Databases / Data management Books

Book SynopsisThe Ethics of Artificial Intelligence in Education identifies and confronts key ethical issues generated over years of AI research, development, and deployment in learning contexts. Adaptive, automated, and data-driven education systems are increasingly being implemented in universities, schools, and corporate training worldwide, but the ethical consequences of engaging with these technologies remain unexplored. Featuring expert perspectives from inside and outside the AIED scholarly community, this book provides AI researchers, learning scientists, educational technologists, and others with questions, frameworks, guidelines, policies, and regulations to ensure the positive impact of artificial intelligence in learning.Trade Review"Pursuing educational AI along more ethical lines requires considerable time and effort, and a considerable amount of deliberation, debate, dialogue, and consensus building. All of this implies replacing ambitions of ‘scaling-up’ with a commitment to slowing-down. This book takes a great initial step in the right direction."—Neil Selwyn, Distinguished Professor in the Faculty of Education, Monash University, Australia, from his foreword"This book contributes importantly to inform and sensibilize readers towards encoding ethics in the AI used in education, at times challenging the status quo, as well as current pedagogical and technological practices."—Gabriela Ramos, Assistant Director-General for Social and Human Sciences, UNESCO, from her forewordTable of ContentsPart I: Ethics of AI In Education: An Outside Perspective 1. Learning to learn differently 2. Educational research and Artificial Intelligence in education: Identifying ethical challenges 3. AI in education: An opportunity riddled with challenges 4. Student-centered requirements for the ethics of AI in education 5. Pitfalls and pathways for trustworthy Artificial Intelligence in education Part II: Ethics of AI In Education: An Inside Perspective 6. Equity and Artificial Intelligence in education: Will “AIED” amplify or alleviate inequities in education? 7. Algorithmic fairness in education 8. Beyond “Fairness:” Structural (in)justice lenses on AI for education 9. The overlapping ethical imperatives of human teachers and their Artificially Intelligent assistants. 10. Integrating AI ethics across the computing curriculum

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Book SynopsisFocused on the mathematical foundations of social media analysis, Graph-Based Social Media Analysis provides a comprehensive introduction to the use of graph analysis in the study of social and digital media. It addresses an important scientific and technological challenge, namely the confluence of graph analysis and network theory with linear algebra, digital media, machine learning, big data analysis, and signal processing. Supplying an overview of graph-based social media analysis, the book provides readers with a clear understanding of social media structure. It uses graph theory, particularly the algebraic description and analysis of graphs, in social media studies.The book emphasizes the big data aspects of social and digital media. It presents various approaches to storing vast amounts of data online and retrieving that data in real-time. It demystifies complex social media phenomena, such as information diffusion, marketing and recommendationTable of ContentsGraphs in Social and Digital Media. Mathematical Preliminaries: Graphs and Matrices. Algebraic Graph Analysis. Web Search Based on Ranking. Label Propagation and Information Diffusion in Graphs. Graph-Based Pattern Classification and Dimensionality Reduction. Matrix and Tensor Factorization with Recommender System Applications. Multimedia Social Search Based on Hypergraph Learning. Graph Signal Processing in Social Media. Big Data Analytics for Social Networks. Semantic Model Adaptation for Evolving Big Social Data. Big Graph Storage, Processing and Visualization.

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Book SynopsisData science is a multi-disciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured (labeled) and unstructured (unlabeled) data. It is the future of Artificial Intelligence (AI) and a necessity of the future to make things easier and more productive. In simple terms, data science is the discovery of data or uncovering hidden patterns (such as complex behaviors, trends, and inferences) from data. Moreover, Big Data analytics/data analytics are the analysis mechanisms used in data science by data scientists. Several tools, such as Hadoop, R, etc., are used to analyze this large amount of data to predict valuable information and for decision-making. Note that structured data can be easily analyzed by efficient (available) business intelligence tools, while most of the data (80% of data by 2020) is in an unstructured form that requires advanced analytics tools. But while analyzing this data, we face several concerTable of ContentsSection I: Introduction about Data Science and Data Analytics 1. Data Science and Data Analytics: Artificial Intelligence and Machine Learning Integrated Based Approach 2. IoT Analytics/Data Science for IoT 3. A Model to Identify Agriculture Production Using Data Science Techniques 4. Identification and Classification of Paddy Crop Diseases Using Big Data Machine Learning Techniques Section II Algorithms, Methods, and Tools for Data Science and Data Analytics 5. Crop Models and Decision Support Systems Using Machine Learning 6. An Ameliorated Methodology to Predict Diabetes Mellitus Using Random Forest 7. High Dimensionality Dataset Reduction Methodologies in Applied Machine Learning 8. Hybrid Cellular Automata Models for Discrete Dynamical Systems 9. An Efficient Imputation Strategy Based on Adaptive Filter for Large Missing Value Datasets 10. An Analysis of Derivative-Based Optimizers on Deep Neural Network Models Section III: Applications of Data Science and Data Analytics 11. Wheat Rust Disease Detection Using Deep Learning 12. A Novel Data Analytics and Machine Learning Model towards Prediction and Classification of Chronic Obstructive Pulmonary Disease 13. A Novel Multimodal Risk Disease Prediction of Coronavirus by Using Hierarchical LSTM Methods 14. A Tier-based Educational Analytics Framework 15. Breast Invasive Ductal Carcinoma Classification Based on Deep Transfer Learning Models with Histopathology Images 16. Prediction of Acoustic Performance Using Machine Learning Techniques Section IV: Issue and Challenges in Data Science and Data Analytics 17. Feedforward Multi-Layer Perceptron Training by Hybridized Method between Genetic Algorithm and Artificial Bee Colony 18. Algorithmic Trading Using Trend Following Strategy: Evidence from Indian Information Technology Stocks 19. A Novel Data Science Approach for Business and Decision Making for Prediction of Stock Market Movement Using Twitter Data and News Sentiments 20. Churn Prediction in Banking the Sector 21. Machine and Deep Learning Techniques for Internet of Things Based Cloud Systems Section V: Future Research Opportunities towards Data Science and Data Analytics 22. Dialect Identification of the Bengali Language 23. Real-Time Security Using Computer Vision 24. Data Analytics for Detecting DDoS Attacks in Network Traffic 25. Detection of Patterns in Attributed Graph Using Graph Mining 26. Analysis and Prediction of the Update of Mobile Android Version

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Book SynopsisData analytics underpin our modern data-driven economy. This textbook explains the relevance of data analytics at the firm and industry levels, tracing the evolution and key components of the field, and showing how data analytics insights can be leveraged for business results. The first section of the text covers key topics such as data analytics tools, data mining, business intelligence, customer relationship management, and cybersecurity. The chapters then take an industry focus, exploring how data analytics can be used in particular settings to strengthen business decision-making. A range of sectors are examined, including financial services, accounting, marketing, sport, health care, retail, transport, and education. With industry case studies, clear definitions of terminology, and no background knowledge required, this text supports students in gaining a solid understanding of data analytics and its practical applications. PowerPoint slides, a test bank of quesTable of Contents1 History and Evolution of Data Analytics 2 Data Mining and Analytics 3 Data Analytics Tools 4 Business Analytics and Intelligence 5 Customer Relationship Analytics, Cloud Computing, Blockchain, and Cognitive Computing 6 Cybersecurity and Data Analytics 7 Data Analytics and the Retail Industry 8 Data Analytics in the Financial Services Industry 9 Data Analytics in the Sports Industry 10 Data Analytics in the Accounting Industry 11 Data Analytics in the Medical Industry 12 Data Analytics in the Manufacturing Industry 13 Data Analytics in the Marketing Industry 14 Data Analytics in the Transportation Industry 15 Data Analytics in Education

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Book SynopsisThere are many webinars and training courses on Data Analytics for Internal Auditors, but no handbook written from the practitionerâs viewpoint covering not only the need and the theory, but a practical hands-on approach to conducting Data Analytics. The spread of IT systems makes it necessary that auditors as well as management have the ability to examine high volumes of data and transactions to determine patterns and trends. The increasing need to continuously monitor and audit IT systems has created an imperative for the effective use of appropriate data mining tools. This book takes an auditor from a zero base to an ability to professionally analyze corporate data seeking anomalies.Table of ContentsIntroduction to Data Analysis. Understanding Sampling. Judgmental vs Statistical Sampling. Probability theory in Data Analysis. Types of Evidence. Population Analysis. Correlations and Regressions. Conducting the Audit. Obtaining Information from IT Systems for Analysis. Use of Computer Assisted Audit Techniques. Analysis of Big Data. Results Analysis and Validation. Root Cause Analysis. Data Analysis and Continuous Monitoring. Continuous Auditing. Financial Analysis. Excel and Data Analysis. ACL and Data Analysis. IDEA and Data Analysis. Analysis Reporting.

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Book SynopsisThe world of cybersecurity and the landscape that it possesses is changing on a dynamic basis. It seems like that hardly one threat vector is launched, new variants of it are already on the way. IT Security teams in businesses and corporations are struggling daily to fight off any cyberthreats that they are experiencing. On top of this, they are also asked by their CIO or CISO to model what future Cyberattacks could potentially look like, and ways as to how the lines of defenses can be further enhanced.IT Security teams are overburdened and are struggling to find ways in order to keep up with what they are being asked to do. Trying to model the cyberthreat landscape is a very laborious process, because it takes a lot of time to analyze datasets from many intelligence feeds. What can be done to accomplish this Herculean task? The answer lies in Artificial Intelligence (AI). With AI, an IT Security team can model what the future Cyberthreat landscape could potentiTable of ContentsChapter 1. Artificial Intelligence. Chapter 2. Machine Learning. Chapter 3. The high Level Overview into Neural Networks. Chapter 4. Typical Applications for Computer Vision. Chapter 5. Conclusion.

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Book SynopsisThis book is intended to serve as a bridge in statistics for graduates and business practitioners interested in using their skills in the area of data science and analytics as well as statistical analysis in general. On the one hand, the book is intended to be a refresher for readers who have taken some courses in statistics, but who have not necessarily used it in their day-to-day work. On the other hand, the material can be suitable for readers interested in the subject as a first encounter with statistical work in Python. Statistics and Data Visualisation with Python aims to build statistical knowledge from the ground up by enabling the reader to understand the ideas behind inferential statistics and begin to formulate hypotheses that form the foundations for the applications and algorithms in statistical analysis, business analytics, machine learning, and applied machine learning. This book begins with the basics of programming in Python and data analysTable of Contents1. Data, Stats and Stories - An Introduction 2. Python Programming Primer 3. Snakes, Bears & Other Numerical Beasts: NumPy, SciPy & Pandas 4. The Measure of All Things - Statistics 5. Definitely Maybe: Probability and Distributions 6. Alluring Arguments and Ugly Facts - Statistical Modelling and Hypothesis Testing 7. Delightful Details - Data Visualisation 8. Dazzling Data Designs - Creating Charts A. Variance: Population v Sample B. Sum of First n Integers C. Sum of Squares of the First n Integers D. The Binomial Coefficient E. The Hypergeometric Distribution F. The Poisson Distribution G. The Normal Distribution H. Skewness and Kurtosis I. Kruskal-Wallis Test - No Ties

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Book SynopsisThe emerging field of Data Science has had a large impact on science and society. This book explores how one distinguishing feature of Data Science its focus on data collected from social and environmental contexts within which learners often find themselves deeply embedded suggests serious implications for learning and education.Drawing from theories of learning and identity development in the learning sciences, this volume investigates the impacts of these complex relationships on how learners think about, use, and share data, including their understandings of data in light of history, race, geography, and politics. More than just using real world examples' to motivate students to work with data, this book demonstrates how learners' relationships to data shape how they approach those data with agency, as part of their social and cultural lives. Together, the contributions offer a vision of how the learning sciences can contribute to a more expansive, socially awareTable of Contents1. Introduction: Situating Data Science—Exploring How Relationships to Data Shape Learning 2. At Home with Data: Family Engagements with Data Involved in Type 1 Diabetes Management 3. Examining Spontaneous Perspective Taking and Fluid Self-to-Data Relationships in Informal Open-Ended Data Exploration 4. Learning at the Intersection of Self and Society: The Family Geobiography as a Context for Data Science Education 5. Authoring Data Stories in a Media Makerspace: Adolescents Developing Critical Data Literacies 6. From Data Collectors to Data Producers: Shifting Students’ Relationship to Data, Lisa Hardy 7. Scripts and Counterscripts in Community-Based Data Science: Participatory Digital Mapping and the Pursuit of a Third Space 8. Learning to Reason with Data: How Did We Get Here and What Do We Know? 9. Educating Data Scientists and Data Literate Citizens for a New Generation of Data

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Book SynopsisPresent energy systems are undergoing a radical transformation, driven by the urgent need to address the climate change crisis. At the same time, we are witnessing the sharp growth of energy data and a revolution of advanced technologies, with artificial intelligence (AI) and Blockchain emerging as two of the most transformative technologies of our time. The convergence of these two technologies has the potential to create a paradigm shift in the energy sector, enabling the development of smart energy systems that are more resilient, efficient, and sustainable.This book situates itself at the forefront of this paradigm shift, providing a timely and comprehensive guide to AI and Blockchain technologies in the energy system. Moving from an introduction to the basic concepts of smart energy systems, this book proceeds to examine the key challenges facing the energy system, and how AI and Blockchain can be used to address these challenges. Research examples are presented to showcTable of ContentsList of FiguresList of TablesForewordPrefaceAuthor BiosContributorsSection I Fundamental TheoriesChapter 1 Smart Energy SystemsChapter 2 Theories of Artificial IntelligenceChapter 3 Theories of Blockchain TechnologiesSection II Applications in Smart Energy SystemsChapter 4 Reforms in Energy Systems: Prosumers Era and Future Low Carbon Energy SystemsChapter 5 Application of Artificial Intelligence for Energy SystemsChapter 6 Implementation of Blockchain in Local Energy MarketsChapter 7 Cyber Physical System Modeling for Energy InternetSection III Testbeds for Smart Energy SystemsChapter 8 Developing Testbeds for Smart Energy Systems

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Book SynopsisNew computational design tools have evolved rapidly and been increasingly applied in the field of design in recent years, complimenting and even replacing the traditional design media and approaches. Design as both the process and product are changing due to the emergence and adoption of these new technologies. Understanding and assessing the impact of these new computational design environments on design and designers is important for advancing design in the contemporary context. Do these new computational environments support or hinder design creativity? How do those tools facilitate designers' thinking? Such knowledge is also important for the future development of design technologies. Research shows that design is never a mysterious non-understandable process, for example, one general view is that design process shares a common analysis-synthesis-evaluation model, during which designers interact between design problem and solution spaces. Understanding designers' thinking in difTable of ContentsIntroduction. Emergent technologies in computational design. Understanding design cognition in computational and generative design. Cognitive impacts and computational design environments. Conclusion.

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Book SynopsisData Science for Sensory and Consumer Scientists is a comprehensive textbook that provides a practical guide to using data science in the field of sensory and consumer science through real-world applications. It covers key topics including data manipulation, preparation, visualization, and analysis, as well as automated reporting, machine learning, text analysis, and dashboard creation. Written by leading experts in the field, this book is an essential resource for anyone looking to master the tools and techniques of data science and apply them to the study of consumer behavior and sensory-led product development. Whether you are a seasoned professional or a student just starting out, this book is the ideal guide to using data science to drive insights and inform decision-making in the sensory and consumer sciences.Key Features:â Elucidation of data scientific workflow. â Introduction to reproducible research. â In-depth coverage of data-scientifTable of Contents1. Bienvenue! 2. Getting Started 3. Why Data Science? 4. Data Manipulation 5. Data Visualization 6. Automated Reporting 7. Example Project: The Biscuit Study 8. Data Collection 9. Data Preparation 10. Data Analysis 11. Value Delivery 12. Machine Learning 13. Text Analysis 14. Dashboards 15. Conclusion and Next Steps

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Book SynopsisAPCOM is a peer-reviewed forum for industrial and research communities working in the mineral industry to share expertise on the application of computer and operations research technology. Recognized since the 1960s as the world's premier conference in the field, APCOM features an impressive range of topics from geostatistics to data warehousing. APCOM 2005 builds on this reputation, showcasing the latest industrial applications and emerging technologies, focusing particularly on mobilizing the inherent value in largely under-used data and information systems, and how these data systems cab be analyzed for real-time or process-based improvements.

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Book SynopsisData warehousing is one of the hottest business topics, and there's more to understanding data warehousing technologies than you might think. Find out the basics of data warehousing and how it facilitates data mining and business intelligence with Data Warehousing For Dummies, 2nd Edition.Table of ContentsIntroduction 1 Part I: The Data Warehouse: Home for Your Data Assets 7 Chapter 1: What’s in a Data Warehouse? 9 Chapter 2: What Should You Expect from Your Data Warehouse? 25 Chapter 3: Have It Your Way: The Structure of a Data Warehouse 37 Chapter 4: Data Marts: Your Retail Data Outlet 59 Part II: Data Warehousing Technology 71 Chapter 5: Relational Databases and Data Warehousing 73 Chapter 6: Specialty Databases and Data Warehousing 85 Chapter 7: Stuck in the Middle with You: Data Warehousing Middleware 95 Part III: Business Intelligence and Data Warehousing 113 Chapter 8: An Intelligent Look at Business Intelligence 115 Chapter 9: Simple Database Querying and Reporting 125 Chapter 10: Business Analysis (OLAP) 135 Chapter 11: Data Mining: Hi-Ho, Hi-Ho, It’s Off to Mine We Go 149 Chapter 12: Dashboards and Scorecards 155 Part IV: Data Warehousing Projects: How to Do Them Right 163 Chapter 13: Data Warehousing and Other IT Projects: The Same but Different 165 Chapter 14: Building a Winning Data Warehousing Project Team 179 Chapter 15: You Need What? When? — Capturing Requirements 193 Chapter 16: Analyzing Data Sources 203 Chapter 17: Delivering the Goods 213 Chapter 18: User Testing, Feedback, and Acceptance 225 Part V: Data Warehousing: The Big Picture 231 Chapter 19: The Information Value Chain: Connecting Internal and External Data 233 Chapter 20: Data Warehousing Driving Quality and Integration 247 Chapter 21: The View from the Executive Boardroom 263 Chapter 22: Existing Sort-of Data Warehouses: Upgrade or Replace? 271 Chapter 23: Surviving in the Computer Industry (and Handling Vendors) 281 Chapter 24: Working with Data Warehousing Consultants 291 Part VI: Data Warehousing in the Not-Too-Distant Future 297 Chapter 25: Expanding Your Data Warehouse with Unstructured Data 299 Chapter 26: Agreeing to Disagree about Semantics 305 Chapter 27: Collaborative Business Intelligence 311 Part VII: The Part of Tens 317 Chapter 28: Ten Questions to Consider When You’re Selecting User Tools 319 Chapter 29: Ten Secrets to Managing Your Project Successfully 325 Chapter 30: Ten Sources of Up-to-Date Information about Data Warehousing 331 Chapter 31: Ten Mandatory Skills for a Data Warehousing Consultant 335 Chapter 32: Ten Signs of a Data Warehousing Project in Trouble 339 Chapter 33: Ten Signs of a Successful Data Warehousing Project 343 Chapter 34: Ten Subject Areas to Cover with Product Vendors 347 Index 351

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Book SynopsisLike many other organizing paradigms, smart data strategy isrevolutionary and essential to enterprise performance. SmartData explores smart data strategy to enhance enterpriseperformance. Smart Data provides valuable tools in business,like skills for better enterprise decision-making, enterpriseperformance, and agility towards change.Table of ContentsForeword. Preface. Acknowledgments. Introduction: A Comprehensive Overview. Predictive Management. IDEF Lexicon for Executives. Organization of This Book. Smart Data in Three Dimensions. Business Rule. Case Study: IT Capital Budgeting Using a Knapsack Problem. Case Study: Better Decision Making: Field Testing, Evaluation and Validation of a Web-Based MedWatch Decision Support System (MWDSS). Engineering an Ubiquitous Strategy for Catalyzing Enterprise Performance Optimization. What Smart Data Provides. References. 1 Context: The Case and Place for Smart Data Strategy. 1.1 Value of Data to the Enterprise. 1.2 Enterprise Performance Versus Enterprise Integration. 1.3 Current Problems and Deficiencies from Poor Data Strategy. 1.4 New Technologies. 1.5 Breaking from Tradition with Improved Results. References. 2 Elements: Smart Data and Smart Data Strategy. 2.1 Performance Outcomes and Attributes. 2.2 Policy and Business Rules. 2.3 Expectations: Managerial and Technical. 2.4 Capacity for Change and Improvement. 2.5 Iteration Versus Big Bang. References. 3 Barriers: Overcoming Hurdles and Reaching a New Performance Trajectory. 3.1 Barriers. 3.2 Overcoming Barriers. 3.3 Top–Down Strategy. 3.4 Balance of Consequences and Reinforcement. 3.5 Collaboration. 3.6 Enterprise Performance Optimization Process. 3.7 Enterprise Performance Optimization Architecture. 3.8 Scoping, Scheduling, Budgeting, and Project and Program Management. References. 4 Visionary Ideas: Technical Enablement. 4.1 Today’s Possibilities. 4.2 Calibrating Executive Expectations. 4.3 Five Years from Now. 4.4 Ten Years From Now. References. 5. CEO’s Smart Data Handbook. 5.1 Strategy. 5.2 Policy. 5.3 Organization. 5.4 Actions. 5.5 Timing. 5.6 Funding and Costing Variables. 5.7 Outcomes and Measurements. References. Index. Wiley Series in Systems Engineering and Management.

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Book SynopsisThis book demonstrates the role of coding in communication and data storage systems design, illustrating the correct use of codes and the selection of the right code parameters. Relevant decoding techniques and their implementation are discussed in detail, while emphasizing the fundamental concepts of coding theory with minimal mathematical tools.Table of ContentsThe Principles of Coding in Digital Communications. Convolutional Codes. Linear Block Codes. Cyclic Codes. Finite Field Arithmetic. BCH Codes. Reed Solomon Codes. Performance Calculations for Block Codes. Multistage Coding. Iterative Decoding. Index.

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Book SynopsisData warehousing is split into two camps: Ralph Kimball leads those who champion a technique called dimensional modeling; Bill Inmon leads the rest who believe in using relational modeling techniques.Table of ContentsAcknowledgments. About the Authors. PART ONE: CONCEPTS. Chapter 1. Introduction. Chapter 2. Fundamental Relational Concepts. PART TWO: MODEL DEVELOPMENT. Chapter 3. Understanding the Business Model. Chapter 4. Developing the Model. Chapter 5. Creating and Maintaining Keys. Chapter 6. Modeling the Calendar. Chapter 7. Modeling Hierarchies. Chapter 8. Modeling Transactions. Chapter 9. Data Warehouse Optimization. PART THREE: OPERATION AND MANAGEMENT. Chapter 10. Accommodating Business Change. Chapter 11. Maintaining the Models. Chapter 12. Deploying the Relational Solution. Chapter 13. Comparison of Data Warehouse Methodologies. Glossary. Recommended Reading. Index.

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Book SynopsisThe many and powerful data structures for representing information physically (in contrast to a database management system that represents information with logical structures) are introduced by this book.Table of ContentsPreface xi Part One Primary File Organizations 25 Part Two Bit Level And Related Structures 127 Part Three Tree Structures 197 Part Four File Sorting 337 Answers to Selected Exercises 375 Index 393

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Book SynopsisThis 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! Individual tabs for each letter—no more tabs cramming multiple letters into the same space! Perfect for faster lookups and better organization. Removable sticker to go incognito! Don''t want text on the cover sharing that it’s a password book? Peel it off! Bonus security tips to encourage maximized online safety. What to do (and what not to do) to stay ahead of scammers. Large trim size for extra space to record over 400 accounts, including important notes, password changes, and non-traditional records such as crypto logins.

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Book SynopsisJulie C. Meloni is a technical consultant who has been developing web-based applications since the Web first saw the light of day. She has authored numerous books and articles on web-based programming and scripting languages and database topics, and you can find translations of her work in 18 different languages.  Table of ContentsPart I: Web Application Basics CHAPTER 1: Understanding How the Web Works A Brief History of HTML and the World Wide Web Creating Web Content Understanding Web Content Delivery Selecting a Web Hosting Provider Testing with Multiple Web Browsers Creating a Sample File Using FTP to Transfer Files Understanding Where to Place Files on the Web Server CHAPTER 2: Structuring HTML and Using Cascading Style Sheets Getting Started with a Simple Web Page HTML Tags Every Web Page Must Have Using Hyperlinks in Web Pages Organizing a Page with Paragraphs and Line Breaks Organizing Your Content with Headings Understanding Semantic Elements How CSS Works A Basic Style Sheet A CSS Style Primer Using Style Classes Using Style IDs Internal Style Sheets and Inline Styles CHAPTER 3: Understanding the CSS Box Model and Positioning The CSS Box Model The Whole Scoop on Positioning Controlling the Way Things Stack Up Managing the Flow of Text Understanding Fixed Layouts Understanding Fluid Layouts Creating a Fixed/Fluid Hybrid Layout Considering a Responsive Web Design CHAPTER 4: Introducing JavaScript Learning Web Scripting Basics How JavaScript Fits into a Web Page Exploring JavaScript’s Capabilities Basic JavaScript Language Concepts JavaScript Syntax Rules Using Comments Best Practices for JavaScript Understanding JSON Using the JavaScript Console to Debug JavaScript CHAPTER 5: Introducing PHP How PHP Works with a Web Server The Basics of PHP Scripts Code Blocks and Browser Output Part II: Getting Started with Dynamic Websites CHAPTER 6: Understanding Dynamic Websites and HTML5 Applications Refresher on the Different Types of Scripting Displaying Random Content on the Client Side Understanding the Document Object Model Using window Objects Working with the document Object Accessing Browser History Working with the location Object More About the DOM Structure Working with DOM Nodes Creating Positionable Elements (Layers) Hiding and Showing Objects Modifying Text Within a Page Adding Text to a Page Changing Images Based on User Interaction Thinking Ahead to Developing HTML5 Applications CHAPTER 7: JavaScript Fundamentals: Variables, Strings, and Arrays Using Variables Understanding Expressions and Operators Data Types in JavaScript Converting Between Data Types Using String Objects Working with Substrings Using Numeric Arrays Using String Arrays Sorting a Numeric Array CHAPTER 8: JavaScript Fundamentals: Functions, Objects, and Flow Control Using Functions Introducing Objects Using Objects to Simplify Scripting Extending Built-in Objects Using the Math Object Working with Math Methods Working with Dates The if Statement Using Shorthand Conditional Expressions Testing Multiple Conditions with if and else Using Multiple Conditions with switch Using for Loops Using while Loops Using do…while Loops Working with Loops Looping Through Object Properties CHAPTER 9: Understanding JavaScript Event Handling Understanding Event Handlers Using Mouse Events Using Keyboard Events Using the load and unload Events CHAPTER 10: The Basics of Using jQuery Using Third-Party JavaScript Libraries jQuery Arrives on the Scene Preparing to Use jQuery Becoming Familiar with the $().ready Handler Selecting DOM and CSS Content Manipulating HTML Content Putting the Pieces Together to Create a jQuery Animation Handling Events with jQuery Part III: Taking Your Web Applications to the Next Level CHAPTER 11: AJAX: Remote Scripting Introducing AJAX Using XMLHttpRequest Creating a Simple AJAX Library Creating an AJAX Quiz Using the Library Debugging AJAX-Based Applications Using jQuery’s Built-in Functions for AJAX CHAPTER 12: PHP Fundamentals: Variables, Strings, and Arrays Variables Data Types Using Expressions and Operators Constants Understanding Arrays Creating Arrays Some Array-Related Constructs and Functions CHAPTER 13: PHP Fundamentals: Functions, Objects, and Flow Control Calling Functions Defining a Function Returning Values from User-Defined Functions Understanding Variable Scope Saving State Between Function Calls with the static Statement More About Arguments Testing for the Existence of a Function Creating an Object Object Inheritance Switching Flow Implementing Loops CHAPTER 14: Working with Cookies and User Sessions Introducing Cookies Setting a Cookie Deleting a Cookie Overview of Server-Side Sessions Working with Session Variables Destroying Sessions and Unsetting Session Variables Using Sessions in an Environment with Registered Users CHAPTER 15: Working with Web-Based Forms How HTML Forms Work Creating a Form Accepting Text Input Naming Each Piece of Form Data Labeling Each Piece of Form Data Grouping Form Elements Exploring Form Input Controls Using HTML5 Form Validation Submitting Form Data Accessing Form Elements with JavaScript Accessing Form Elements with PHP Using Hidden Fields to Save State in Dynamic Forms Sending Mail on Form Submission Part IV: Integrating a Database into Your Applications CHAPTER 16: Understanding the Database Design Process The Importance of Good Database Design Types of Table Relationships Understanding Normalization Following the Design Process CHAPTER 17: Learning Basic SQL Commands Learning the MySQL Data Types Learning the Table-Creation Syntax Using the INSERT Statement Using the SELECT Statement Using WHERE in Your Queries Selecting from Multiple Tables Using the UPDATE Statement to Modify Records Using the REPLACE Statement Using the DELETE Statement Frequently Used String Functions in MySQL Using Date and Time Functions in MySQL CHAPTER 18: Interacting with MySQL Using PHP MySQL or MySQLi? Connecting to MySQL with PHP Working with MySQL Data Part V: Getting Started with Application Development CHAPTER 19: Creating a Simple Discussion Forum Designing the Database Tables Creating an Include File for Common Functions Creating the Input Forms and Scripts Displaying the Topic List Displaying the Posts in a Topic Adding Posts to a Topic Modifying the Forum Display with JavaScript CHAPTER 20: Creating an Online Storefront Planning and Creating the Database Tables Displaying Categories of Items Displaying Items Using JavaScript with an Online Storefront CHAPTER 21: Creating a Simple Calendar Building a Simple Display Calendar Creating the Calendar in JavaScript CHAPTER 22: Managing Web Applications Understanding Some Best Practices in Web Application Development Writing Maintainable Code Implementing Version Control in Your Work Understanding the Value and Use of Code Frameworks Appendixes APPENDIX A: Installation QuickStart Guide with XAMPP APPENDIX B: Installing and Configuring MySQL APPENDIX C: Installing and Configuring Apache APPENDIX D: Installing and Configuring PHP

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Book SynopsisTrade Review"[A] penetrating study of missing (‘dark’) data and its impacts on decisions—skewing stats, enabling fraud, embedding inequity and triggering preventable catastrophes. Advocating ‘data science judo,’ Hand offers expert training, from recognizing when facts are being cherry-picked to designing randomized trials. A book illuminating shadowed corners in science, medicine and policy."---Barbara Kiser, Nature"A tour de force. . . . Hand is a good and able guide to take us through the many aspects of dark data that are potentially skewing our understanding of real world observations and potential scientific breakthroughs. He writes in an accessible and understandable way too."---Simon Cocking, Irish Tech News"Well-written and accessible."---Tim Harford, Undercover Economist"You need to read [Dark Data], and be convinced by David’s reasoning and his examples of cases in which unseen or unreported data play a critical and sometimes even a fatal role. You are likely to walk away with the feeling that the term dark data is indeed a very effective one to arouse both curiosity and suspicion, mixed with happiness that finally a great term was coined by a statistician—and sadness that the statistician is not you."---Xiao-Li Meng, IMS Bulletin"An exploration of a major problem in data analysis with an attempt of classification, analysing causes, mechanisms, and to some extent also suggest mitigations."---Adhemar Bultheel, European Mathematical Society"An excellent guide to the many reasons for caution in interpreting data."---Diane Coyle, Enlightened Economist

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Book SynopsisTrade Review"[A] penetrating study of missing (‘dark’) data and its impacts on decisions—skewing stats, enabling fraud, embedding inequity and triggering preventable catastrophes. Advocating ‘data science judo,’ Hand offers expert training, from recognizing when facts are being cherry-picked to designing randomized trials. A book illuminating shadowed corners in science, medicine and policy."---Barbara Kiser, Nature"A tour de force. . . . Hand is a good and able guide to take us through the many aspects of dark data that are potentially skewing our understanding of real world observations and potential scientific breakthroughs. He writes in an accessible and understandable way too."---Simon Cocking, Irish Tech News"Well-written and accessible."---Tim Harford, Undercover Economist"You need to read [Dark Data], and be convinced by David’s reasoning and his examples of cases in which unseen or unreported data play a critical and sometimes even a fatal role. You are likely to walk away with the feeling that the term dark data is indeed a very effective one to arouse both curiosity and suspicion, mixed with happiness that finally a great term was coined by a statistician—and sadness that the statistician is not you."---Xiao-Li Meng, IMS Bulletin"An exploration of a major problem in data analysis with an attempt of classification, analysing causes, mechanisms, and to some extent also suggest mitigations."---Adhemar Bultheel, European Mathematical Society"An excellent guide to the many reasons for caution in interpreting data."---Diane Coyle, Enlightened Economist

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Book SynopsisThis comprehensive guide offers a detailed treatment of the analysis, design, simulation and testing of the full range of today''s leading delta-sigma data converters. Written by professionals experienced in all practical aspects of delta-sigma modulator design, Delta-Sigma Data Converters provides comprehensive coverage of low and high-order single-bit, bandpass, continuous-time, multi-stage modulators as well as advanced topics, including idle-channel tones, stability, decimation and interpolation filter design, and simulation.Table of ContentsPreface. Introduction. An Overview of Basic Concepts (J. Candy). Quantization Noise in DeltaSigma A/D Converters (R. Gray). Quantization Errors and Dithering in DeltaSigma Modulators (S. Norsworthy). Stability Theory for DeltaSigma Modulators (R. Adams & R. Schreier). The Design of High-Order Single-Bit DeltaSigma ADCs (R. Adams). The Design of Cascaded DeltaSigma ADCs (M. Rebeschini). High-Speed Cascaded DeltaSigma ADCs (B. Brandt). Delta-Sigma ADCs with Multibit Internal Converters (R. Carley, et al.). The Design of Bandpass DeltaSigma ADCs (S. Jantzi, et al.). Architectures for DeltaSigma DACs (G. Temes, et al.). Analog Circuit Design for DeltaSigma ADCs (B. Brandt, et al.). Analog Circuit Design for DeltaSigma DACs (M. Rebeschini & P. Ferguson). Decimation and Interpolation for DeltaSigma Conversion (S. Norsworthy & R. Crochiere). CAD for the Analysis and Design of DeltaSigma Converters (C. Wolff, et al.). Index. About the Editors.

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Book SynopsisTable of ContentsPreface. Introduction to Data Conversion and Processing. Basic Sampling Circuits. Sample-and-Hold Architectures. Basic Principles of Digital-to-Analog Conversion. Digital-to-Analog Converter Architectures. Analog-to-Digital Converter Architectures. Building Blocks Data Conversion Systems. Precision Techniques. Testing and Characterization. Index.

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Book SynopsisAs Web-based systems and e-commerce carry businesses into the 21st century, databases are becoming workhorses that shoulder each and every online transaction. For organizations to have effective 24/7 Web operations, they need powerhouse databases that deliver at peak performance-all the time. High Performance Web Databases: Design, Development, and Deployment arms you with every essential technique from design and modeling to advanced topics such as data conversion, performance tuning, Web access and interfacing legacy systems, and securityTable of ContentsDatabase Planning and Getting Started. Information Gathering and Analysis. Managing Business Rules. Performance Modeling Methods. Performance Design and Development. Database Integrity and Quality. Distributed Databases, Portability, and Interoperability. Database Integration with the Internet and the Web. Data Migration, Conversion, and Legacy Applications. Performance Tuning. Data Administration and Operations. Data Base Security.

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Book SynopsisThe explosion of Web-based data has created a demand among executives and technologists for methods to identify, gather, analyze, and utilize data that may be of value to corporations and organizations. The emergence of data mining, and the larger field of Web mining, has businesses lost within a confusing maze of mechanisms and strategies for obtaining and managing crucial intelligence.Web Data Mining and Applications in Business Intelligence and Counter-Terrorism responds by presenting a clear and comprehensive overview of Web mining, with emphasis on CRM and, for the first time, security and counter-terrorism applications. The tools and methods of Web mining are revealed in an easy-to-understand style, emphasizing the importance of practical, hands-on experience in the creation of successful e-business solutions.The author, a program director for Data and Applications Security at the National Science Foundations, details how both opportunities and dangers on the WTable of ContentsIntroduction. SUPPORTING TECHNOLOGIES FOR WEB DATA MINING. The World Wide Web and E-Commerce. Data Mining. Core Data Mining Technologies. Web Database Management. Information Retrieval Systems. Information Management Technologies. The Semantic Web. WEB DATA MINING TECHNIQUES, TOOLS, AND TRENDS. Data Mining and the Web. Processes and Techniques for Web Data Mining. Mining the Databases on the Web. Information Retrieval and Web Data Mining. Information Management and Web Data Mining. Semantic Web Mining. Mining Usage Patterns and Structure on the Web. Prototypes, Products, and Standards for Web Data Mining. Some Applications for Web Mining. WEB DATA MINING APPLICATIONS FOR COUNTER-TERRORISM. Some Information on Terrorism, Security Threats, and Protection Measures. Web Data Mining for Counter-Terrorism. Mining the Web Databases for Counter-Terrorism. Information Retrieval and Web Mining for Counter-Terrorism. Information Management and Web Mining for Counter-Terrorism. Semantic Web Mining for Counter-Terrorism. Web Usage and Structure Mining for Counter-Terrorism. National Security, Privacy, Civil Liberties, and Web Mining. Revisiting Security Threats with Respect to Web Mining. E-Commerce, Business Intelligence, and Counter-Terrorism. Summary and Directions. Appendices.

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Book SynopsisRecommender systems are ubiquitous in modern life and are one of the main monetization channels for Internet technology giants. This book helps graduate students, researchers and practitioners to get to grips with this cutting-edge field and build the thorough understanding and practical skills needed to progress in the area. It not only introduces the applications of deep learning and generative AI for recommendation models, but also focuses on the industry architecture of the recommender systems. The authors include a detailed discussion of the implementation solutions used by companies such as YouTube, Alibaba, Airbnb and Netflix, as well as the related machine learning framework including model serving, model training, feature storage and data stream processing.

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Book SynopsisHybrid Intelligent Systems for Information Retrieval covers three areas along with the introduction to Intelligent IR, i.e., Optimal Information Retrieval Using Evolutionary Approaches, Semantic Search for Web Information Retrieval, and Natural Language Processing for Information Retrieval. Talks about the design, implementation, and performance issues of the hybrid intelligent information retrieval system in one book Gives a clear insight into challenges and issues in designing a hybrid information retrieval system Includes case studies on structured and unstructured data for hybrid intelligent information retrieval Provides research directions for the design and development of intelligent search enginesThis book is aimed primarily at graduates and researchers in the information retrieval domain. Table of Contents1. Introduction. 2. Matching Functions. 3. Information Retrieval Models. 4. Hybrid Swarm Intelligence Approaches for Optimal Information Retrieval. 5. Information Retrieval and Semantic Search. 6. Ontology Creation using Clustering Technique. 7. Natural Language Processing for Information Retrieval. 8. Deep learning (DL) for Information retrieval (IR). 9. Application of Ontology in Domain Specific Information Retrieval: A case study. 10. Applications of Natural Language Processing and Information Retrieval.

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Book SynopsisIn what is certain to be a seminal work on metadata, John Horodyski masterfully affirms the value of metadata while providing practical examples of its role in our personal and professional lives. He does more than tell us that metadata mattersâhe vividly illustrates why it matters. âPatricia C. Franks, PhD, CA, CRM, IGP, CIGO, FAI, President, NAGARA, Professor Emerita, San Josà State University, USAIf data is the language upon which our modern society will be built, then metadata will be its grammar, the construction of its meaning, the building for its content, and the ability to understand what data can be for us all. We are just starting to bring change into the management of the data that connects our experiences.Metadata Matters explains how metadata is the foundation of digital strategy. If digital assets are to be discovered, they want to be found. The path to good metadata design begins with the realization that digital assets need to be identified, organized, and made available for discovery. This book explains how metadata will help ensure that an organization is building the right system for the right users at the right time. Metadata matters and is the best chance for a return on investment on digital assets and is also a line of defense against lost opportunities. It matters to the digital experience of users. It helps organizations ensure that users can identify, discover, and experience their brands in the ways organizations intend. It is a necessary defense, which this book shows how to build.Trade ReviewDigital technology has become our externalized nervous system. Our mental activities are closely linked to the quality and organization of the data we produce and consult on a daily basis. For our work to be effective and well-coordinated, it is necessary that our metadata system be fit for purpose and regularly updated. John Horodyski's book Metadata Matters is an impassioned plea for intelligent metadata management. It is a must read for Chief Information Officers, Chief Data Officers and anyone concerned with sound knowledge management.—Pierre Lévy, PhD, Fellow of the Royal Society of Canada, CEO of INTLEKT Metadata Inc.Metadata guru John manages to pull off the difficult task of writing a book that’s not only much needed and useful but also highly engaging. In lucid prose, using rich examples from our personal and professional lives, John makes a strong case for metadata and its central role for your digital strategy. You’ll learn how metadata can increase the return on investment of marketing and content systems. There’s practical guidance, best practices and more to put all this knowledge into practice as well. A must read for marketing, content, and digital professionals.—Kashyap Kompella, CEO, RPA2AI ResearchMetadata is about making information accessible, and John Horodyski has made the subject of metadata accessible to all in this very readable book that not only teaches principles of metadata but also increases our awareness and appreciation of metadata. Drawing on his rich experience as consultant, Horodyski thoroughly addresses metadata in all fields and industries. This book is not limited to those who plan to manage their metadata but is for anyone who wonders whether they need to or whether they should even care.—Heather Hedden, Author, The Accidental TaxonomistFrom helping us make our everyday choices to making our machines smart, metadata powers our world. John's book is a love song sung to the stuff that's about stuff, full of stories that will entertain you and examples that will help you understand, craft, and choose metadata that indeed matters.—Louis Rosenfeld, co-author of Information Architecture and publisher and founder, Rosenfeld MediaIn what is certain to be a seminal work on metadata, John Horodyski masterfully affirms the value of metadata while providing practical examples of its role in our personal and professional lives. He does more than tell us that metadata matters—he vividly illustrates why it matters. As a Digital Asset Management (DAM) and Metadata Expert, Horodyski is uniquely aware that metadata itself is an asset that is needed to provide context so that other information can be located, retrieved, managed, and interpreted. He distinguishes among different types of metadata—descriptive, administrative, and structural—and discusses the usefulness of metadata standards to provide consistency which can facilitate findability, migration, and interoperability, as well as result in cost savings. Whether new to the concept of metadata or veteran metadata specialists, by the end of the book, all readers will be metadata champions!—Patricia C. Franks, PhD, CA, CRM, IGP, CIGO, FAI, President, NAGARA, Professor Emerita, San José State University, USAFinally, an easy-to-read handbook that explores how metadata can inspire us to unlock the potential of the information we create. In Metadata Matters, John Horodyski delivers practical, real-world examples of how putting metadata to work can help us develop differentiating capabilities that would be otherwise difficult or impossible to enjoy. Do yourself a favor. Get this book and devour every chapter. You'll discover how effectively using metadata can dramatically advance the role of content across your enterprise.—Scott Abel, The Content WranglerTable of Contents1 In Praise of Metadata: Lost and Found 2 Metadata: Some Assembly Required 3 Taxonomical Tenets 4 Definitions 5 Adjectivity: Language, Meaning, and Optimization for Content Curation and Discovery 6 Metadata Is a Human Endeavor 7 Governance 8 Metadata and Workflow 9 What Do Good Metadata, UX, and Search Look Like? 10 Please Feed the Robots Good Data 11 Building a Metadata Strategy 12 Metadata Maturity 13 Metadata Is a Love Note to the Future . . . Appendix: Metadata Manifesto Glossary Index

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Book SynopsisThe process of developing predictive models includes many stages. Most resources focus on the modeling algorithms but neglect other critical aspects of the modeling process. This book describes techniques for finding the best representations of predictors for modeling and for nding the best subset of predictors for improving model performance. A variety of example data sets are used to illustrate the techniques along with R programs for reproducing the results. Trade Review"The book is timely and needed. The interest in all things 'data science' morphed into everybody pretending to do, or know, Machine Learning. Kuhn and Johnson happen to actually know this—as evidenced by their earlier and still-popular tome entitled ‘Applied Predictive Modeling.’ The proposed ‘Feature Engineering and Selection’ builds on this and extends it. I expect it to become as popular with a wide reach as both a textbook, self-study material, and reference."~Dirk Eddelbuettel, University of Illinois at Urbana-Champaign"As a reviewer, it has been exciting and edifying to see this book develop into what is likely to become one of the foundational works on feature engineering. It is launching propitiously on the current tide of interest in both interpretable models and AutoML."~Robert Horton, Microsoft"In recent years, the statistics literature has featured new developments in modeling and predictive analytics. Approaches such as cross-validation and statistical/machine learning techniques have become widespread. The author's previous book ("Applied Predictive Modeling", APM) provided a wide-ranging introduction and integration of these methods and suggested a workflow in R to carry out exploratory and confirmation analyses. With this project, the authors have identified an important and interesting component of these methods that describes building better models by focusing on the predictors (feature engineering)…The authors focus on the variables that go into the model (and how they are represented) and argue that such issues are as important (or more important) than the particular methods that are applied to an analysis...The proposed book is likely to serve as a textbook (for a number of undergraduate and graduate courses in a variety of disciplines) and reference (for a large number of statisticians seeking principled and well-organized modeling)."~Nicholas Horton, Amherst College"I think this book is great and a joy to read…I like the pragmatic and practical approach taken in the book, and the examples given are very illustrative. The emphasis on how and when to use resampling is refreshing and something that the community needs to hear more." ~Andreas C. Muller, Columbia University"The book is timely and needed. The interest in all things 'data science' morphed into everybody pretending to do, or know, Machine Learning. Kuhn and Johnson happen to actually know this—as evidenced by their earlier and still-popular tome entitled ‘Applied Predictive Modeling.’ The proposed ‘Feature Engineering and Selection’ builds on this and extends it. I expect it to become as popular with a wide reach as both a textbook, self-study material, and reference."~Dirk Eddelbuettel, University of Illinois at Urbana-Champaign"As a reviewer, it has been exciting and edifying to see this book develop into what is likely to become one of the foundational works on feature engineering. It is launching propitiously on the current tide of interest in both interpretable models and AutoML."~Robert Horton, Microsoft"In recent years, the statistics literature has featured new developments in modeling and predictive analytics. Approaches such as cross-validation and statistical/machine learning techniques have become widespread. The author's previous book ("Applied Predictive Modeling", APM) provided a wide-ranging introduction and integration of these methods and suggested a workflow in R to carry out exploratory and confirmation analyses. With this project, the authors have identified an important and interesting component of these methods that describes building better models by focusing on the predictors (feature engineering)…The authors focus on the variables that go into the model (and how they are represented) and argue that such issues are as important (or more important) than the particular methods that are applied to an analysis...The proposed book is likely to serve as a textbook (for a number of undergraduate and graduate courses in a variety of disciplines) and reference (for a large number of statisticians seeking principled and well-organized modeling)."~Nicholas Horton, Amherst College"I think this book is great and a joy to read…I like the pragmatic and practical approach taken in the book, and the examples given are very illustrative. The emphasis on how and when to use resampling is refreshing and something that the community needs to hear more." ~Andreas C. Muller, Columbia UniversityTable of Contents1. Introduction. 2. Illustrative Example: Predicting Risk of Ischemic Stroke. 3. A Review of the Predictive Modeling Process. 4. Exploratory Visualizations. 5. Encoding Categorical Predictors. 6. Engineering Numeric Predictors. 7. Detecting Interaction Effects. 8. Handling Missing Data. 9. Working with Profile Data. 10. Feature Selection Overview. 11. Greedy Search Methods. 12. Global Search Methods.

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Book SynopsisThis book aims to help the reader better understand the importance of data analysis in project management. Moreover, it provides guidance by showing tools, methods, techniques and lessons learned on how to better utilize the data gathered from the projects. First and foremost, insight into the bridge between data analytics and project management aids practitioners looking for ways to maximize the practical value of data procured. The book equips organizations with the know-how necessary to adapt to a changing workplace dynamic through key lessons learned from past ventures. The book's integrated approach to investigating both fields enhances the value of research findings. Table of ContentsIntroduction to Data Analytics (DA). Why Data Analytics in Project Management (PM)? The Importance of DA in PM. The Key role of Data Analytics in Business Analysis. Business Analysis in Managing Projects. Earned Value Method. IT solutions of DA as Applied to PM. How to manage Big Data issues in Projects’ Environment. Data Mining and the Project Management Office. Project Portfolio Management. Future Directions.

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Book SynopsisThe book equips students with the end-to-end skills needed to do data science. That means gathering, cleaning, preparing, and sharing data, then using statistical models to analyse data, writing about the results of those models, drawing conclusions from them, and finally, using the cloud to put a model into production, all done in a reproducible way.At the moment, there are a lot of books that teach data science, but most of them assume that you already have the data. This book fills that gap by detailing how to go about gathering datasets, cleaning and preparing them, before analysing them. There are also a lot of books that teach statistical modelling, but few of them teach how to communicate the results of the models and how they help us learn about the world. Very few data science textbooks cover ethics, and most of those that do, have a token ethics chapter. Finally, reproducibility is not often emphasised in data science books. This book is based around a straight-forward workflow conducted in an ethical and reproducible way: gather data, prepare data, analyse data, and communicate those findings. This book will achieve the goals by working through extensive case studies in terms of gathering and preparing data, and integrating ethics throughout. It is specifically designed around teaching how to write about the data and models, so aspects such as writing are explicitly covered. And finally, the use of GitHub and the open-source statistical language R are built in throughout the book.Key Features: Extensive code examples. Ethics integrated throughout. Reproducibility integrated throughout. Focus on data gathering, messy data, and cleaning data. Extensive formative assessment throughout. Trade Review"This clean and fun book covers a wide range of topics on statistical communication, programming, and modeling in a way that should be a useful supplement to any statistics course or self-learning program. I absolutely love this book!"- Andrew Gelman, Columbia University"An excellent book. Communication and reproducibility are of increasing concern in statistics, and this book covers these topics and more in a practical, appealing, and truly unique way."- Daniela Witten, University of Washington"Many data science texts tell you how to perform perfunctory calculations. Instead, Telling Stories with Data tells you how to engage in the mindset and process of analysis. By arming students with the computational, statistical and philosophical skills needed to use data in sense-making and story-telling, this book stands out from the pack as uniquely actionable and empowering."- Emily Riederer, Capital One"This is not another statistics book. It is much better than that. It is a book about doing quantitative research, about scientific justification, about quality control, about communication and epistemic humility. It's a valuable supplement to any methods curriculum, and useful for self-learners as well."- Richard McElreath, Max Planck Institute for Evolutionary Anthropology"Telling Stories with Data is a thoughtful guide to using data to learn and affect positive change. The book includes each stage of the process and can serve as a long-lasting companion to many data scientists and future data story tellers."- Christopher Peters, Zapier“A clever career choice is to pick a field where your skills are complementary with a growing resource. In the coming decades, those who are adept in analysing data will flourish. That means crunching statistics and telling compelling stories. Rohan Alexander’s book will help you do both.”- Andrew Leigh, Member of the Australian Parliament and author of Randomistas: How Radical Researchers Are Changing Our World"Every data analyst has to tell stories with data, and yet traditional textbooks focus on statistical methods alone. Telling Stories with Data teaches the entire data science workflow, including data acquisition, communication, and reproducibility. I highly recommend this unique book!"- Kosuke Imai, Harvard University"This is an extraordinary, wonderful, book, full of wise advice for anyone starting in data science. Intermixing concepts and code means the ideas are immediately made concrete, and the emphasis on reproducible workflows brings a welcome dose of rigor to a rapidly developing field."- David Spiegelhalter, The University of CambridgeTable of Contents1. Telling stories with data 2. Drinking from a fire hose 3. Reproducible workflows Part 1. Foundations 4. Writing research 5. Static communication Part 2. Communication 6. Farm data 7. Gather data 8. Hunt data Part 3. Acquisition 9. Clean and prepare 10. Store and share Part 4. Preparation 11. Exploratory data analysis 12. Linear models 13. Generalized linear models 14. Causality from observational data 15. Multilevel regression with post-stratification 16. Text as data 17. Concluding remarks

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