003cp\u003e4.4.1 Arithmetic Operators 57\u003c\/p\u003e \u003cp\u003e4.4.2 Relational Operators 57\u003c\/p\u003e \u003cp\u003e4.4.3 Logical Operators 58\u003c\/p\u003e \u003cp\u003e4.4.4 Assignment Operators 59\u003c\/p\u003e \u003cp\u003e4.4.5 Other Operators 61\u003c\/p\u003e \u003cp\u003e4.5 Flow Control Statements 63\u003c\/p\u003e \u003cp\u003e4.5.1 Choices 63\u003c\/p\u003e \u003cp\u003e4.5.2 Loops 65\u003c\/p\u003e \u003cp\u003e4.6 Functions 69\u003c\/p\u003e \u003cp\u003e4.6.1 Built-in Functions 69\u003c\/p\u003e \u003cp\u003e4.6.2 Help with Functions 69\u003c\/p\u003e \u003cp\u003e4.6.3 User-defined Functions 70\u003c\/p\u003e \u003cp\u003e4.6.4 Changing Functions 70\u003c\/p\u003e \u003cp\u003e4.6.5 Creating Function with Default Arguments 71\u003c\/p\u003e \u003cp\u003e4.7 Packages 72\u003c\/p\u003e \u003cp\u003e4.7.1 Discovering Packages in R 72\u003c\/p\u003e \u003cp\u003e4.7.2 Managing Packages in R 73\u003c\/p\u003e \u003cp\u003e4.8 Selected Data Interfaces 75\u003c\/p\u003e \u003cp\u003e4.8.1 CSV Files 75\u003c\/p\u003e \u003cp\u003e4.8.2 Excel Files 79\u003c\/p\u003e \u003cp\u003e4.8.3 Databases 79\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Lexical Scoping and Environments 81\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Environments in R 81\u003c\/p\u003e \u003cp\u003e5.2 Lexical Scoping in R 83\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 The Implementation of OO 87\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Base Types 89\u003c\/p\u003e \u003cp\u003e6.2 S3 Objects 91\u003c\/p\u003e \u003cp\u003e6.2.1 Creating S3 Objects 94\u003c\/p\u003e \u003cp\u003e6.2.2 Creating Generic Methods 96\u003c\/p\u003e \u003cp\u003e6.2.3 Method Dispatch 97\u003c\/p\u003e \u003cp\u003e6.2.4 Group Generic Functions 98\u003c\/p\u003e \u003cp\u003e6.3 S4 Objects 100\u003c\/p\u003e \u003cp\u003e6.3.1 Creating S4 Objects 100\u003c\/p\u003e \u003cp\u003e6.3.2 Using S4 Objects 101\u003c\/p\u003e \u003cp\u003e6.3.3 Validation of Input 105\u003c\/p\u003e \u003cp\u003e6.3.4 Constructor functions 107\u003c\/p\u003e \u003cp\u003e6.3.5 The Data slot 108\u003c\/p\u003e \u003cp\u003e6.3.6 Recognising Objects, Generic Functions, and Methods 108\u003c\/p\u003e \u003cp\u003e6.3.7 CreatingS4Generics 110\u003c\/p\u003e \u003cp\u003e6.3.8 Method Dispatch 111\u003c\/p\u003e \u003cp\u003e6.4 The Reference Class, refclass, RC or R5 Model 113\u003c\/p\u003e \u003cp\u003e6.4.1 Creating RC Objects 113\u003c\/p\u003e \u003cp\u003e6.4.2 Important Methods and Attributes 117\u003c\/p\u003e \u003cp\u003e6.5 Conclusions about the OO Implementation 119\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Tidy R with the Tidyverse 121\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 The Philosophy of the Tidyverse 121\u003c\/p\u003e \u003cp\u003e7.2 Packages in the Tidyverse 124\u003c\/p\u003e \u003cp\u003e7.2.1 The Core Tidyverse 124\u003c\/p\u003e \u003cp\u003e7.2.2 The Non-core Tidyverse 125\u003c\/p\u003e \u003cp\u003e7.3 Working with the Tidyverse 127\u003c\/p\u003e \u003cp\u003e7.3.1 Tibbles 127\u003c\/p\u003e \u003cp\u003e7.3.2 Piping with R 132\u003c\/p\u003e \u003cp\u003e7.3.3 Attention Points When Using the Pipe 133\u003c\/p\u003e \u003cp\u003e7.3.4 Advanced Piping 134\u003c\/p\u003e \u003cp\u003e7.3.5 Conclusion 137\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Elements of Descriptive Statistics 139\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Measures of Central Tendency 139\u003c\/p\u003e \u003cp\u003e8.1.1 Mean 139\u003c\/p\u003e \u003cp\u003e8.1.2 The Median 142\u003c\/p\u003e \u003cp\u003e8.1.3 The Mode 143\u003c\/p\u003e \u003cp\u003e8.2 Measures of Variation or Spread 145\u003c\/p\u003e \u003cp\u003e8.3 Measures of Covariation 147\u003c\/p\u003e \u003cp\u003e8.3.1 The Pearson Correlation 147\u003c\/p\u003e \u003cp\u003e8.3.2 The Spearman Correlation 148\u003c\/p\u003e \u003cp\u003e8.3.3 Chi-square Tests 149\u003c\/p\u003e \u003cp\u003e8.4 Distributions 150\u003c\/p\u003e \u003cp\u003e8.4.1 Normal Distribution 150\u003c\/p\u003e \u003cp\u003e8.4.2 Binomial Distribution 153\u003c\/p\u003e \u003cp\u003e8.5 Creating an Overview of Data Characteristics 155\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Visualisation Methods 159\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Scatterplots 161\u003c\/p\u003e \u003cp\u003e9.2 Line Graphs 163\u003c\/p\u003e \u003cp\u003e9.3 Pie Charts 165\u003c\/p\u003e \u003cp\u003e9.4 Bar Charts 167\u003c\/p\u003e \u003cp\u003e9.5 Boxplots 171\u003c\/p\u003e \u003cp\u003e9.6 Violin Plots 173\u003c\/p\u003e \u003cp\u003e9.7 Histograms 176\u003c\/p\u003e \u003cp\u003e9.8 Plotting Functions 179\u003c\/p\u003e \u003cp\u003e9.9 Maps and Contour Plots 180\u003c\/p\u003e \u003cp\u003e9.10 Heat-maps 181\u003c\/p\u003e \u003cp\u003e9.11 Text Mining 184\u003c\/p\u003e \u003cp\u003e9.11.1 Word Clouds 184\u003c\/p\u003e \u003cp\u003e9.11.2 Word Associations 188\u003c\/p\u003e \u003cp\u003e9.12 Colours in R 191\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Time Series Analysis 197\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Time Series in R 197\u003c\/p\u003e \u003cp\u003e10.1.1 The Basics of Time Series in R 197\u003c\/p\u003e \u003cp\u003e10.2 Forecasting 200\u003c\/p\u003e \u003cp\u003e10.2.1 Moving Average 200\u003c\/p\u003e \u003cp\u003e10.2.2 Seasonal Decomposition 206\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Further Reading 211\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIII Data Import 213\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 A Short History of Modern Database Systems 215\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 RDBMS 219\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 SQL 223\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Designing the Database 223\u003c\/p\u003e \u003cp\u003e14.2 Building the Database Structure 226\u003c\/p\u003e \u003cp\u003e14.2.1 Installing a RDBMS 226\u003c\/p\u003e \u003cp\u003e14.2.2 Creating the Database 228\u003c\/p\u003e \u003cp\u003e14.2.3 Creating the Tables and Relations 229\u003c\/p\u003e \u003cp\u003e14.3 Adding Data to the Database 235\u003c\/p\u003e \u003cp\u003e14.4 Querying the Database 239\u003c\/p\u003e \u003cp\u003e14.4.1 The Basic Select Query 239\u003c\/p\u003e \u003cp\u003e14.4.2 More Complex Queries 240\u003c\/p\u003e \u003cp\u003e14.5 Modifying the Database Structure 244\u003c\/p\u003e \u003cp\u003e14.6 Selected Features of SQL 249\u003c\/p\u003e \u003cp\u003e14.6.1 Changing Data 249\u003c\/p\u003e \u003cp\u003e14.6.2 Functions in SQL 249\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Connecting R to an SQL Database 253\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIV Data Wrangling 257\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Anonymous Data 261\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Data Wrangling in the tidyverse 265\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e17.1 Importing the Data 266\u003c\/p\u003e \u003cp\u003e17.1.1 Importing from an SQLRDBMS 266\u003c\/p\u003e \u003cp\u003e17.1.2 Importing Flat Files in the Tidyverse 267\u003c\/p\u003e \u003cp\u003e17.2 Tidy Data 275\u003c\/p\u003e \u003cp\u003e17.3 Tidying Up Data with tidyr 277\u003c\/p\u003e \u003cp\u003e17.3.1 Splitting Tables 278\u003c\/p\u003e \u003cp\u003e17.3.2 Convert Headers to Data 281\u003c\/p\u003e \u003cp\u003e17.3.3 Spreading One Column Over Many 284\u003c\/p\u003e \u003cp\u003e17.3.4 Split One Columns into Many 285\u003c\/p\u003e \u003cp\u003e17.3.5 Merge Multiple Columns Into One 286\u003c\/p\u003e \u003cp\u003e17.3.6 Wrong Data 287\u003c\/p\u003e \u003cp\u003e17.4 SQL-like Functionality via dplyr 288\u003c\/p\u003e \u003cp\u003e17.4.1 Selecting Columns 288\u003c\/p\u003e \u003cp\u003e17.4.2 Filtering Rows 289\u003c\/p\u003e \u003cp\u003e17.4.3 Joining 290\u003c\/p\u003e \u003cp\u003e17.4.4 Mutating Data 293\u003c\/p\u003e \u003cp\u003e17.4.5 Set Operations 296\u003c\/p\u003e \u003cp\u003e17.5 String Manipulation in the tidyverse 299\u003c\/p\u003e \u003cp\u003e17.5.1 Basic String Manipulation 300\u003c\/p\u003e \u003cp\u003e17.5.2 Pattern Matching with Regular Expressions 302\u003c\/p\u003e \u003cp\u003e17.6 Dates with lubridate 314\u003c\/p\u003e \u003cp\u003e17.6.1 ISO 8601 Format 315\u003c\/p\u003e \u003cp\u003e17.6.2 Time-zones 317\u003c\/p\u003e \u003cp\u003e17.6.3 Extract Date and Time Components 318\u003c\/p\u003e \u003cp\u003e17.6.4 Calculating with Date-times 319\u003c\/p\u003e \u003cp\u003e17.7 Factors with Forcats 325\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Dealing with Missing Data 333\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e18.1 Reasons for Data to be Missing 334\u003c\/p\u003e \u003cp\u003e18.2 Methods to Handle Missing Data 336\u003c\/p\u003e \u003cp\u003e18.2.1 Alternative Solutions to Missing Data 336\u003c\/p\u003e \u003cp\u003e18.2.2 Predictive Mean Matching(PMM) 338\u003c\/p\u003e \u003cp\u003e18.3 R Packages to Deal with Missing Data 339\u003c\/p\u003e \u003cp\u003e18.3.1 mice 339\u003c\/p\u003e \u003cp\u003e18.3.2 missForest 340\u003c\/p\u003e \u003cp\u003e18.3.3 Hmisc 341\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 Data Binning 343\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e19.1 What is Binning and Why Use It 343\u003c\/p\u003e \u003cp\u003e19.2 Tuning the Binning Procedure 347\u003c\/p\u003e \u003cp\u003e19.3 More Complex Cases: Matrix Binning 352\u003c\/p\u003e \u003cp\u003e19.4 Weight of Evidence and Information Value 359\u003c\/p\u003e \u003cp\u003e19.4.1 Weight of Evidence(WOE) 359\u003c\/p\u003e \u003cp\u003e19.4.2 Information Value(IV) 359\u003c\/p\u003e \u003cp\u003e19.4.3 WOE and IV in R 359\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20 Factoring Analysis and Principle Components 363\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e20.1 Principle Components Analysis (PCA) 364\u003c\/p\u003e \u003cp\u003e20.2 Factor Analysis 368\u003c\/p\u003e \u003cp\u003e\u003cb\u003eV Modelling 373\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e21 Regression Models 375\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e21.1 Linear Regression 375\u003c\/p\u003e \u003cp\u003e21.2 Multiple Linear Regression 379\u003c\/p\u003e \u003cp\u003e21.2.1 Poisson Regression 379\u003c\/p\u003e \u003cp\u003e21.2.2 Non-linear Regression 381\u003c\/p\u003e \u003cp\u003e21.3 Performance of Regression Models 384\u003c\/p\u003e \u003cp\u003e21.3.1 Mean Square Error (MSE) 384\u003c\/p\u003e \u003cp\u003e21.3.2 \u003ci\u003eR\u003c\/i\u003e-Squared 384\u003c\/p\u003e \u003cp\u003e21.3.3 Mean Average Deviation(MAD) 386\u003c\/p\u003e \u003cp\u003e\u003cb\u003e22 Classification Models 387\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e22.1 Logistic Regression 388\u003c\/p\u003e \u003cp\u003e22.2 Performance of Binary Classification Models 390\u003c\/p\u003e \u003cp\u003e22.2.1 The Confusion Matrix and Related Measures 391\u003c\/p\u003e \u003cp\u003e22.2.2 ROC 393\u003c\/p\u003e \u003cp\u003e22.2.3 The AUC 396\u003c\/p\u003e \u003cp\u003e22.2.4 The Gini Coefficient 397\u003c\/p\u003e \u003cp\u003e22.2.5 Kolmogorov-Smirnov (KS) for Logistic Regression 398\u003c\/p\u003e \u003cp\u003e22.2.6 Finding an Optimal Cut-off 399\u003c\/p\u003e \u003cp\u003e\u003cb\u003e23 Learning Machines 405\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e23.1 Decision Tree 407\u003c\/p\u003e \u003cp\u003e23.1.1 Essential Background 407\u003c\/p\u003e \u003cp\u003e23.1.2 Important Considerations 412\u003c\/p\u003e \u003cp\u003e23.1.3 Growing Trees with the Package rpart 414\u003c\/p\u003e \u003cp\u003e23.1.4 Evaluating the Performance of a Decision Tree 424\u003c\/p\u003e \u003cp\u003e23.2 Random Forest 428\u003c\/p\u003e \u003cp\u003e23.3 Artificial Neural Networks (ANNs) 434\u003c\/p\u003e \u003cp\u003e23.3.1 The Basics of ANNs in R 434\u003c\/p\u003e \u003cp\u003e23.3.2 Neural Networks in R 436\u003c\/p\u003e \u003cp\u003e23.3.3 The Work-flow to for Fitting a NN 438\u003c\/p\u003e \u003cp\u003e23.3.4 Cross Validate the NN 444\u003c\/p\u003e \u003cp\u003e23.4 Support Vector Machine 447\u003c\/p\u003e \u003cp\u003e23.4.1 Fitting a SVM in R 447\u003c\/p\u003e \u003cp\u003e23.4.2 Optimizing the SVM 449\u003c\/p\u003e \u003cp\u003e23.5 Unsupervised Learning and Clustering 450\u003c\/p\u003e \u003cp\u003e23.5.1 k-Means Clustering 450\u003c\/p\u003e \u003cp\u003e23.5.2 Visualizing Clusters in Three Dimensions 462\u003c\/p\u003e \u003cp\u003e23.5.3 Fuzzy Clustering 464\u003c\/p\u003e \u003cp\u003e23.5.4 Hierarchical Clustering 466\u003c\/p\u003e \u003cp\u003e23.5.5 Other Clustering Methods 468\u003c\/p\u003e \u003cp\u003e\u003cb\u003e24 Towards a Tidy Modelling Cycle with modelr 469\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e24.1 Adding Predictions 470\u003c\/p\u003e \u003cp\u003e24.2 Adding Residuals 471\u003c\/p\u003e \u003cp\u003e24.3 Bootstrapping Data 472\u003c\/p\u003e \u003cp\u003e24.4 Other Functions of modelr 474\u003c\/p\u003e \u003cp\u003e\u003cb\u003e25 Model Validation 475\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e25.1 Model Quality Measures 476\u003c\/p\u003e \u003cp\u003e25.2 Predictions and Residuals 477\u003c\/p\u003e \u003cp\u003e25.3 Bootstrapping 479\u003c\/p\u003e \u003cp\u003e25.3.1 Bootstrapping in Base R 479\u003c\/p\u003e \u003cp\u003e25.3.2 Bootstrapping in the tidyverse with modelr 481\u003c\/p\u003e \u003cp\u003e25.4 Cross-Validation 483\u003c\/p\u003e \u003cp\u003e25.4.1 Elementary Cross Validation 483\u003c\/p\u003e \u003cp\u003e25.4.2 Monte Carlo Cross Validation 486\u003c\/p\u003e \u003cp\u003e25.4.3 \u003ci\u003ek\u003c\/i\u003e-Fold Cross Validation 488\u003c\/p\u003e \u003cp\u003e25.4.4 Comparing Cross Validation Methods 489\u003c\/p\u003e \u003cp\u003e25.5 Validation in a Broader Perspective 492\u003c\/p\u003e \u003cp\u003e\u003cb\u003e26 Labs 495\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e26.1 Financial Analysis with quantmod 495\u003c\/p\u003e \u003cp\u003e26.1.1 The Basics of quantmod 495\u003c\/p\u003e \u003cp\u003e26.1.2 Types of Data Available in quantmod 496\u003c\/p\u003e \u003cp\u003e26.1.3 Plotting with quantmod 497\u003c\/p\u003e \u003cp\u003e26.1.4 The quantmod Data Structure 500\u003c\/p\u003e \u003cp\u003e26.1.5 Support Functions Supplied by quantmod 502\u003c\/p\u003e \u003cp\u003e26.1.6 Financial Modelling in quantmod 504\u003c\/p\u003e \u003cp\u003e\u003cb\u003e27 Multi Criteria Decision Analysis (MCDA) 511\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e27.1 What and Why 511\u003c\/p\u003e \u003cp\u003e27.2 General Work-flow 513\u003c\/p\u003e \u003cp\u003e27.3 Identify the Issue at Hand: Steps 1 and 2 516\u003c\/p\u003e \u003cp\u003e27.4 Step3: the Decision Matrix 518\u003c\/p\u003e \u003cp\u003e27.4.1 Construct a Decision Matrix 518\u003c\/p\u003e \u003cp\u003e27.4.2 Normalize the Decision Matrix 520\u003c\/p\u003e \u003cp\u003e27.5 Step 4: Delete Inefficient and Unacceptable Alternatives 521\u003c\/p\u003e \u003cp\u003e27.5.1 Unacceptable Alternatives 521\u003c\/p\u003e \u003cp\u003e27.5.2 Dominance – Inefficient Alternatives 521\u003c\/p\u003e \u003cp\u003e27.6 Plotting Preference Relationships 524\u003c\/p\u003e \u003cp\u003e27.7 Step5: MCDA Methods 526\u003c\/p\u003e \u003cp\u003e27.7.1 Examples of Non-compensatory Methods 526\u003c\/p\u003e \u003cp\u003e27.7.2 The Weighted Sum Method(WSM) 527\u003c\/p\u003e \u003cp\u003e27.7.3 Weighted Product Method(WPM) 530\u003c\/p\u003e \u003cp\u003e27.7.4 ELECTRE 530\u003c\/p\u003e \u003cp\u003e27.7.5 PROMethEE 540\u003c\/p\u003e \u003cp\u003e27.7.6 PCA(Gaia) 553\u003c\/p\u003e \u003cp\u003e27.7.7 Outranking Methods 557\u003c\/p\u003e \u003cp\u003e27.7.8 Goal Programming 558\u003c\/p\u003e \u003cp\u003e27.8 Summary MCDA 561\u003c\/p\u003e \u003cp\u003e\u003cb\u003eVI Introduction to Companies 563\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e28 Financial Accounting (FA) 567\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e28.1 The Statements of Accounts 568\u003c\/p\u003e \u003cp\u003e28.1.1 Income Statement 568\u003c\/p\u003e \u003cp\u003e28.1.2 Net Income: The P\u0026amp;L statement 568\u003c\/p\u003e \u003cp\u003e28.1.3 Balance Sheet 569\u003c\/p\u003e \u003cp\u003e28.2 The Value Chain 571\u003c\/p\u003e \u003cp\u003e28.3 Further, Terminology 573\u003c\/p\u003e \u003cp\u003e28.4 Selected Financial Ratios 575\u003c\/p\u003e \u003cp\u003e\u003cb\u003e29 Management Accounting 583\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e29.1 Introduction 583\u003c\/p\u003e \u003cp\u003e29.1.1 Definition of Management Accounting (MA) 583\u003c\/p\u003e \u003cp\u003e29.1.2 Management Information Systems (MIS) 584\u003c\/p\u003e \u003cp\u003e29.2 Selected Methods in MA 585\u003c\/p\u003e \u003cp\u003e29.2.1 Cost Accounting 585\u003c\/p\u003e \u003cp\u003e29.2.2 Selected Cost Types 587\u003c\/p\u003e \u003cp\u003e29.3 Selected Use Cases of MA 590\u003c\/p\u003e \u003cp\u003e29.3.1 Balanced Scorecard 590\u003c\/p\u003e \u003cp\u003e29.3.2 Key Performance Indicators (KPIs) 591\u003c\/p\u003e \u003cp\u003e\u003cb\u003e30 Asset Valuation Basics 597\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e30.1 Time Value of Money 598\u003c\/p\u003e \u003cp\u003e30.1.1 Interest Basics 598\u003c\/p\u003e \u003cp\u003e30.1.2 Specific Interest Rate Concepts 598\u003c\/p\u003e \u003cp\u003e30.1.3 Discounting 600\u003c\/p\u003e \u003cp\u003e30.2 Cash 601\u003c\/p\u003e \u003cp\u003e30.3 Bonds 602\u003c\/p\u003e \u003cp\u003e30.3.1 Features of a Bond 602\u003c\/p\u003e \u003cp\u003e30.3.2 Valuation of Bonds 604\u003c\/p\u003e \u003cp\u003e30.3.3 Duration 606\u003c\/p\u003e \u003cp\u003e30.4 The Capital Asset Pricing Model (CAPM) 610\u003c\/p\u003e \u003cp\u003e30.4.1 The CAPM Framework 610\u003c\/p\u003e \u003cp\u003e30.4.2 The CAPM and Risk 612\u003c\/p\u003e \u003cp\u003e30.4.3 Limitations and Shortcomings of the CAPM 612\u003c\/p\u003e \u003cp\u003e30.5 Equities 614\u003c\/p\u003e \u003cp\u003e30.5.1 Definition 614\u003c\/p\u003e \u003cp\u003e30.5.2 Short History 614\u003c\/p\u003e \u003cp\u003e30.5.3 Valuation of Equities 615\u003c\/p\u003e \u003cp\u003e30.5.4 Absolute Value Models 616\u003c\/p\u003e \u003cp\u003e30.5.5 Relative Value Models 625\u003c\/p\u003e \u003cp\u003e30.5.6 Selection of Valuation Methods 630\u003c\/p\u003e \u003cp\u003e30.5.7 Pitfalls in Company Valuation 631\u003c\/p\u003e \u003cp\u003e30.6 Forwards and Futures 638\u003c\/p\u003e \u003cp\u003e30.7 Options 640\u003c\/p\u003e \u003cp\u003e30.7.1 Definitions 640\u003c\/p\u003e \u003cp\u003e30.7.2 Commercial Aspects 642\u003c\/p\u003e \u003cp\u003e30.7.3 Short History 643\u003c\/p\u003e \u003cp\u003e30.7.4 Valuation of Options at Maturity 644\u003c\/p\u003e \u003cp\u003e30.7.5 The Black and Scholes Model 649\u003c\/p\u003e \u003cp\u003e30.7.6 The Binomial Model 654\u003c\/p\u003e \u003cp\u003e30.7.7 Dependencies of the Option Price 660\u003c\/p\u003e \u003cp\u003e30.7.8 The Greeks 664\u003c\/p\u003e \u003cp\u003e30.7.9 Delta Hedging 665\u003c\/p\u003e \u003cp\u003e30.7.10 Linear Option Strategies 667\u003c\/p\u003e \u003cp\u003e30.7.11 Integrated Option Strategies 674\u003c\/p\u003e \u003cp\u003e30.7.12 Exotic Options 678\u003c\/p\u003e \u003cp\u003e30.7.13 Capital Protected Structures 680\u003c\/p\u003e \u003cp\u003e\u003cb\u003eVII Reporting 683\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e31 A Grammar of Graphics with ggplot2 687\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e31.1 TheBasicsofggplot2 688\u003c\/p\u003e \u003cp\u003e31.2 Over-plotting 692\u003c\/p\u003e \u003cp\u003e31.3 CaseStudyforggplot2 696\u003c\/p\u003e \u003cp\u003e\u003cb\u003e32 R Markdown 699\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e33 knitr and LATEX 703\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e34 An Automated Development Cycle 707\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e35 Writing and Communication Skills 709\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e36 Interactive Apps 713\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e36.1 Shiny 715\u003c\/p\u003e \u003cp\u003e36.2 Browser Born Data Visualization 719\u003c\/p\u003e \u003cp\u003e36.2.1 HTML-widgets 719\u003c\/p\u003e \u003cp\u003e36.2.2 Interactive Maps with leaflet 720\u003c\/p\u003e \u003cp\u003e36.2.3 Interactive Data Visualisation with ggvis 721\u003c\/p\u003e \u003cp\u003e36.2.4 googleVis 723\u003c\/p\u003e \u003cp\u003e36.3 Dashboards 725\u003c\/p\u003e \u003cp\u003e36.3.1 The Business Case: a Diversity Dashboard 726\u003c\/p\u003e \u003cp\u003e36.3.2 A Dashboard with flexdashboard 731\u003c\/p\u003e \u003cp\u003e36.3.3 A Dashboard with shinydashboard 737\u003c\/p\u003e \u003cp\u003e\u003cb\u003eVIII Bigger and Faster R 741\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e37 Parallel Computing 743\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e37.1 Combine foreach and doParallel 745\u003c\/p\u003e \u003cp\u003e37.2 Distribute Calculations over LAN with Snow 748\u003c\/p\u003e \u003cp\u003e37.3 Using the GPU 752\u003c\/p\u003e \u003cp\u003e37.3.1 Getting Started with gpuR 754\u003c\/p\u003e \u003cp\u003e37.3.2 On the Importance of Memory use 757\u003c\/p\u003e \u003cp\u003e37.3.3 Conclusions for GPU Programming 759\u003c\/p\u003e \u003cp\u003e\u003cb\u003e38 R and Big Data 761\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e38.1 Use a Powerful Server 763\u003c\/p\u003e \u003cp\u003e38.1.1 Use R on a Server 763\u003c\/p\u003e \u003cp\u003e38.1.2 Let the Database Server do the Heavy Lifting 763\u003c\/p\u003e \u003cp\u003e38.2 Using more Memory than we have RAM 765\u003c\/p\u003e \u003cp\u003e\u003cb\u003e39 Parallelism for Big Data 767\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e39.1 Apache Hadoop 769\u003c\/p\u003e \u003cp\u003e39.2 Apache Spark 771\u003c\/p\u003e \u003cp\u003e39.2.1 Installing Spark 771\u003c\/p\u003e \u003cp\u003e39.2.2 Running Spark 773\u003c\/p\u003e \u003cp\u003e39.2.3 SparkR 776\u003c\/p\u003e \u003cp\u003e39.2.4 sparklyr 788\u003c\/p\u003e \u003cp\u003e39.2.5 SparkR or sparklyr 791\u003c\/p\u003e \u003cp\u003e\u003cb\u003e40 The Need for Speed 793\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e40.1 Benchmarking 794\u003c\/p\u003e \u003cp\u003e40.2 Optimize Code 797\u003c\/p\u003e \u003cp\u003e40.2.1 Avoid Repeating the Same 797\u003c\/p\u003e \u003cp\u003e40.2.2 Use Vectorisation where Appropriate 797\u003c\/p\u003e \u003cp\u003e40.2.3 Pre-allocating Memory 799\u003c\/p\u003e \u003cp\u003e40.2.4 Use the Fastest Function 800\u003c\/p\u003e \u003cp\u003e40.2.5 Use the Fastest Package 801\u003c\/p\u003e \u003cp\u003e40.2.6 Be Mindful about Details 802\u003c\/p\u003e \u003cp\u003e40.2.7 Compile Functions 804\u003c\/p\u003e \u003cp\u003e40.2.8 Use C or C++ Code in R 806\u003c\/p\u003e \u003cp\u003e40.2.9 Using a C++ Source File in R 809\u003c\/p\u003e \u003cp\u003e40.2.10CallCompiledC++Functions in R 811\u003c\/p\u003e \u003cp\u003e40.3 Profiling Code 812\u003c\/p\u003e \u003cp\u003e40.3.1 The Package profr 813\u003c\/p\u003e \u003cp\u003e40.3.2 The Package proftools 813\u003c\/p\u003e \u003cp\u003e40.4 Optimize Your Computer 817\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIX Appendices 819\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eA Create your own R Package 821\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA.1 Creating the Package in the R Console 823\u003c\/p\u003e \u003cp\u003eA.2 Update the Package Description 825\u003c\/p\u003e \u003cp\u003eA.3 Documenting the Functionsxs 826\u003c\/p\u003e \u003cp\u003eA.4 Loading the Package 827\u003c\/p\u003e \u003cp\u003eA.5 Further Steps 828\u003c\/p\u003e \u003cp\u003e\u003cb\u003eB Levels of Measurement 829\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eB.1 Nominal Scale 829\u003c\/p\u003e \u003cp\u003eB.2 Ordinal Scale 830\u003c\/p\u003e \u003cp\u003eB.3 Interval Scale 831\u003c\/p\u003e \u003cp\u003eB.4 Ratio Scale 832\u003c\/p\u003e \u003cp\u003e\u003cb\u003eC Trademark Notices 833\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eC.1 General Trademark Notices 834\u003c\/p\u003e \u003cp\u003eC.2 R-Related Notices 835\u003c\/p\u003e \u003cp\u003eC.2.1 Crediting Developers of R Packages 835\u003c\/p\u003e \u003cp\u003eC.2.2 The R-packages used in this Book 835\u003c\/p\u003e \u003cp\u003e\u003cb\u003eD Code Not Shown in the Body of the Book 839\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eE Answers to Selected Questions 845\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBibliography 859\u003c\/p\u003e \u003cp\u003eNomenclature 869\u003c\/p\u003e \u003cp\u003eIndex 881 \u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":48866406957399,"sku":"9781119632726","price":98.75,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781119632726.jpg?v=1722278498"},{"product_id":"data-science-for-dummies-9781119811558","title":"Data Science For Dummies","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eMonetize your company's data and data science expertise without spending a fortune on hiring independent strategy consultants to help What if there was one simple, clear process for ensuring that all your company's data science projects achieve a high a return on investment? What if you could validate your ideas for future data science projects, and select the one idea that's most prime for achieving profitability while also moving your company closer to its business vision? There is.    Industry-acclaimed data science consultant, Lillian Pierson, shares her proprietary STAR Framework  A simple, proven process for leading profit-forming data science projects.    Not sure what data science is yet? Don't worry! Parts 1 and 2 of Data Science For Dummies will get all the bases covered for you. And if you're already a data science expert? Then you really won't want to miss the data science strategy and data monetization gems that are shared in Part 3 onward throughout this book.    Data Science For Dummies demonstrates: The only process you'll ever need to lead profitable data science projectsSecret, reverse-engineered data monetization tactics that no one's talking aboutThe shocking truth about how simple natural language processing can beHow to beat the crowd of data professionals by cultivating your own unique blend of data science expertise Whether you're new to the data science field or already a decade in, you're sure to learn something new and incredibly valuable from Data Science For Dummies. Discover how to generate massive business wins from your company's data by picking up your copy today.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eIntroduction 1\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 1: Getting Started with Data Science 5\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eChapter 1: Wrapping Your Head Around Data Science 7\u003c\/p\u003e \u003cp\u003eChapter 2: Tapping into Critical Aspects of Data Engineering 19\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 2: Using Data Science to Extract Meaning from Your Data 37\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eChapter 3: Machine Learning Means Using a Machine to Learn from Data 39\u003c\/p\u003e \u003cp\u003eChapter 4: Math, Probability, and Statistical Modeling 51\u003c\/p\u003e \u003cp\u003eChapter 5: Grouping Your Way into Accurate Predictions 77\u003c\/p\u003e \u003cp\u003eChapter 6: Coding Up Data Insights and Decision Engines 103\u003c\/p\u003e \u003cp\u003eChapter 7: Generating Insights with Software Applications 137\u003c\/p\u003e \u003cp\u003eChapter 8: Telling Powerful Stories with Data 161\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 3: Taking Stock of Your Data Science Capabilities 187\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eChapter 9: Developing Your Business Acumen 189\u003c\/p\u003e \u003cp\u003eChapter 10: Improving Operations 205\u003c\/p\u003e \u003cp\u003eChapter 11: Making Marketing Improvements 229\u003c\/p\u003e \u003cp\u003eChapter 12: Enabling Improved Decision-Making 245\u003c\/p\u003e \u003cp\u003eChapter 13: Decreasing Lending Risk and Fighting Financial Crimes 265\u003c\/p\u003e \u003cp\u003eChapter 14: Monetizing Data and Data Science Expertise 275\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 4: Assessing Your Data Science Options 289\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eChapter 15: Gathering Important Information about Your Company 291\u003c\/p\u003e \u003cp\u003eChapter 16: Narrowing In on the Optimal Data Science Use Case 311\u003c\/p\u003e \u003cp\u003eChapter 17: Planning for Future Data Science Project Success 327\u003c\/p\u003e \u003cp\u003eChapter 18: Blazing a Path to Data Science Career Success 341\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 5: The Part of Tens 367\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eChapter 19: Ten Phenomenal Resources for Open Data 369\u003c\/p\u003e \u003cp\u003eChapter 20: Ten Free or Low-Cost Data Science Tools and Applications 381\u003c\/p\u003e \u003cp\u003eIndex 397\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":48866417606999,"sku":"9781119811558","price":24.64,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781119811558.jpg?v=1722278545"},{"product_id":"comptia-data-study-guide-9781119845256","title":"CompTIA Data Study Guide","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eIntroduction xv\u003c\/p\u003e \u003cp\u003eAssessment Test xxii\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1 Today’s Data Analyst 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWelcome to the World of Analytics 2\u003c\/p\u003e \u003cp\u003eData 2\u003c\/p\u003e \u003cp\u003eStorage 3\u003c\/p\u003e \u003cp\u003eComputing Power 4\u003c\/p\u003e \u003cp\u003eCareers in Analytics 5\u003c\/p\u003e \u003cp\u003eThe Analytics Process 6\u003c\/p\u003e \u003cp\u003eData Acquisition 7\u003c\/p\u003e \u003cp\u003eCleaning and Manipulation 7\u003c\/p\u003e \u003cp\u003eAnalysis 8\u003c\/p\u003e \u003cp\u003eVisualization 8\u003c\/p\u003e \u003cp\u003eReporting and Communication 8\u003c\/p\u003e \u003cp\u003eAnalytics Techniques 10\u003c\/p\u003e \u003cp\u003eDescriptive Analytics 10\u003c\/p\u003e \u003cp\u003ePredictive Analytics 11\u003c\/p\u003e \u003cp\u003ePrescriptive Analytics 11\u003c\/p\u003e \u003cp\u003eMachine Learning, Artificial Intelligence, and Deep Learning 11\u003c\/p\u003e \u003cp\u003eData Governance 13\u003c\/p\u003e \u003cp\u003eAnalytics Tools 13\u003c\/p\u003e \u003cp\u003eSummary 15\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 Understanding Data 17\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eExploring Data Types 18\u003c\/p\u003e \u003cp\u003eStructured Data Types 20\u003c\/p\u003e \u003cp\u003eUnstructured Data Types 31\u003c\/p\u003e \u003cp\u003eCategories of Data 36\u003c\/p\u003e \u003cp\u003eCommon Data Structures 39\u003c\/p\u003e \u003cp\u003eStructured Data 39\u003c\/p\u003e \u003cp\u003eUnstructured Data 41\u003c\/p\u003e \u003cp\u003eSemi-structured\u003c\/p\u003e \u003cp\u003eData 42\u003c\/p\u003e \u003cp\u003eCommon File Formats 42\u003c\/p\u003e \u003cp\u003eText Files 42\u003c\/p\u003e \u003cp\u003eJavaScript Object Notation 44\u003c\/p\u003e \u003cp\u003eExtensible Markup Language (XML) 45\u003c\/p\u003e \u003cp\u003eHyperText Markup Language (HTML) 47\u003c\/p\u003e \u003cp\u003eSummary 48\u003c\/p\u003e \u003cp\u003eExam Essentials 49\u003c\/p\u003e \u003cp\u003eReview Questions 51\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 Databases and Data Acquisition 57\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eExploring Databases 58\u003c\/p\u003e \u003cp\u003eThe Relational Model 59\u003c\/p\u003e \u003cp\u003eRelational Databases 62\u003c\/p\u003e \u003cp\u003eNonrelational Databases 68\u003c\/p\u003e \u003cp\u003eDatabase Use Cases 71\u003c\/p\u003e \u003cp\u003eOnline Transactional Processing 71\u003c\/p\u003e \u003cp\u003eOnline Analytical Processing 74\u003c\/p\u003e \u003cp\u003eSchema Concepts 75\u003c\/p\u003e \u003cp\u003eData Acquisition Concepts 81\u003c\/p\u003e \u003cp\u003eIntegration 81\u003c\/p\u003e \u003cp\u003eData Collection Methods 83\u003c\/p\u003e \u003cp\u003eWorking with Data 88\u003c\/p\u003e \u003cp\u003eData Manipulation 89\u003c\/p\u003e \u003cp\u003eQuery Optimization 96\u003c\/p\u003e \u003cp\u003eSummary 99\u003c\/p\u003e \u003cp\u003eExam Essentials 100\u003c\/p\u003e \u003cp\u003eReview Questions 101\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 Data Quality 105\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eData Quality Challenges 106\u003c\/p\u003e \u003cp\u003eDuplicate Data 106\u003c\/p\u003e \u003cp\u003eRedundant Data 107\u003c\/p\u003e \u003cp\u003eMissing Values 110\u003c\/p\u003e \u003cp\u003eInvalid Data 111\u003c\/p\u003e \u003cp\u003eNonparametric data 112\u003c\/p\u003e \u003cp\u003eData Outliers 113\u003c\/p\u003e \u003cp\u003eSpecification Mismatch 114\u003c\/p\u003e \u003cp\u003eData Type Validation 114\u003c\/p\u003e \u003cp\u003eData Manipulation Techniques 116\u003c\/p\u003e \u003cp\u003eRecoding Data 116\u003c\/p\u003e \u003cp\u003eDerived Variables 117\u003c\/p\u003e \u003cp\u003eData Merge 118\u003c\/p\u003e \u003cp\u003eData Blending 119\u003c\/p\u003e \u003cp\u003eConcatenation 121\u003c\/p\u003e \u003cp\u003eData Append 121\u003c\/p\u003e \u003cp\u003eImputation 122\u003c\/p\u003e \u003cp\u003eReduction 124\u003c\/p\u003e \u003cp\u003eAggregation 126\u003c\/p\u003e \u003cp\u003eTransposition 127\u003c\/p\u003e \u003cp\u003eNormalization 128\u003c\/p\u003e \u003cp\u003eParsing\/String Manipulation 130\u003c\/p\u003e \u003cp\u003eManaging Data Quality 132\u003c\/p\u003e \u003cp\u003eCircumstances to Check for Quality 132\u003c\/p\u003e \u003cp\u003eAutomated Validation 136\u003c\/p\u003e \u003cp\u003eData Quality Dimensions 136\u003c\/p\u003e \u003cp\u003eData Quality Rules and Metrics 140\u003c\/p\u003e \u003cp\u003eMethods to Validate Quality 142\u003c\/p\u003e \u003cp\u003eSummary 144\u003c\/p\u003e \u003cp\u003eExam Essentials 145\u003c\/p\u003e \u003cp\u003eReview Questions 146\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 Data Analysis and Statistics 151\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eFundamentals of Statistics 152\u003c\/p\u003e \u003cp\u003eDescriptive Statistics 155\u003c\/p\u003e \u003cp\u003eMeasures of Frequency 155\u003c\/p\u003e \u003cp\u003eMeasures of Central Tendency 160\u003c\/p\u003e \u003cp\u003eMeasures of Dispersion 164\u003c\/p\u003e \u003cp\u003eMeasures of Position 173\u003c\/p\u003e \u003cp\u003eInferential Statistics 175\u003c\/p\u003e \u003cp\u003eConfidence Intervals 175\u003c\/p\u003e \u003cp\u003eHypothesis Testing 179\u003c\/p\u003e \u003cp\u003eSimple Linear Regression 186\u003c\/p\u003e \u003cp\u003eAnalysis Techniques 190\u003c\/p\u003e \u003cp\u003eDetermine Type of Analysis 190\u003c\/p\u003e \u003cp\u003eTypes of Analysis 191\u003c\/p\u003e \u003cp\u003eExploratory Data Analysis 192\u003c\/p\u003e \u003cp\u003eSummary 192\u003c\/p\u003e \u003cp\u003eExam Essentials 194\u003c\/p\u003e \u003cp\u003eReview Questions 196\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 Data Analytics Tools 201\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSpreadsheets 202\u003c\/p\u003e \u003cp\u003eMicrosoft Excel 203\u003c\/p\u003e \u003cp\u003eProgramming Languages 205\u003c\/p\u003e \u003cp\u003eR 205\u003c\/p\u003e \u003cp\u003ePython 206\u003c\/p\u003e \u003cp\u003eStructured Query Language (SQL) 208\u003c\/p\u003e \u003cp\u003eStatistics Packages 209\u003c\/p\u003e \u003cp\u003eIBM SPSS 210\u003c\/p\u003e \u003cp\u003eSAS 211\u003c\/p\u003e \u003cp\u003eStata 211\u003c\/p\u003e \u003cp\u003eMinitab 212\u003c\/p\u003e \u003cp\u003eMachine Learning 212\u003c\/p\u003e \u003cp\u003eIBM SPSS Modeler 213\u003c\/p\u003e \u003cp\u003eRapidMiner 214\u003c\/p\u003e \u003cp\u003eAnalytics Suites 217\u003c\/p\u003e \u003cp\u003eIBM Cognos 217\u003c\/p\u003e \u003cp\u003ePower BI 218\u003c\/p\u003e \u003cp\u003eMicroStrategy 219\u003c\/p\u003e \u003cp\u003eDomo 220\u003c\/p\u003e \u003cp\u003eDatorama 221\u003c\/p\u003e \u003cp\u003eAWS QuickSight 222\u003c\/p\u003e \u003cp\u003eTableau 222\u003c\/p\u003e \u003cp\u003eQlik 224\u003c\/p\u003e \u003cp\u003eBusinessObjects 225\u003c\/p\u003e \u003cp\u003eSummary 225\u003c\/p\u003e \u003cp\u003eExam Essentials 225\u003c\/p\u003e \u003cp\u003eReview Questions 227\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7 Data Visualization with Reports and Dashboards 231\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eUnderstanding Business Requirements 232\u003c\/p\u003e \u003cp\u003eUnderstanding Report Design Elements 235\u003c\/p\u003e \u003cp\u003eReport Cover Page 236\u003c\/p\u003e \u003cp\u003eExecutive Summary 237\u003c\/p\u003e \u003cp\u003eDesign Elements 239\u003c\/p\u003e \u003cp\u003eDocumentation Elements 244\u003c\/p\u003e \u003cp\u003eUnderstanding Dashboard Development Methods 247\u003c\/p\u003e \u003cp\u003eConsumer Types 247\u003c\/p\u003e \u003cp\u003eData Source Considerations 248\u003c\/p\u003e \u003cp\u003eData Type Considerations 249\u003c\/p\u003e \u003cp\u003eDevelopment Process 250\u003c\/p\u003e \u003cp\u003eDelivery Considerations 250\u003c\/p\u003e \u003cp\u003eOperational Considerations 252\u003c\/p\u003e \u003cp\u003eExploring Visualization Types 252\u003c\/p\u003e \u003cp\u003eCharts 252\u003c\/p\u003e \u003cp\u003eMaps 258\u003c\/p\u003e \u003cp\u003eWaterfall 264\u003c\/p\u003e \u003cp\u003eInfographic 266\u003c\/p\u003e \u003cp\u003eWord Cloud 267\u003c\/p\u003e \u003cp\u003eComparing Report Types 268\u003c\/p\u003e \u003cp\u003eStatic and Dynamic 268\u003c\/p\u003e \u003cp\u003eAd Hoc 269\u003c\/p\u003e \u003cp\u003eSelf-Service (On-Demand) 269\u003c\/p\u003e \u003cp\u003eRecurring Reports 269\u003c\/p\u003e \u003cp\u003eTactical and Research 270\u003c\/p\u003e \u003cp\u003eSummary 271\u003c\/p\u003e \u003cp\u003eExam Essentials 272\u003c\/p\u003e \u003cp\u003eReview Questions 274\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8 Data Governance 279\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eData Governance Concepts 280\u003c\/p\u003e \u003cp\u003eData Governance Roles 281\u003c\/p\u003e \u003cp\u003eAccess Requirements 281\u003c\/p\u003e \u003cp\u003eSecurity Requirements 286\u003c\/p\u003e \u003cp\u003eStorage Environment Requirements 289\u003c\/p\u003e \u003cp\u003eUse Requirements 291\u003c\/p\u003e \u003cp\u003eEntity Relationship Requirements 292\u003c\/p\u003e \u003cp\u003eData Classification Requirements 292\u003c\/p\u003e \u003cp\u003eJurisdiction Requirements 297\u003c\/p\u003e \u003cp\u003eBreach Reporting Requirements 298\u003c\/p\u003e \u003cp\u003eUnderstanding Master Data Management 299\u003c\/p\u003e \u003cp\u003eProcesses 300\u003c\/p\u003e \u003cp\u003eCircumstances 301\u003c\/p\u003e \u003cp\u003eSummary 303\u003c\/p\u003e \u003cp\u003eExam Essentials 304\u003c\/p\u003e \u003cp\u003eReview Questions 306\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix Answers to the Review Questions 311\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eChapter 2: Understanding Data 312\u003c\/p\u003e \u003cp\u003eChapter 3: Databases and Data Acquisition 314\u003c\/p\u003e \u003cp\u003eChapter 4: Data Quality 315\u003c\/p\u003e \u003cp\u003eChapter 5: Data Analysis and Statistics 317\u003c\/p\u003e \u003cp\u003eChapter 6: Data Analytics Tools 319\u003c\/p\u003e \u003cp\u003eChapter 7: Data Visualization with Reports and Dashboards 322\u003c\/p\u003e \u003cp\u003eChapter 8: Data Governance 323\u003c\/p\u003e \u003cp\u003eIndex 327\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":48866420195671,"sku":"9781119845256","price":40.38,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781119845256.jpg?v=1722278560"},{"product_id":"ise-database-system-concepts-9781260084504","title":"ISE Database System Concepts","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eDatabase System Concepts\u003c\/b\u003e by Silberschatz, Korth and Sudarshan is now in its 7th edition and is one of the cornerstone texts of database education. It presents the fundamental concepts of database management in an intuitive manner geared toward allowing students to begin working with databases as quickly as possible.\u003cp\u003e\u003c\/p\u003e\u003cp\u003eThe text is designed for a first course in databases at the junior\/senior undergraduate level or the first year graduate level. It also contains additional material that can be used as supplements or as introductory material for an advanced course. Because the authors present concepts as intuitive descriptions, a familiarity with basic data structures, computer organization, and a high-level programming language are the only prerequisites. Important theoretical results are covered, but formal proofs are omitted. In place of proofs, figures and examples are used to suggest why a result is true. \u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eChapter 1: Introduction\u003cbr\u003e\u003cb\u003ePart 1: Relational Languages\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eChapter 2: Introduction to the Relational Model\u003cbr\u003e\u003cbr\u003eChapter 3: Introduction to SQL\u003cbr\u003e\u003cbr\u003eChapter 4: Intermediate SQL\u003cbr\u003e\u003cbr\u003eChapter 5: Advanced SQL\u003cbr\u003e\u003cbr\u003e\u003cb\u003ePart II: Database Design\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eChapter 6: Database Design Using the E-R Model\u003cbr\u003e\u003cbr\u003eChapter 7: Relational Database Design\u003cbr\u003e\u003cbr\u003e\u003cb\u003ePart III: Application Design and Development\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eChapter 8: Complex Data Types\u003cbr\u003e\u003cbr\u003eChapter 9: Application Development\u003cbr\u003e\u003cbr\u003e\u003cb\u003ePart IV: Big Data Analytics\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eChapter 10: Big Data\u003cbr\u003e\u003cbr\u003eChapter 11: Data Analytics\u003cbr\u003e\u003cbr\u003e\u003cb\u003ePart V: Storage Management and Indexing\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eChapter 12: Physical Storage Systems\u003cbr\u003e\u003cbr\u003eChapter 13: Data Storage Structures\u003cbr\u003e\u003cbr\u003eChapter 14: Indexing\u003cbr\u003e\u003cbr\u003e\u003cb\u003ePart VI: Query Processing and Optimization\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eChapter 15: Query Processing\u003cbr\u003e\u003cbr\u003eChapter 16: Query Optimization\u003cbr\u003e\u003cbr\u003e\u003cb\u003ePart VII: Transaction Management\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eChapter 17: Transactions\u003cbr\u003e\u003cbr\u003eChapter 18: Concurrency Control\u003cbr\u003e\u003cbr\u003eChapter 19: Recovery System\u003cbr\u003e\u003cbr\u003e\u003cb\u003ePart VIII: Parallel and Distributed Databases\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eChapter 20: Database-System Architectures\u003cbr\u003e\u003cbr\u003eChapter 21: Parallel and Distributed Storage\u003cbr\u003e\u003cbr\u003eChapter 22: Parallel and Distributed Query Processing\u003cbr\u003e\u003cbr\u003eChapter 23: Parallel and Distributed Transaction Processing\u003cbr\u003e\u003cbr\u003e\u003cb\u003ePart IX: Advanced Topics\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eChapter 24: Advanced Indexing Techniques\u003cbr\u003e\u003cbr\u003eChapter 25: Advanced Application Development\u003cbr\u003e\u003cbr\u003eChapter 26: Blockchain Databases\u003cbr\u003e\u003cbr\u003e\u003cb\u003ePart X: Appendix A\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eAppendix A: Detailed University Schema\u003cbr\u003e\u003cbr\u003e\u003cb\u003ePart XI: Online Chapters\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eChapter 27: Formal Relational Query Languages\u003cbr\u003e\u003cbr\u003eChapter 28: Advanced Relational Database Design\u003cbr\u003e\u003cbr\u003eChapter 29: Object-Based Databases\u003cbr\u003e\u003cbr\u003eChapter 30: XML\u003cbr\u003e\u003cbr\u003eChapter 31: Information Retrieval\u003cbr\u003e\u003cbr\u003eChapter 32: PostgreSQL\u003cbr\u003e\u003cbr\u003e","brand":"McGraw-Hill Education","offers":[{"title":"Default Title","offer_id":48866488910167,"sku":"9781260084504","price":59.39,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781260084504.jpg?v=1722278900"},{"product_id":"the-modern-data-warehouse-in-azure-9781484258224","title":"The Modern Data Warehouse in Azure","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eBuild a modern data warehouse on Microsoft's Azure Platform that is flexible, adaptable, and fastfast to snap together, reconfigure, and fast at delivering results to drive good decision making in your business.   Gone are the days when data warehousing projects were lumbering dinosaur-style projects that took forever, drained budgets, and produced business intelligence (BI) just in time to tell you what to do 10 years ago. This book will show you how to assemble a data warehouse solution like a jigsaw puzzle by connecting specific Azure technologies that address your own needs and bring value to your business. You will see how to implement a range of architectural patterns using batches, events, and streams for both data lake technology and SQL databases. You will discover how to manage metadata and automation to accelerate the development of your warehouse while establishing resilience at every level. And you will know how to feed downstream analytic solutions such as Power BI and Az\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1. The Rise of the Modern Data Warehouse2. The SQL Engine3. The Integration Engine4. The Ingestion Architecture5. 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