Data capture and analysis Books
Cambridge University Press Cambridge Advanced National AAQ in IT Data
Book Synopsis
£31.30
O'Reilly Media Fundamentals of Data Engineering
Book SynopsisWith this practical book, you'll learn how to plan and build systems to serve the needs of your organization and customers by evaluating the best technologies available through the framework of the data engineering lifecycle.
£47.99
John Wiley & Sons Inc The Data Warehouse Toolkit
Book SynopsisThis revised and updated edition of the bestseller provides a complete library of dimensional modeling techniques, the most comprehensive collection ever written.Table of ContentsIntroduction xxvii 1 Data Warehousing, Business Intelligence, and Dimensional Modeling Primer 1 Different Worlds of Data Capture and Data Analysis 2 Goals of Data Warehousing and Business Intelligence 3 Dimensional Modeling Introduction 7 Kimball’s DW/BI Architecture 18 Alternative DW/BI Architectures 26 Dimensional Modeling Myths 30 More Reasons to Think Dimensionally 32 Agile Considerations 34 Summary 35 2 Kimball Dimensional Modeling Techniques Overview 37 Fundamental Concepts 37 Basic Fact Table Techniques 41 Basic Dimension Table Techniques 46 Integration via Conformed Dimensions 50 Dealing with Slowly Changing Dimension Attributes 53 Dealing with Dimension Hierarchies 56 Advanced Fact Table Techniques 58 Advanced Dimension Techniques 62 Special Purpose Schemas 67 3 Retail Sales 69 Four-Step Dimensional Design Process 70 Retail Case Study 72 Dimension Table Details 79 Retail Schema in Action 94 Retail Schema Extensibility 95 Factless Fact Tables 97 Dimension and Fact Table Keys 98 Resisting Normalization Urges 104 Summary 109 4 Inventory 111 Value Chain Introduction 111 Inventory Models 112 Fact Table Types 119 Value Chain Integration 122 Enterprise Data Warehouse Bus Architecture 123 Conformed Dimensions 130 Conformed Facts 138 Summary 139 5 Procurement 141 Procurement Case Study 141 Procurement Transactions and Bus Matrix 142 Slowly Changing Dimension Basics 147 Hybrid Slowly Changing Dimension Techniques 159 Slowly Changing Dimension Recap 164 Summary 165 6 Order Management 167 Order Management Bus Matrix 168 Order Transactions 168 Invoice Transactions 187 Accumulating Snapshot for Order Fulfillment Pipeline 194 Summary 199 7 Accounting 201 Accounting Case Study and Bus Matrix 202 General Ledger Data 203 Budgeting Process 210 Dimension Attribute Hierarchies 214 Consolidated Fact Tables 224 Role of OLAP and Packaged Analytic Solutions 226 Summary 227 8 Customer Relationship Management 229 CRM Overview 230 Customer Dimension Attributes 233 Bridge Tables for Multivalued Dimensions 245 Complex Customer Behavior 249 Customer Data Integration Approaches 256 Low Latency Reality Check 260 Summary 261 9 Human Resources Management 263 Employee Profile Tracking 263 Headcount Periodic Snapshot 267 Bus Matrix for HR Processes 268 Packaged Analytic Solutions and Data Models 270 Recursive Employee Hierarchies 271 Multivalued Skill Keyword Attributes 274 Survey Questionnaire Data 277 Summary 279 10 Financial Services 281 Banking Case Study and Bus Matrix 282 Dimension Triage to Avoid Too Few Dimensions 283 Supertype and Subtype Schemas for Heterogeneous Products 293 Hot Swappable Dimensions 296 Summary 296 11 Telecommunications 297 Telecommunications Case Study and Bus Matrix 297 General Design Review Considerations 299 Design Review Guidelines 304 Draft Design Exercise Discussion 306 Remodeling Existing Data Structures 309 Geographic Location Dimension 310 Summary 310 12 Transportation 311 Airline Case Study and Bus Matrix 311 Extensions to Other Industries 317 Combining Correlated Dimensions 318 More Date and Time Considerations 321 Localization Recap 324 Summary 324 13 Education 325 University Case Study and Bus Matrix 325 Accumulating Snapshot Fact Tables 326 Factless Fact Tables 329 More Educational Analytic Opportunities 336 Summary 336 14 Healthcare 339 Healthcare Case Study and Bus Matrix 339 Claims Billing and Payments 342 Electronic Medical Records 348 Facility/Equipment Inventory Utilization 351 Dealing with Retroactive Changes 351 Summary 352 15 Electronic Commerce 353 Clickstream Source Data 353 Clickstream Dimensional Models 357 Integrating Clickstream into Web Retailer’s Bus Matrix 368 Profitability Across Channels Including Web 370 Summary 373 16 Insurance 375 Insurance Case Study 376 Policy Transactions 379 Premium Periodic Snapshot 385 More Insurance Case Study Background 388 Claim Transactions 390 Claim Accumulating Snapshot 392 Policy/Claim Consolidated Periodic Snapshot 395 Factless Accident Events 396 Common Dimensional Modeling Mistakes to Avoid 397 Summary 401 17 Kimball DW/BI Lifecycle Overview 403 Lifecycle Roadmap 404 Lifecycle Launch Activities 406 Lifecycle Technology Track 416 Lifecycle Data Track 420 Lifecycle BI Applications Track 422 Lifecycle Wrap-up Activities 424 Common Pitfalls to Avoid 426 Summary 427 18 Dimensional Modeling Process and Tasks 429 Modeling Process Overview 429 Get Organized 431 Design the Dimensional Model 434 Summary 441 19 ETL Subsystems and Techniques 443 Round Up the Requirements 444 The 34 Subsystems of ETL 449 Extracting: Getting Data into the Data Warehouse 450 Cleaning and Conforming Data 455 Delivering: Prepare for Presentation 463 Managing the ETL Environment 483 Summary 496 20 ETL System Design and Development Process and Tasks 497 ETL Process Overview 497 Develop the ETL Plan 498 Develop One-Time Historic Load Processing 503 Develop Incremental ETL Processing 512 Real-Time Implications 520 Summary 526 21 Big Data Analytics 527 Big Data Overview 527 Recommended Best Practices for Big Data 531 Summary 542 Index 543
£47.50
Springer Fachmedien Wiesbaden Big Data im Gesundheitswesen kompakt: Konzepte,
Book SynopsisDas kompakte Fachbuch gibt einen Überblick über die Möglichkeiten von „Big Data“ im Gesundheitswesen und beschreibt anhand von ausgewählten Szenarien mögliche Einsatzgebiete.Die Autoren erläutern zentrale Systemkomponenten und IT-Standards und thematisieren anhand wichtiger Daten des Gesundheitswesens die Notwendigkeit der Strukturierung und Modellierung von Daten. Das Buch gibt Hinweise wie Geschäftsprozesse im Gesundheitswesen dokumentiert, analysiert und verbessert werden können. Anwendungsszenarien, wie die Datenanalysen für Krankenhäuser, Labore, Versicherungen und die Pharmaindustrie, zeigen die praktische Relevanz des Themas. Aber auch rechtliche und ethische Aspekte werden inhaltlich angeschnitten.Ein Buch für Entscheider in der medizinischen Leitung und Verwaltung von Krankenhäusern, Fachleute sowie niedergelassene Ärzte und Apotheker, aber auch Personen in Ausbildung und Studium im Gesundheitswesen. Table of ContentsBig-Data-Analytics im Gesundheitswesen - Medizin - Verwaltung - Forschung: Anwendungsgebiete für Big-Data-Analytics - Gesetzliche Rahmenbedingungen und Big-Data-Ethik
£13.49
Kogan Page Ltd DataDriven HR
Book SynopsisBernard Marr is one of the leading voices in Technology and Innovation. A futurist and strategic performance consultant, he has advised many of the world's best-known organizations on their business and data strategies. A frequent keynote speaker, he also writes on the topic of data and analytics for various publications including Forbes and the Huffington Post. Bernard Marr is also the author of Data Strategy (2021) and The Intelligence Revolution (2020) published by Kogan Page.Trade Review"Without a doubt human capability (talent + leadership + organization + HR) increasingly delivers value to all stakeholders. This excellent book provides business and HR leaders the information required to improve decision making. Bernard's insights on analytics and AI will be the keys for progress." * Dave Ulrich, Rensis Likert Professor, Ross School of Business, University of Michigan Partner, The RBL Group *"If anyone was going to publish a book about the impact of the latest technology developments such as AI on the field of HR and People Analytics my bets were on Bernard Marr. And you won't be disappointed. The book offers a deep dive into the world of data of every kind, every possible use case, honest overview of technology and important considerations. It has never been more critical to educate ourselves about it." * Maja Luckos, VP, Employee Success, Salesforce *"This book propelled me into a world of possibilities for HR leaders in embracing the 'intelligence revolution' to shape people strategies that add value to their organizations and their people. It's enlightened me to the power of AI-enabled HR and how I might use it, and it's made me want to learn more. This is a must read for all HR leaders." * Linda Sleath, Group HR Director, Topps Tiles Plc. *"Data-Driven HR strikes a nice balance between exploring emerging trends in people analytics while primarily serving as a practical guide to HR professionals at any stage of their data journey. The second edition seamlessly weaves AI into a narrative that's easy to engage with and is packed full of examples that bring the theories to life." * Mark Ferrie, People Analytics Director, Meta *"Data-Driven HR is a terrific overview of the enormous world of people analytics and AI. For people trying to understand this important space, this book shows you the way." * Josh Bersin, Global Industry Analyst and CEO of The Josh Bersin Company *"Data, analytics and AI provides to elevate HR from its traditional role as a support function to one of a strategic partner creating value for the enterprise, its customers and its employees. There's a well-thumbed copy of the first edition of Data Driven HR on my bookshelf, and in this timely update Marr, one of the most knowledgeable people on the topic, explains how data and AI can enable HR to drive better decision making about people, deliver an enhanced service to employees; and make HR processes more efficient." * David Green, Managing Partner at Insight222, co-author of Excellence in People Analytics, and host of the Digital HR Leaders podcast. *"Bernard Marr has once again delivered an indispensable guide to harnessing the power of data, analytics and AI in HR. This updated edition thoroughly captures the latest innovations shaping human resources while still being accessible for HR professionals at any level. Through compelling examples and clear frameworks, Marr demonstrates how to drive business value through evidence-based talent practices. This is a must-read playbook for any HR leader looking to build capabilities in data-driven decision-making." * Professor Max Blumberg, PhD, University of Leeds *"This is a great guide for HR professionals who are grappling with the transition to becoming data led. It's easy to read, and with real examples and case studies across the employee lifecycle, it's also a pragmatic resource to have in your HR toolkit." * Ashish Sinha Korn Ferry Head of People Analytics, AI & Strategy EMEA Practice Leader *"AI is transforming the world of work and our personal lives. With a people-centric approach, Bernard Marr demystifies data driven AI enabled HR with context, thought provoking insights and examples of AI at the time this book was written. We all have a role to play when it comes to this rapidly evolving space as the output of AI will be a reflection of our culture and values. Staying on top of leading practices, lessons learned, emerging regulations and standards is critical so we can unlock AI's potential and value add to the business, our customers and employees while minimizing risk. This book sets the foundation so we can do just that!" * Terilyn Juarez Monroe, Terilyn Juarez Monroe, Chief People Officer *"Data-Driven HR is an indispensable resource for Career Services professionals looking to equip their students with cutting-edge strategies in today's competitive job market. This comprehensive book offers invaluable insights into recruitment and candidate selection, employer branding, pinpointing the most effective recruitment channels, and harnessing AI-enhanced automation to identify and assess the best candidates for businesses. It's a game-changer for career advisors committed to empowering their students with the knowledge and skills needed to excel in the evolving world of talent acquisition and HR." * Dr. Amber Wigmore Álvarez, Associate Professor, IE Business School and IE University *Table of Contents Chapter - 00: Preface; Section - ONE: Data, Analytics and AI in HR; Chapter - 01: How data and AI are transforming HR; Chapter - 02: How data and AI have come to revolutionise HR; Chapter - 03: The Data, Analytics and AI tools available to HR; Section - TWO: Data-Driven and AI-enabled HR in Practice; Chapter - 04: Better HR insights and decision-making; Chapter - 05: Recruitment and candidate selection; Chapter - 06: Employee Onboarding; Chapter - 07: Performance Monitoring and Management; Chapter - 08: Employee Training and Development; Chapter - 09: Performance monitoring and management; Chapter - 10: Identify the use cases; Chapter - 11: Building skills and aligning culture; Section - THREE: Making data-driven and AI enabled HR happen; Chapter - 12: Identifying the use cases for your organization; Chapter - 13: The future of HR
£31.34
Manning Publications Data Analysis with Python and PySpark
Book SynopsisWhen it comes to data analytics, it pays tothink big. PySpark blends the powerful Spark big data processing engine withthe Python programming language to provide a data analysis platform that can scaleup for nearly any task. Data Analysis with Python and PySpark is yourguide to delivering successful Python-driven data projects. Data Analysis with Python and PySpark is a carefully engineered tutorial that helps you use PySpark to deliver your data-driven applications at any scale. This clear and hands-on guide shows you how to enlarge your processing capabilities across multiple machines with data from any source, ranging from Had oop-based clusters to Excel worksheets. You'll learn how to break down big analysis tasks into manageable chunks and how to choose and use the best PySpark data abstraction for your unique needs. The Spark data processing engine is an amazing analytics factory: raw data comes in,and insight comes out. Thanks to its ability to handle massive amounts of data distributed across a cluster, Spark has been adopted as standard by organizations both big and small. PySpark, which wraps the core Spark engine with a Python-based API, puts Spark-based data pipelines in the hands of programmers and data scientists working with the Python programming language. PySpark simplifies Spark's steep learning curve, and provides a seamless bridge between Spark and an ecosystem of Python-based data science tools. Trade Review“A great and gentle introduction to spark.” Javier Collado Cabeza “A phenomenal introduction to PySpark from the ground up.”Anonymous Reviewer “A great book to get you started with PySpark!” Jeremy Loscheider “Takes you on an example focused tour of building pyspark data structures from the data you provide and processing them at speed.” Alex Lucas “If you need to learn PySpark (as a Data Scientist or Data Wrangler) start with this book!”Geoff Clark
£40.85
Pearson Education Modern Information Retrieval
Book SynopsisThis is a rigorous and complete textbook for a first course on information retrieval from the computer science perspective. It provides an up-to-date student oriented treatment of information retrieval including extensive coverage of new topics such as web retrieval, web crawling, open source search engines and user interfaces.Table of Contents Contents Preface Acknowledgements 1 Introduction 2 User Interfaces for Search by Marti Hearst 3 Modeling 4 Retrieval Evaluation 5 Relevance Feedback and Query Expansion 6 Documents: Languages & Properties with Gonzalo Navarro and Nivio Ziviani 7 Queries: Languages & Properties with Gonzalo Navarro 8 Text Classification with Marcos Gon¸calves 9 Indexing and Searching with Gonzalo Navarro 10 Parallel and Distributed IR with Eric Brown 11 Web Retrieval with Yoelle Maarek 12 Web Crawling with Carlos Castillo 13 Structured Text Retrieval with Mounia Lalmas 14 Multimedia Information Retrieval by Dulce Poncele´on and Malcolm Slaney 15 Enterprise Search by David Hawking 16 Library Systems by Edie Rasmussen 17 Digital Libraries by Marcos Gon¸calves A Open Source Search Engines with Christian Middleton B Biographies Bibliography Index
£64.59
Ebury Publishing Data Grab
Book SynopsisYour life online is their product.In the past, colonialism was a landgrab of natural resources, exploitative labour and private property from countries around the world. It promised to modernise and civilise, but actually sought to control. It stole from native populations and made them sign contracts they didn't understand. It took resources just because they were there.Colonialism has not disappeared it has taken on a new form.In the new world order, data is the new oil. Big Tech companies are grabbing our most basic natural resources our data exploiting our labour and connections, and repackaging our information to control our views, track our movements, record our conversations and discriminate against us. Every time we unthinkingly click Accept' on Terms and Conditions, we allow our most personal information to kept indefinitely, repackaged by big Tech companies to control and exploit us for their own profit.In this searinTrade ReviewI wish that Data Grab was required reading when I was a graduate student working in the field of AI. Perspectives like these are crucial if we are to break the colonial paradigm that pervades computing disciplines -- Timnit Gebru, founder of the Distributed AI Research InstituteA blistering, vital exposure of the predatory world of data colonialism. In this vivid and passionately written book, Mejias and Couldry urge us to wake up to the invasive and extractive world of today’s Big Tech -- Mike Savage, author of 'Social Class in the 21st Century'Remarkable... Data Grab helps us understand that the historical and ongoing relations of power have extended to the realm of data, a new raw material of digital capitalism. Mejias and Couldry place us on a path to recognise, resist, and challenge these forces -- Dr Ramesh Srinivasan, Professor at the UCLA Department of Information Studies and Director of UC Digital Cultures LabAs in their previous work, Mejias and Couldry show how important it is to take the perspective of the colonized, not the colonizer, in explaining how the digital world is governed. Data Grab offers important insights into how we should analyse power and counter-power in terms of data control. I particularly recommend this book for providing examples of local and vocal initiatives across various continents. A true eye-opener -- José van Dijck, Distinguished Professor of Media and Digital Society, Utrecht UniversityIn this essential and original work, Mejias and Couldry lay out a powerful and persuasive analysis of the logical continuity between modern colonialism and the extraction of data by Big Tech and its platforms. Their call to resist data colonialism could not be more urgent or more timely -- Jeremy Gilbert, author of 'Hegemony Now: How Big Tech and Wall Street Won the World' and 'Twenty-First Century Socialism'
£18.70
Taylor & Francis Ltd Business Statistics Using Excel
Book SynopsisThis book gives readers a hands-on understanding of Excel-assisted statistical techniques to take effective business decisions. It showcases applications of the tools and techniques of statistics for analysing business data from the domain of business statistics.The volume provides an exhaustive introduction to the application of statistics in solving business problems and implementing data analytics for effective decision making in all kinds of business situations around the world. With an emphasis on simplicity in presentation of concepts of statistical methods and associated Excel functions, the volume explores the implementation of Excel functions through well-defined sequences of steps. It covers an array of key topics which include Discussions on real-world problems, decision support systems, scope of business statistics, types, and steps of research; Introduction to Excel and its mathematical and preliminary statistical functions; usage of different Table of Contents1. Introduction 2. Introduction to Excel 3. Count, Frequency and Histogram 4. Average Functions 5. Median and Mode 6. Measures of Variation 7. Measures Of Skewness 8. Probability Distributions 9. Sampling Distribution of Mean and Variance 10. Testing Of Hypothesis 11. Chi-Square Test 12. Nonparametric Test 13. Correlation and Covariance 14. Forecasting 15. Analysis of Variance (Anova) 16. Charts 17. Linear Programming
£35.99
John Wiley & Sons Inc Beginning Power BI for Business Users
Book SynopsisDiscover the utility of your organization's data with Microsoft Power BI In Beginning Power BI for Business Users: Learning to Turn Data into Insights, accomplished data professional and business intelligence expert Paul Fuller delivers an intuitive and accessible handbook for professionals seeking to use Microsoft's Power BI to access, analyze, understand, report, and act on the data available to their organizations. In the book, you'll discover Power BI's robust feature set, learn to ingest and model data, visualize and report on that data, and even use the DAX scripting language to unlock still more utility from Microsoft's popular program. Beginning with general principles geared to readers with no or little experience with reporting or data analytics tools, the author walks you through how to manipulate common, publicly available data sourcesincluding Excel files and relational databases. You'll also learn to: Use the included and testedTable of ContentsIntroduction xxi Part 1: Introduction Chapter 1: Introducing Power BI 3 What Is Power BI? 3 Why Would I Use Power BI? 7 But Why? 7 Disconnected Data 8 Fixed Formulas 9 Ten Reasons Why Power BI Transforms Your Work 10 Will I Still Use Excel? 12 How Much Is this Going to Cost? 14 Where Do We Go from Here? 16 Chapter 2: From Data to Insight 17 Three Guiding Principles 17 Distinguishing Data, Information, and Knowledge 18 Follow a Consistent, Proven Method 21 Data Sources 21 Files 21 Databases 22 Online Services 23 Plan Ahead 23 Import Data 25 Prepare Data 26 Model Data 28 Report Data 29 Analyze Data 30 Use Visualizations Well 31 Three Ways to Ruin Visual Analytics 32 Use as Many Cool Gadgets as You Can Find 32 Overwhelm the User with Tons of Detail 32 Pick the Wrong Tool for the Job 34 Four Ways to Improve Visual Analytics 35 Reduce the Nondata Pixels 35 Enhance the Data Pixels 36 Conclusion 38 Part 2: Power BI Guided Walk-Throughs Chapter 3: Let’s Take a Flyover! 41 Getting Connected 41 A Quick Tour of the Power BI Service 42 Frequented Hotspots of Power BI 42 Adding Sample Content 43 Understanding the Power BI Service Interface 44 Highlighting 47 Slicers 50 Filters 52 Drill-Down 54 Dashboards 55 Drill-Through 58 Visibility 59 Show as Table 59 Sorting Visuals 60 Filtering— One More Time 61 Power BI Building Blocks 61 Flyover of Power BI Desktop 62 Navigating Power BI Desktop 63 Conclusion 65 Chapter 4: Building Your First Report 67 Connecting to a Data Source 67 Examining the Data Model 68 Exploring the Data 75 Building Your Report 85 Designing for Mobile Devices 101 Conclusion 102 Chapter 5: Preparing Data 103 GDP Data 103 Power Query Overview 105 Data Arrangement Steps 106 Rename Query 106 Remove Rows 106 Remove Columns 107 Applied Steps So Far 107 Promote Headers 108 Renaming Columns 108 Empty Values 109 Handling Aggregated Data 109 Filtering Rows 110 Unpivoting Data 110 Change Column Data Type 112 Changing Values in Columns 112 Duplicating Columns 113 Multiply by a Million 113 Removing Implicit Measures 114 Column Formatting 115 Calculated Columns 115 Add Previous Year Column 116 Hiding Columns 117 LOOKUPVALUE Function 118 DIVIDE Function 119 Visualize Our GDP Data 120 Where Are We? 121 Cleaning Messy Data 122 Handle Many-to-Many Relationships 123 Duplicate a Query 125 Split Column 125 Format Column 127 Remove Duplicates 127 Index Column 128 Merge Data 128 Applying Changes 129 Finding the Power Query Button 131 Simple Cleanup Tasks 131 Replacing Values 132 Custom Columns 133 More Cleanup 135 Cleaning Date Columns 136 Conclusion 138 Chapter 6: Modeling Data 139 Dimensional Modeling 140 Why Does This Matter? 143 Makes Sense to You 143 Cross-Business Process Analysis 144 Flexible Grouping 144 Performance for Power BI 144 Facts 145 Types of Facts 146 Granularity 146 Relationships Between Facts (Never!) 147 Dimensions 148 Using Integer Keys for Relationships 149 Hierarchies in Dimensions 149 Date Dimensions 150 Power Query and Data Modeling 151 How to Build a Date Dimension 152 Import a Date Table from SQL Server Database 153 Create a Date Dimension with Power Query 155 Create a Date Dimension with DAX 158 Sort by Column 161 Mark It as a Date Table 161 How to Arrange into a Star- Schema 161 Create a State Dimension 162 Create a GDP Category Dimension 163 Add a New StateID Foreign Key to Our Fact Table 163 Unpivot the GDP Year Values 164 Putting It All into Action 166 Conclusion 167 Part 3: Going Deeper Into Power BI Chapter 7: Relationships and Filtering 171 Filtering Through Relationships 171 Visuals Interact Independently 174 The Direction of Relationships 176 Bidirectional Filtering 179 Report Filter Types 181 Visual Filters 181 Date Filtering 182 Numeric Filtering 182 Text Filtering 183 Page and Report Filters 185 Controlling the Use of Filters 185 Conclusion 186 Chapter 8: Enhancing Your Report 187 Configuring Drill- Down 187 Configuring Drill- Through 190 Buttons 193 Bookmarks 195 Personal Bookmarks vs. Report Bookmarks 199 Selections and Visibility 201 Conclusion 205 Chapter 9: Refreshing, Sharing, and Collaborating 207 Refreshing Data 207 Configuring Data Source Credentials 210 Power BI Data Gateway 213 Scheduling Refresh 214 Dataset Modes 216 Sharing and Collaborating 216 Workspaces 216 Creating Workspaces 217 Publishing 217 Security and Permission Levels 219 Power BI Apps 223 Sharing 230 Summary 232 Chapter 10: Introducing Dax 233 Setup 233 Calculated Columns 235 How to Create a Calculated Column 236 DAX Measures 240 COUNTROWS and DISTINCTCOUNT 244 Filter Context 246 CALCULATE Function 249 Conclusion 250 Chapter 11: Conclusion 251 Where Does My Organization Begin? 251 Delivery Strategy 251 Business-Led Self-Service 252 IT-Managed Self-Service 253 Corporate BI 253 Which Delivery Strategy Is Right for My Organization? 253 Where Do I Go from Here? 254 Top Gurus 254 Advice 254 Index 257
£40.38
Kogan Page Ltd Data Ethics
Book SynopsisKatherine O'Keefe works with Ireland's national water utility, Uisce Éireann, and is on the teaching faculty of the Law Society of Ireland's Diploma Centre. She has developed international professional accreditation schemes for information management, for which she was awarded the DAMA International Professional Achievement Award in 2017. Daragh O Brien is the founder and managing director of Castlebridge, a leading data strategyconsultancy. He lectures in Data Protection and Data Governance in the Sutherland School of Law at University College Dublin. Daragh is a founding member of the Strategic Advisory Board to the School of Business in Maynooth University.Trade Review"Ethics play an increasingly important role when considering how to collect and use personal data. This updated edition of Data Ethics clearly explains how to take ethics seriously and make it an integral part of business information management and governance. The combination of sound and up to date legal theories with practical tips and case studies makes it a useful handbook for anyone working with data on a regular basis." * Paul Breitbarth, Senior Visiting Fellow, European Centre on Privacy and Cybersecurity, Maastricht University *"In a world where AI is creating a growing wave of often dubious information, O Brien and O'Keefe's book should be mandatory reading for everybody in IT, media, regulatory bodies and beyond. This new edition of Data Ethics focuses on emerging topics of vital importance in a world where ethical decisions by IT may literally be, in the extreme, matters of life and death." * Dr Barry Devlin, Founder, 9 Sight Consulting and author of Business unIntelligence *"I can't think of a subject more relevant than data ethics. Given that we live in a data-dependent world, the most important question is not "Can I do something with data?" but "Should I do something with data?". These questions should be considered by teens learning to code, businesspeople gathering and exploiting customer data, scientists developing and releasing Artificial Intelligence (AI) applications, and anyone creating and using data. Daragh and Katherine provide an excellent groundwork for addressing these questions and give us the tools to think and act with our data in a responsible way. Read their book, share it and apply it!" * Danette McGilvray, President and Principal, Granite Falls Consulting, Inc. and author of Executing Data Quality Projects *"Reading Data Ethics gave me goosebumps. Impeccably researched, it is the definitive work on the topic. Simultaneously confronting and enlightening, it challenged my own ethical framework and validated the principles I hold dear in my practice as a Data Governance Executive. The foreword by John Ladley is delightful and sets the scene perfectly for what is to follow. I look forward to our DAMA community here in Australia, and internationally, having the opportunity to share their experiences after reading this outstanding book on data ethics." * Andrew Andrews, Data Governance Manager, ANZ Banking Group and Vice President of Marketing, DAMA International *Table of Contents Chapter - 00: Introduction - Why write a book on data ethics?; Chapter - 01: Ethics in the context of data management; Chapter - 02: Introduction to ethical concepts and frameworks; Chapter - 03: Ethical principles, standards and practice; Chapter - 04: Ethics, privacy and analytics; Chapter - 05: Ethics and data management (including AI); Chapter - 06: Developing an ethical architecture for information management; Chapter - 07: Introducing the Ethical Enterprise Information Management (E2IM) framework; Chapter - 08: Information ethics as an information quality system; Chapter - 09: Information ethics and data governance; Chapter - 10: Information ethics and risk - Tools and methods for identifying and managing ethical risk; Chapter - 11: Data ethics - the bigger picture; Chapter - 12: And in conclusion...;
£42.74
Oxford University Press Big Data
Book SynopsisAn unimaginably vast amount of data is now generated by our on-line lives and businesses, At the same time, our ability to store, manage, analyse, and exploit this data is becoming ever more sophisticated. This Very Short Introduction maps out the technology, and also the range of possibilities, challenges, and ethical questions it raises.Trade ReviewBig data is in the news, and this excellent very short introduction brings the reader up to speed and enables them to understand the various components and implications. * Paradigm Explorer *This is a very useful, concise introduction to the topic of big data. * Jonathan Cowie, Science Fact & Science Fiction Concatenation *A very short introduction to a very big subject ... arguably the most topical of this book series ... This very short introduction is perfect for anyone who is a little bit baffled by the very concept of big data. Holmes introduces the subject in a format that is both concise and manageable. * Jade Taylor-Salazar, E&T Magazine *Table of ContentsBYTE SIZE CHART; REFERENCES; FURTHER READING; INDEX
£9.99
Taylor & Francis Ltd Digital Analytics for Marketing
Book SynopsisThis second edition of Digital Analytics for Marketing provides students with a comprehensive overview of the tools needed to measure digital activity and implement best practices when using data to inform marketing strategy. It is the first text of its kind to introduce students to analytics platforms from a practical marketing perspective.Demonstrating how to integrate large amounts of data from web, digital, social, and search platforms, this helpful guide offers actionable insights into data analysis, explaining how to connect the dots and humanize information to make effective marketing decisions. The authors cover timely topics, such as social media, web analytics, marketing analytics challenges, and dashboards, helping students to make sense of business measurement challenges, extract insights, and take effective actions. The book's experiential approach, combined with chapter objectives, summaries, and review questions, will engage readers, deepening their learTable of Contents1. Introduction to Digital Marketing and Analytics 2. Digital Marketing KPIs, Strategy, Ecosystems, Governance, and More 3. The Evolution of Digital Analytics and the Internet 4. The Growth and Relevance of Social Media in Analytics and Digital Marketing 5. Data for Digital Marketing Analytics 6. Social Media Analytics for Digital Marketing 7. Actions, Hyperlink, and Mobile Analytics in Digital Marketing 8. Advanced AI and Algorithms 9. Basic Web Analytics and Web Intelligence 10. Advanced Web Analytics and Web Intelligence 11. Aligning Digital Marketing with Business Strategy 12. Deriving Strategic Insights and ‘Digital Value’ from Digital Marketing Analytics
£156.75
John Wiley & Sons Inc An Introduction to Categorical Data Analysis
Book SynopsisA valuable new edition of a standard reference The use of statistical methods for categorical data has increased dramatically, particularly for applications in the biomedical and social sciences. An Introduction to Categorical Data Analysis, Third Edition summarizes these methods and shows readers how to use them using software. Readers will find a unified generalized linear models approach that connects logistic regression and loglinear models for discrete data with normal regression for continuous data. Adding to the value in the new edition is: Illustrations of the use of R software to perform all the analyses in the book A new chapter on alternative methods for categorical data, including smoothing and regularization methods (such as the lasso), classification methods such as linear discriminant analysis and classification trees, and cluster analysis New sections in many chapters introducing the Bayesian approach for the methodTable of ContentsPreface ix About the Companion Website xiii 1 Introduction 1 1.1 Categorical Response Data 1 1.2 Probability Distributions for Categorical Data 3 1.3 Statistical Inference for a Proportion 5 1.4 Statistical Inference for Discrete Data 10 1.5 Bayesian Inference for Proportions * 13 1.6 Using R Software for Statistical Inference about Proportions * 17 Exercises 21 2 Analyzing Contingency Tables 25 2.1 Probability Structure for Contingency Tables 26 2.2 Comparing Proportions in 2 × 2 Contingency Tables 29 2.3 The Odds Ratio 31 2.4 Chi-Squared Tests of Independence 36 2.5 Testing Independence for Ordinal Variables 42 2.6 Exact Frequentist and Bayesian Inference * 46 2.7 Association in Three-Way Tables 52 Exercises 56 3 Generalized Linear Models 65 3.1 Components of a Generalized Linear Model 66 3.2 Generalized Linear Models for Binary Data 68 3.3 Generalized Linear Models for Counts and Rates 72 3.4 Statistical Inference and Model Checking 76 3.5 Fitting Generalized Linear Models 82 Exercises 84 4 Logistic Regression 89 4.1 The Logistic Regression Model 89 4.2 Statistical Inference for Logistic Regression 94 4.3 Logistic Regression with Categorical Predictors 98 4.4 Multiple Logistic Regression 102 4.5 Summarizing Effects in Logistic Regression 107 4.6 Summarizing Predictive Power: Classification Tables, ROC Curves, and Multiple Correlation 110 Exercises 113 5 Building and Applying Logistic Regression Models 123 5.1 Strategies in Model Selection 123 5.2 Model Checking 130 5.3 Infinite Estimates in Logistic Regression 136 5.4 Bayesian Inference, Penalized Likelihood, and Conditional Likelihood for Logistic Regression * 140 5.5 Alternative Link Functions: Linear Probability and Probit Models * 145 5.6 Sample Size and Power for Logistic Regression * 150 Exercises 151 6 Multicategory Logit Models 159 6.1 Baseline-Category Logit Models for Nominal Responses 159 6.2 Cumulative Logit Models for Ordinal Responses 167 6.3 Cumulative Link Models: Model Checking and Extensions * 176 6.4 Paired-Category Logit Modeling of Ordinal Responses * 184 Exercises 187 7 Loglinear Models for Contingency Tables and Counts 193 7.1 Loglinear Models for Counts in Contingency Tables 194 7.2 Statistical Inference for Loglinear Models 200 7.3 The Loglinear – Logistic Model Connection 207 7.4 Independence Graphs and Collapsibility 210 7.5 Modeling Ordinal Associations in Contingency Tables 214 7.6 Loglinear Modeling of Count Response Variables * 217 Exercises 221 8 Models for Matched Pairs 227 8.1 Comparing Dependent Proportions for Binary Matched Pairs 228 8.2 Marginal Models and Subject-Specific Models for Matched Pairs 230 8.3 Comparing Proportions for Nominal Matched-Pairs Responses 235 8.4 Comparing Proportions for Ordinal Matched-Pairs Responses 239 8.5 Analyzing Rater Agreement * 243 8.6 Bradley–Terry Model for Paired Preferences * 247 Exercises 249 9 Marginal Modeling of Correlated, Clustered Responses 253 9.1 Marginal Models Versus Subject-Specific Models 254 9.2 Marginal Modeling: The Generalized Estimating Equations (GEE) Approach 255 9.3 Marginal Modeling for Clustered Multinomial Responses 260 9.4 Transitional Modeling, Given the Past 263 9.5 Dealing with Missing Data * 266 Exercises 268 10 Random Effects: Generalized Linear Mixed Models 273 10.1 Random Effects Modeling of Clustered Categorical Data 273 10.2 Examples: Random Effects Models for Binary Data 278 10.3 Extensions to Multinomial Responses and Multiple Random Effect Terms 284 10.4 Multilevel (Hierarchical) Models 288 10.5 Latent Class Models * 291 Exercises 295 11 Classification and Smoothing * 299 11.1 Classification: Linear Discriminant Analysis 300 11.2 Classification: Tree-Based Prediction 302 11.3 Cluster Analysis for Categorical Responses 306 11.4 Smoothing: Generalized Additive Models 310 11.5 Regularization for High-Dimensional Categorical Data (Large p) 313 Exercises 321 12 A Historical Tour of Categorical Data Analysis * 325 Appendix: Software for Categorical Data Analysis 331 A.1 R for Categorical Data Analysis 331 A.2 SAS for Categorical Data Analysis 332 A.3 Stata for Categorical Data Analysis 342 A.4 SPSS for Categorical Data Analysis 346 Brief Solutions to Odd-Numbered Exercises 349 Bibliography 363 Examples Index 365 Subject Index 369
£109.76
Cambridge University Press The Statistical Physics of Data Assimilation and
Book SynopsisData assimilation is a hugely important mathematical technique, relevant in fields as diverse as geophysics, data science, and neuroscience. This modern book provides an authoritative treatment of the field as it relates to several scientific disciplines, with a particular emphasis on recent developments from machine learning and its role in the optimisation of data assimilation. Underlying theory from statistical physics, such as path integrals and Monte Carlo methods, are developed in the text as a basis for data assimilation, and the author then explores examples from current multidisciplinary research such as the modelling of shallow water systems, ocean dynamics, and neuronal dynamics in the avian brain. The theory of data assimilation and machine learning is introduced in an accessible and unified manner, and the book is suitable for undergraduate and graduate students from science and engineering without specialized experience of statistical physics.Table of Contents1. Prologue: linking 'The Future' with the present; 2. A data assimilation reminder; 3. Remembrance of things path; 4. SDA variational principles; Euler–Lagrange equations and Hamiltonian formulation; 5. Using waveform information; 6. Annealing in the model precision Rf; 7. Discrete time integration in data assimilation variational principles; Lagrangian and Hamiltonian formulations; 8. Monte Carlo methods; 9. Machine learning and its equivalence to statistical data assimilation; 10. Two examples of the practical use of data assimilation; 11. Unfinished business; Bibliography; Index.
£55.09
O'Reilly Media The SelfService Data Roadmap
Book SynopsisData-driven insights are a key competitive advantage for any industry today, but deriving insights from raw data can still take days or weeks. With this practical book, data engineers, data scientists, and team managers will learn how to build a self-service data science platform that helps anyone in your organization extract insights from data.
£42.39
Manning Publications How to Lead in Data Science
Book SynopsisTo lead a data science team, you need to expertly articulate technology roadmaps, support a data-driven culture, and plan a data strategy that drives a competitive business plan. In this practical guide, you'll learn leadership techniques the authors have developed building multiple high-performance data teams. In How to Lead in Data Science you'll master techniques for leading data science at every seniority level, from heading up a single project to overseeing a whole company's data strategy. You'll find advice on plotting your long-term career advancement, as well as quick wins you can put into practice right away. Throughout, carefully crafted assessments and interview scenarios encourage introspection, reveal personal blind spots, and show development areas to help advance your career. Leading a data science team takes more than the typical set of business management skills. You need specific know-how to articulate technology roadmaps, support a data-driven culture, and plan a data strategy that drives a competitive business plan. Whether you're looking to manage your team better or work towards a seat at your company's top leadership table, this book will show you how. Trade Review“Improveleadership skills, irrespective of the domain you are in.” Vishwesh RaviShrimali “Whether you are new to managing, new to data science, or just want tobe a better advocate for your data team there are a lot of tips to improve yourpractice.” MichaelPetrey “This is a book that surpasses the boundaries of mining data and coding,but warns you about not forgetting them in the effort to successfully lead datascience teams.” JesúsJuárez-Guerrero “Excellent book. Covers a large complex topic in a clear and understandableway.” GaryBake “Excellent and ambitious book that provides actionable insight on how tolead in data science. Filled with insightful vignettes, anecdotes, and casestudies to bring life and relevance to the frameworks and discussion.” MarcParadis
£37.99
BCS Learning & Development Limited Managing Data Quality: A practical guide
Book SynopsisData is an increasingly important business asset and enabler for organisational activities. With growth in data sets and data volumes, it's becoming ever harder to manage. Data quality - the fitness for purpose of data - is a key aspect of data management and failure to understand it increases organisational risk and decreases efficiency and profitability. This book explains data quality management in practical terms, focusing on three key areas - the nature of data in enterprises, the purpose and scope of data quality management, and implementing a data quality management system, in line with ISO 8000-61.Trade Review'Written by two world-renowned experts, this book is the world's first comprehensive guide to the management of "data quality" and the ISO 8000 series.' -- Yoshiaki Sonoda * Engineering Manager and Data Quality evangelist, Mitsubishi Heavy Industries, Ltd. *'Tim King and Julian Schwarzenbach have a wealth of experience both in improving the way organizations manage information quality and in developing standards to support managing data quality, and as a result they bring not just ideas but examples of both good and bad practice that you can learn from...the best book on the subject I’ve read.' -- Matthew West * Director, Information Junction *'I often hear the phrase ‘we don’t trust the data’ and often this stems from poor data quality. Tim and Julian have brought their considerable experience together to give you a detailed and practical guide on how to improve the quality of your data, including real world examples to bring their points to life.' -- Caroline Carruthers * Chief Executive, Carruthers and Jackson, Co-author of ‘The CDO Playbook’ *'Managing Data Quality shines a light on the true nature of data quality, and its fundamental contribution to effective decision making. The Authors guide the reader through an accessible and logical journey, one that is anchored in real-world application, providing valuable frameworks for data management professionals and business leaders alike.' -- Dr Mark Parsons * Chief Proposition Development Officer, Arcadis Gen *'This book is a very valuable and welcome addition to the literature on data quality best practice.' -- Nigel Turner * Principal Information Management Consultant, Global Data Strategy *Table of ContentsPart 1: The Challenge of Enterprise Data The Data Asset Challenges When Exploiting and Managing Data The Impact of People on Data Quality Case Studies and Examples Part 2: A Framework for Data Quality Management The Purpose and Scope of Data Quality Management The ISO 8000-61 Approach Data Quality Management Capability Levels ISO 8000-61 Processes The Maturity Journey Part 3: Implementing Data Quality Management Preparing the Organisation for Data Quality Management Implementing Data Quality Management The Human Factor - Ensuring People Support Data Quality Management Conclusions
£28.49
World Scientific Publishing Company Cloud Computing And Big Data Technology
Book Synopsis
£80.00
O'Reilly Media Fundamentals of Data Observability
Book Synopsis
£39.74
Manning Publications Demand Forecasting Best Practices
Book SynopsisMaster the demand forecasting skills you need to decide what resources to acquire, products to produce, and where and how to distribute them. For demand planners, S&OP managers, supply chain leaders, and data scientists. Demand Forecasting Best Practices is a unique step-by-step guide, demonstrating forecasting tools, metrics, and models alongside stakeholder management techniques that work in a live business environment. You will learn how to: Lead a demand planning team to improve forecasting quality while reducing workload Properly define the objectives, granularity, and horizon of your demand planning process Use smart, value-weighted KPIs to track accuracy and bias Spot areas of your process where there is room for improvement Help planners and stakeholders (sales, marketing, finances) add value to your process Identify what kind of data you should be collecting, and how Utilise different types of statistical and machine learning models Follow author Nicolas Vandeput's original five-step framework for demand planning excellence and learn how to tailor it to your own company's needs. You will learn how to optimise demand planning for a more effective supply chain and will soon be delivering accurate predictions that drive major business value. About the technology Demand forecasting is vital for the success of any product supply chain. It allows companies to make better decisions about what resources to acquire, what products to produce, and where and how to distribute them. As an effective demand forecaster, you can help your organisation avoid overproduction, reduce waste, and optimise inventory levels for a real competitive advantage.
£41.72
O'Reilly Media Think Bayes
Book SynopsisIf you know how to program, you're ready to tackle Bayesian statistics. With this book, you'll learn how to solve statistical problems with Python code instead of mathematical formulas, using discrete probability distributions rather than continuous mathematics.
£33.74
Princeton University Press AgentBased and IndividualBased Modeling
Book SynopsisTrade ReviewPraise for the first edition "Biologists . . . have been relatively slow to take advantage of enhanced computing power and unlock the potential of these techniques. This book removes any excuse."—Frontiers of Biogeography"This volume would be an excellent text for an introductory course in modeling as science, or for self-study by a mature researcher interested in learning about this important new way of doing science."—H. Van Dyke Parunak, JASSS"This book represents something I have been [awaiting] for some years now: a good and solid introduction to the field of individual- and agent-based models. . . . The book is not only a practical guide but also serves as a good introduction to the basics of 'healthy' programming. These authors are the right ones to do this as they have a strong background in the philosophical aspects as well as the practical issues of modelling."—Basic and Applied Ecology"Agent-Based and Individual-Based Modeling has the potential to foster an appreciation of the value and place of individual-based models in our field in the next generation of emerging ecologists."—Christopher X. Jon Jensen, Ecology
£49.30
O'Reilly Media Snowflake The Definitive Guide
Book SynopsisSnowflake's ability to eliminate data silos and run workloads from a single platform creates opportunities to democratize data analytics, allowing users within an organization to make data-driven decisions. This clear, comprehensive guide will show you how to build integrated data applications and develop new revenue streams based on data.
£47.99
Pluto Press Audit Culture
Book SynopsisAn anthropological account of how rankings, statistics and numbers are reshaping the world we live inTrade Review'A new and compelling argument for why so many institutions continue to be spellbound by rankings and metrics - despite the cultural carnage they cause in schools, hospitals, universities, corporations and governmental agencies. How can we halt this 'death by audit' craze that has swept through modern society like a deadly virus? In this thought provoking book, the authors develop a radical agenda that will strike fear into number-loving technocrats around the world.' -- Peter Fleming, author of 'Dark Academia: How Universities Die''If you want to go and see a film, choose a university or find the best restaurant, you are likely to consult some sort of ranking ... In this timely work, Shore and Wright ask us to question this contemporary common sense and the market managerialism that lies behind it. Can we imagine a world without audit, one in which our choices are not counted, and trust does not rely on numbers?' -- Professor Martin Parker, University of Bristol Business School'The expansion of audits, indicators and rankings has become a pressing issue for governance and democracy. Cris Shore and Susan Wright build on decades of work to provide a powerful and definitive critical diagnosis of the effects of this audit culture on individuals, public organisations and society. Their book should be essential reading for scholars and policy makers.' -- Michael Power, Professor of Accounting, London School of Economics and Political Science'A visionary book. Two anthropologists piece together a global jigsaw: how for 25 years practices of accountability have been transforming almost every aspect of organisational and personal life. A brilliantly lucid, vigorously argued critique, clear-eyed about the structures that undermine us.' -- Marilyn Strathern, Emeritus Professor of Social Anthropology, University of CambridgeTable of ContentsList of Figures Abbreviations Acknowledgements Series Preface Preface 1. Introduction: Audit Culture and the New World (Dis)Order 2. Rankings as Populist Project: Governing by Numbers and Hollowing out Democracy 3. The Big Four Accountancy Firms and the Evolution of Contemporary Capitalism 4. Global Governance through Standards, Seduction and Soft Power 5. Metrics, Managerialism and Market Making: Unlocking Value in Healthcare 6. Reforming Higher Education: The Kafkaesque Pursuit of ‘World Class’ Status 7. The New Subjects of Audit: Performance Management and Quantified Selves 8. Conclusion: Repurposing Audit – Restoring Trust, Accountability and Democracy Bibliography Index
£17.99
Facet Publishing Halo Data: Understanding and Leveraging the Value
Book SynopsisThe past two decades have seen an explosion both in the volume of data we use, and our understanding of its management.However, while techniques and technology for manipulating data have advanced rapidly in this time, the concepts around the value of our data have not. This lack of progress has made it increasingly difficult for organisations to understand the value in their data, the value of their data and how exploit that value. Halo Data proposes a paradigm shift in methodology for organisations to properly appreciate and leverage the value of their data. Written by an author team with many years’ experience in data strategy, management and technology, the book will first review the current state of our understanding of data. This opening will demonstrate the limitations of this status quo, including a discussion on metadata and its limitations, data monetisation and data-driven business models. Following this, the book will present a new concept and framework for understanding and quantifying value in an organisation’s data and a practical methodology for using this in practice.Ideal for data leaders and executives who are looking to leverage the data at their fingertips.Table of ContentsIntroduction1 Who owns the definitions and terms about data?2 What is metadata?3 Other ideas of data value and monetization4 Value from a different source5 Hello Halo Data6 Getting to know Halo Data7 Early examples of Halo data approaches8 Halo data and data ethics9 Halo data framework10 Halo Data applied risk assessment, regulation, customer, the citizen11 Halo Data and storytelling
£29.33
The Pragmatic Programmers Concurrent Data Processing in Elixir: Fast,
Book SynopsisLearn different ways of writing concurrent code in Elixir and increase your application's performance, without sacrificing scalability or fault-tolerance. Most projects benefit from running background tasks and processing data concurrently, but the world of OTP and various libraries can be challenging. Which Supervisor and what strategy to use? What about GenServer? Maybe you need back-pressure, but is GenStage, Flow, or Broadway a better choice? You will learn everything you need to know to answer these questions, start building highly concurrent applications in no time, and write code that's not only fast, but also resilient to errors and easy to scale. Whether you are building a high-frequency stock trading application or a consumer web app, you need to know how to leverage concurrency to build applications that are fast and efficient. Elixir and the OTP offer a range of powerful tools, and this guide will show you how to choose the best tool for each job, and use it effectively to quickly start building highly concurrent applications. Learn about Tasks, supervision trees, and the different types of Supervisors available to you. Understand why processes and process linking are the building blocks of concurrency in Elixir. Get comfortable with the OTP and use the GenServer behaviour to maintain process state for long-running jobs. Easily scale the number of running processes using the Registry. Handle large volumes of data and traffic spikes with GenStage, using back-pressure to your advantage. Create your first multi-stage data processing pipeline using producer, consumer, and producer-consumer stages. Process large collections with Flow, using MapReduce and more in parallel. Thanks to Broadway, you will see how easy it is to integrate with popular message broker systems, or even existing GenStage producers. Start building the high-performance and fault-tolerant applications Elixir is famous for today. What You Need: You'll need Elixir 1.9+ and Erlang/OTP 22+ installed on a Mac OS X, Linux, or Windows machine.
£30.35
Elsevier Science Relational Database Design and Implementation
Book SynopsisTable of ContentsPart I: Introduction 1. The Database Environment 2. Systems Analysis and Database Requirements Part II: Relational database design theory 3. Why Good Design Matters 4. Entities and Relationships 5. The Relational Data Model 6. Relational Algebra 7. Normalization 8. Database Design and Performance Tuning 9. Codd’s Rules for Relational DBMSs Part III: Relational database design practice 10. Introduction to SQL 11. Using SQL to Implement a Relational Design 12. Using CASE Tools for Database Design 13. Database Design Case Study #1: Mighty-Mite Motors 14. Database Design Case Study #2: East Coast Aquarium 15. Database Design Case Study #3: SmartMart Part IV: Using interactive SQL to manipulate a relational database 16. Simple SQL Retrieval 17. Retrieving Data from More Than One Table 18. Advanced Retrieval Operations 19. Working With Groups of Rows 20. Data Modification 21. Creating Additional Structural Elements Part V: Database implementation issues 22. Concurrency Control 23. Database Security 24. Data Warehousing 25. Data Quality Part VI: Beyond the relational data model 26. XML Support 27. Object-Relational Databases 28. Relational Databases and “Big Data: The Alternative of a NoSQL Solution Part VII: Appendices A. Historical Antecedents B. SQL Programming C. SQL Syntax Summary
£44.64
University of Washington Press Queer Data Studies
Book Synopsis
£29.66
Taylor & Francis Ltd Match Analysis
Book SynopsisMatch analysis is a performance-diagnostic procedure, which can be used to carry out systematic gaming analysis during competition and training. The analysis of team and racket sports, whether in competition, for opponent preparation (match plan), follow-up, or training is nowadays indispensable in many sports games at different levels. This analysis nevertheless presents many open questions and problem areas: Which data should be used? Who manages the data? Who provides whom with which information? How is this information presented, digested, and applied? The more complex and anonymous the data management is, the more commercial, expensive, and uncontrollable information management and provision becomes. Match Analysis: How to Use Data in Professional Sport is the first book to examine this topic through three types of data sets; video, event, and position data and show how to interpret this data and apply the findings for better team and individual sport perfoTrade Review"If you want to build a successful professional sports organization it is essential to pay attention to creating a winning culture. This includes the incorporation and integration of performance analytics and sports science data, which are invaluable tools for strategic competition preparation, game analysis, injury prevention and rehabilitation." Don Smolenski, President, Philadelphia Football Eagles, Superbowl LII Champions "As a professional squash player, I understand first-hand the importance of science and cognitive paradigms involved in world-class sports. If your goal is to be the best, you will have to check out the book on Match Analysis in order to appreciate systematic gaming analysis during competition and practice."Amanda Sobhy, Professional Squash Player (# 1 USA, # 5 World Ranking)"A unique overview of the very latest theoretical and methodological analysis possibilities of the identification of complex patterns and relationships associated with successful performance in 13 kinds of sport."Raul Pelaez Blanco, Sports Analysis, Innovation & Technology Manager, Football Club FC Barcelona"Especially as a former national coach, I know what extremely significant potential a professional match analysis has in performance soccer. The present book is the first to describe fundamental, novel and complex content in a simple and target-oriented way, not only in soccer." Jürgen KlinsmannTable of Contents1. Match Analysis in 20202. History of Match Analysis3. Match Analysis in Practice: Football4. Match Analysis in Practice: Beach VolleyballPart 1: Match Analysis on the Basis of Video Data5. Match Analysis in American Football6. Match Analysis in Basketball7. Match Analysis in Cricket8. Match Analysis in Field Hockey 9. Opponent Analysis in Football 10. Visual Exploratory Scanning in Football11. Match Analysis in Ice Hockey12. Match Analysis in Rugby13. Match Analysis in Squash14. Match Analysis in Table Tennis15. Match Analysis in Team Handball16. Match Analysis in Tennis17. Match Analysis in Volleyball Part 2: Match Analysis on the Basis of Event Data18. KPIs19. Scouting20. Normalizing Kpi’s Based on PossessionPart 3: Match Analysis on the Basis of Position Data21. Model-Based Performance Analysis in Football22. Tactical KPIs in Football23. Physiological KPIs24. KPI: Collective Behavior in Football25. Applying Machine Learning in Football: The Identification of Counterpressing in Football26. KPI in the German Bundesliga 27. Communication of Match Analysis28. Limits of Match Analysis29. Match Analysis in 2030
£36.99
O'Reilly Media Data Science on the Google Cloud Platform
Book SynopsisThis hands-on guide shows data engineers and data scientists how to implement an end-to-end data pipeline with cloud native tools on GCP. Throughout this updated second edition, you'll work through a sample business decision by employing a variety of data science approaches.
£47.99
O'Reilly Media Practical Data Privacy
Book SynopsisThis essential guide will give you solid advice and best practices on breakthrough privacy-enhancing technologies such as encrypted learning and differential privacy--as well as a look at emerging technologies and techniques in the field.
£39.74
O'Reilly Media Trino The Definitive Guide
Book SynopsisIn the second edition of this practical guide, you'll learn how to conduct analytics on data where it lives, whether it's a data lake using Hive, a modern lakehouse with Iceberg or Delta Lake, a different system like Cassandra, Kafka, or SingleStore, or a relational database like PostgreSQL or Oracle.
£47.99
O'Reilly Media Delta Lake Up and Running
Book SynopsisWith the surge in big data and AI, organizations can rapidly create data products. However, the effectiveness of their analytics and machine learning models depends on the data's quality. Delta Lake's open source format offers a robust lakehouse framework over platforms like Amazon S3, ADLS, and GCS.
£39.74
Cambridge University Press Modern Statistics for Modern Biology
Book SynopsisIf you are a biologist and want to get the best out of the powerful methods of modern computational statistics, this is your book. You can visualize and analyze your own data, apply unsupervised and supervised learning, integrate datasets, apply hypothesis testing, and make publication-quality figures using the power of R/Bioconductor and ggplot2. This book will teach you ''cooking from scratch'', from raw data to beautiful illuminating output, as you learn to write your own scripts in the R language and to use advanced statistics packages from CRAN and Bioconductor. It covers a broad range of basic and advanced topics important in the analysis of high-throughput biological data, including principal component analysis and multidimensional scaling, clustering, multiple testing, unsupervised and supervised learning, resampling, the pitfalls of experimental design, and power simulations using Monte Carlo, and it even reaches networks, trees, spatial statistics, image data, and microbial ecology. Using a minimum of mathematical notation, it builds understanding from well-chosen examples, simulation, visualization, and above all hands-on interaction with data and code.Trade Review'This is a gorgeous book, both visually and intellectually, superbly suited for anyone who wants to learn the nuts and bolts of modern computational biology. It can also be a practical, hands-on starting point for life scientists and students who want to break out of 'canned packages' into the more versatile world of R coding. Much richer than the typical statistics textbook, it covers a wide range of topics in machine learning and image processing. The chapter on making high-quality graphics is alone worth the price of the book.' William H. Press, University of Texas, Austin'The book is a timely, comprehensive and practical reference for anyone working with modern quantitative biotechnologies. It can be read at multiple levels. For scientists with a statistics background, it is a thorough review of key methods for design and analysis of high-throughput experiments. For life scientists with a limited exposure to statistics, it offers a series of examples with relevant data and R code. Avoiding buzzwords and hype, the book advocates appropriate statistical practice for reproducible research. I expect it to be as influential for the life sciences community as Modern Applied Statistics with S, by Venables and Ripley or Introduction to Statistical Learning, by James, Witten, Hastie and Tibshirani are for applied statistics.' Olga Vitek, Northeastern University, Boston'Navigating rich data to arrive at sensible insight requires confidence in our biological understanding, informatic ability, statistical sophistication, and skills at effective communication. Fortunately the wisdom and effort of the worldwide research community has been distilled into accessible and rich collections of R and Bioconductor software packages. Holmes and Huber provide a comprehensive guide to navigating modern statistical methods for working with complex, large, and nuanced biological data. The presentation provides a firm conceptual foundation coupled with worked practical examples, extended analysis, and refined discussion of practical and theoretical challenges facing the modern practitioner. This book provides us with the confidence and tools necessary for the analysis and comprehension of modern biological data using modern statistical methods.' Martin Morgan, Roswell Park Comprehensive Cancer Center, leader of the Bioconductor project'Holmes and Huber take an integrated approach to presenting the key statistical concepts and methods needed for the analysis of biological data. Specifically, they do a wonderful job of building these foundations in the context of modern computational tools, genuine scientific questions, and real-world datasets. The code showcases many of the newest features of R and its dynamic package ecosystem, such as using ggplot2 for visualization and dplyr for data manipulation.' Jenny Bryan, RStudio and University of British Columbia'... the book is extremely readable and engaging, it explains complicated concepts in simple terms, and uses illuminating graphics and examples. Any researcher who wants to learn or teach up-to-date statistics to biologists will find this an essential volume for modern teaching of modern statistics to modern biologists.' Noa Pinter-Wollman, The Quarterly Review of BiologyTable of ContentsIntroduction; 1. Generative models for discrete data; 2. Statistical modeling; 3. High-quality graphics in R; 4. Mixture models; 5. Clustering; 6. Testing; 7. Multivariate analysis; 8. High-throughput count data; 9. Multivariate methods for heterogeneous data; 10. Networks and trees; 11. Image data; 12. Supervised learning; 13. Design of high-throughput experiments and their analyses; Statistical concordance; Bibliography; Index.
£47.49
Cambridge University Press Optimization for Data Analysis
Book SynopsisOptimization techniques are at the core of data science, including data analysis and machine learning. An understanding of basic optimization techniques and their fundamental properties provides important grounding for students, researchers, and practitioners in these areas. This text covers the fundamentals of optimization algorithms in a compact, self-contained way, focusing on the techniques most relevant to data science. An introductory chapter demonstrates that many standard problems in data science can be formulated as optimization problems. Next, many fundamental methods in optimization are described and analyzed, including: gradient and accelerated gradient methods for unconstrained optimization of smooth (especially convex) functions; the stochastic gradient method, a workhorse algorithm in machine learning; the coordinate descent approach; several key algorithms for constrained optimization problems; algorithms for minimizing nonsmooth functions arising in data science; foundTrade Review'This delightful compact tome gives the reader all the results they should have in their pocket to contribute to optimization and statistical learning. With the clean, elegant derivations of many of the foundational optimization methods underlying modern large-scale data analysis, everyone from students just getting started to researchers knowing this book inside and out will be well-positioned for both using the algorithms and developing new ones for machine learning, optimization, and statistics.' John C. Duchi, Stanford University'Optimization algorithms play a vital role in the rapidly evolving field of machine learning, as well as in signal processing, statistics and control. Numerical optimization is a vast field, however, and a student wishing to learn the methods required in the world of data science could easily get lost in the literature. This book does a superb job of presenting the most important algorithms, providing both their mathematical foundations and lucid motivations for their development. Written by two of the foremost experts in the field, this book gently guides a reader without prior knowledge of optimization towards the methods and concepts that are central in modern data science applications.' Jorge Nocedal, Northwestern University'This timely introductory book gives a rigorous view of continuous optimization techniques which are being used in machine learning. It is an excellent resource for those who are interested in understanding the mathematical concepts behind commonly used machine learning techniques.' Shai Shalev-Shwartz, Hebrew University of Jerusalem'This textbook is a much-needed exposition of optimization techniques, presented with conciseness and precision, with emphasis on topics most relevant for data science and machine learning applications. I imagine that this book will be immensely popular in university courses across the globe, and become a standard reference used by researchers in the area.' Amitabh Basu, Johns Hopkins UniversityTable of Contents1. Introduction; 2. Foundations of smooth optimization; 3. Descent methods; 4. Gradient methods using momentum; 5. Stochastic gradient; 6. Coordinate descent; 7. First-order methods for constrained optimization; 8. Nonsmooth functions and subgradients; 9. Nonsmooth optimization methods; 10. Duality and algorithms; 11. Differentiation and adjoints.
£37.99
John Wiley & Sons Inc Data Quality
Book SynopsisDiscover how to achieve business goals by relying on high-quality, robust data In Data Quality: Empowering Businesses with Analytics and AI, veteran data and analytics professional delivers a practical and hands-on discussion on how to accelerate business results using high-quality data. In the book, you'll learn techniques to define and assess data quality, discover how to ensure that your firm's data collection practices avoid common pitfalls and deficiencies, improve the level of data quality in the business, and guarantee that the resulting data is useful for powering high-level analytics and AI applications. The author shows you how to: Profile for data quality, including the appropriate techniques, criteria, and KPIs Identify the root causes of data quality issues in the business apart from discussing the 16 common root causes that degrade data quality in the organization. Formulate the reference architecture for data quality, inTable of ContentsForeword by Bill Inmon Preface About the Book Quality Principles Applied in This Book Organization of the Book Who Should Read This Book? References Acknowledgments Define Phase Chapter 1: Introduction Introduction Data, Analytics, AI, and Business Performance Data as a Business Asset or Liability Data Governance, Data Management, and Data Quality Leadership Commitment to Data Quality Key Takeaways Conclusion References Chapter 2: Business Data Introduction Data in Business Telemetry Data Purpose of Data in Business Business Data Views Key Characteristics of Business Data Critical Data Elements (CDE) Key Takeaways Conclusion References Chapter 3: Data Quality in Business Introduction Data Quality Dimensions Context in Data Quality Consequences and Costs of Poor Data Quality Data Depreciation and Its Factors Data in IT Systems Data Quality and Trusted Information Key Takeaways Conclusion References Analyze Phase Chapter 4: Causes for Poor Data Quality Introduction Data Quality RCA Techniques Typical Causes of Poor Data Quality Key Takeaways Conclusion References Chapter 5: Data Lifecycle and Lineage Introduction Business-Enabled DLC Stages IT Business-Enabled DLC Stages Data Lineage Key Takeaways Conclusion References Chapter 6: Profiling for Data Quality Introduction Criteria for Data Profiling Data Profiling Techniques for Measures of Centrality Data Profiling Techniques for Measures of Variation Integrating Centrality and Variation KPIs Key Takeaways Conclusion References Realize Phase Chapter 7: Reference Architecture for Data Quality Introduction Options to Remediate Data Quality DataOps Data Product Data Fabric and Data Mesh Data Enrichment Key Takeaways Conclusion References Chapter 8: Best Practices to Realize Data Quality Introduction Overview of Best Practices BP 1: Identify the Business KPIs and the Ownership of These KPIs and the Pertinent Data BP 2: Build and Improve the Data Culture and Literacy in the Organization BP 3: Define the Current and Desired state of Data Quality BP 4: Follow the Minimalistic Approach to Data Capture BP 5: Select and Define the Data Attributes for Data Quality BP 6: Capture and Manage Critical Data with Data Standards in MDM Systems Key Takeaways Conclusion References Chapter 9: Best Practices to Realize Data Quality Introduction BP 7: Automate the Integration of Critical Data Elements BP 8: Define the SoR and Securely Capture Transactional Data in the SoR/OLTP System BP 9: Build and Manage Robust Data Integration Capabilities BP 10: Distribute Data Sourcing and Insight Consumption Key Takeaways Conclusion References Sustain Phase Chapter 10: Data Governance Introduction Data Governance Principles Data Governance Design Components Implementing the Data Governance Program Data Observability Data Compliance – ISO 27001 and SOC2 Key Takeaways Conclusion References Chapter 11: Protecting Data Introduction Data Classification Data Safety Data Security Key Takeaways Conclusion References Chapter 12: Data Ethics Introduction Data Ethics Importance of Data Ethics Principles of Data Ethics Model Drift in Data Ethics Data Privacy Managing Data Ethically Key Takeaways Conclusion References Appendix 1: Abbreviations and Acronyms Appendix 2: Glossary Appendix 3: Data Literacy Competencies About the Author Index
£24.79
O'Reilly Media Cloud Native DataCenter Networking
Book SynopsisWhether you're pursuing a multitenant private cloud, a network for running machine learning, or an enterprise data center, author Dinesh Dutt takes you through the steps necessary to design a data center that's affordable, high capacity, easy to manage, agile, and reliable.
£39.74
O'Reilly Media Mastering Shiny
Book SynopsisHadley Wickham from RStudio shows data scientists, data analysts, statisticians, and scientific researchers with no knowledge of HTML, CSS, or JavaScript how to create rich web apps from R.
£47.99
O'Reilly Media Machine Learning Pocket Reference
Book SynopsisWith detailed notes, tables, and examples, this handy reference will help you navigate the basics of structured machine learning. Author Matt Harrison delivers a valuable guide that you can use for additional support during training and as a convenient resource when you dive into your next machine learning project.
£20.39
Apple Academic Press Inc. Big Data Analytics: Harnessing Data for New
Book SynopsisThis volume explores the diverse applications of advanced tools and technologies of the emerging field of big data and their evidential value in business. It examines the role of analytics tools and methods of using big data in strengthening businesses to meet today’s information challenges and shows how businesses can adapt big data for effective businesses practices.This volume shows how big data and the use of data analytics is being effectively adopted more frequently, especially in companies that are looking for new methods to develop smarter capabilities and tackle challenges in dynamic processes. Many illustrative case studies are presented that highlight how companies in every sector are now focusing on harnessing data to create a new way of doing business.Table of ContentsPART I: BIG DATA: OPPORTUNITIES AND CHALLENGES 1. Big Data: An Overview 2. Big Data Between Pros and Cons 3. Big Data Uses and the Challenges They Face 4. Twitter’s Big Data Analysis Using RStudio 5. Big Data for Business Growth in Small and Medium Enterprises (SMEs) PART II: BIG DATA AND BUSINESSES’ DECISION-MAKING PROCESS? 6. The Role of Big Data in Strategic Decision-Making 7. Data Mining and Its Contribution to Decision-Making in Business Organizations 8. The Strategic Role of Big Data Analytics in the Decision-Making Process 9. The Role of the Information System in Making Strategic Decisions in the Economic Institution: Case Study of Baticic in Ain Defla, Algeria 10. The Role of Big Data Analysis and Strategic Vigilance in Decision-Making 11. Big Data Analysis and Its Role in Making Strategic Decisions PART III: BIG DATA APPLICATIONS: BUSINESS EXAMPLES 12. The Farthest Planning of Big Data in the Light of Information Technology: "Smart Cities: A World to Yet" 13. Blockchain Technology as a Method Based on Organizing Big Data to Build Smart Cities: The Dubai Experience 14. The Uses of Big Data in the Health Sector 15. The Role of Big Data in Avoiding the Banking Default in Algeria (The Possibility of Upgrading the Preventive Centers of the Bank of Algeria as a Source of Big Data) 16. Marketing Information System as a Marketing Crisis Management Mechanism through Big Data Analytics: A Case Study of Algeria Telecom in Bouira 17. Perspectives of Big Data Analytics’ Integration in the Business Strategy of Amazon, Inc. 18. The Hospital Information System: A Fundamental Lever for Performance in Hospitals PART IV: BIG DATA AND SUSTAINABLE DEVELOPMENT 19. Big Data Analysis and Sustainable Development 20. Big Data for Sustainable Development Goals: Theoretical Approach 21. Using Big Data in Official Statistics for Sustainable Development 22. The Initiatives of the UN to Improve the Quality of Big Data and Support the Sustainable Development Goals for 2030 23. Big Data and It Role in Achieving the Sustainable Development Goals: Experiences of Leading Organizations
£132.05
Pelagic Publishing An Introduction to R: Data Analysis and
Book SynopsisThe modern world is awash with data. The R Project is a statistical environment and programming language that can help to make sense of it all. A huge open-source project, R has become enormously popular because of its power and flexibility. With R you can organise, analyse and visualise data. This clear and methodical book will help you learn how to use R from the ground up, giving you a start in the world of data science. Learning about data is important in many academic and business settings, and R offers a potent and adaptable programming toolbox. The book covers a range of topics, including: importing/exporting data, summarising data, visualising data, managing and manipulating data objects, data analysis (regression, ANOVA and association among others) and programming functions. Regardless of your background or specialty, you'll find this book the perfect primer on data analysis, data visualisation and data management, and a springboard for further exploration.Table of Contents1. A brief introduction to R 2. Basic math 3. Introduction to R objects 4. Making and importing data objects 5. Managing and exporting data objects 6. R object types and their properties 7. Working with data objects 8. Manipulating data objects 9. Summarizing data 10. Tabulation 11. Graphics: basic charts 12. Graphics: adding to plots 13. Graphics: advanced methods 14. Analyze data: statistical analyses 15. Programming tools Appendix Index
£35.00
De Gruyter Big Data Data Mining and Data Science
Book Synopsis
£131.85
BPB Publications Modern Data Architecture in AI
£28.49
De Gruyter Data Fabric Architectures: Web-Driven
Book SynopsisThe immense increase on the size and type of real time data generated across various edge computing platform results in unstructured databases and data silos. This edited book gathers together an international set of researchers to investigate the possibilities offered by data-fabric solutions; the volume focuses in particular on data architectures and on semantic changes in future data landscapes.
£105.00
Transcript Verlag Interrogating Datafication – Towards a Praxeology
Book SynopsisWhat constitutes a data practice and how do contemporary digital media technologies reconfigure our understanding of practices in general? Autonomously acting media, distributed digital infrastructures, and sensor-based media environments challenge the conditions of accounting for data practices both theoretically and empirically. Which forms of cooperation are constituted in and by data practices? And how are human and nonhuman agencies distributed and interrelated in data-saturated environments? The volume collects theoretical, empirical, and historiographical contributions from a range of international scholars to shed light on the current shift from media to data practices.
£35.99
Manning Publications Time Series Forecasting in Python
Book SynopsisBuild predictive models from time-based patterns in your data. Master statistical models including new deep learning approaches for time series forecasting. In Time Series Forecasting in Python you will learn how to: Recognize a time series forecasting problem and build a performant predictive model Create univariate forecasting models that account for seasonal effects and external variables Build multivariate forecasting models to predict many time series at once Leverage large datasets by using deep learning for forecasting time series Automate the forecasting process DESCRIPTION Time Series Forecasting in Python teaches you to build powerful predictive models from time-based data. Every model you create is relevant, useful, and easy to implement with Python. You'll explore interesting real-world datasets like Google's daily stock price and economic data for the USA, quickly progressing from the basics to developing large-scale models that use deep learning tools like TensorFlow.Time Series Forecasting in Python teaches you to apply time series forecasting and get immediate, meaningful predictions. You'll learn both traditional statistical and new deep learning models for time series forecasting, all fully illustrated with Python source code. Time Series Forecasting in Python teaches you to build powerful predictive models from time-based data. Every model you create is relevant, useful, and easy to implement with Python. You'll explore interesting real-world datasets like Google's daily stock price and economic data for the USA, quickly progressing from the basics to developing large-scale models that use deep learning tools like TensorFlow. about the technology Time series forecasting reveals hidden trends and makes predictions about the future from your data. This powerful technique has proven incredibly valuable across multiple fields—from tracking business metrics, to healthcare and the sciences. Modern Python libraries and powerful deep learning tools have opened up new methods and utilities for making practical time series forecasts. about the book Time Series Forecasting in Python teaches you to apply time series forecasting and get immediate, meaningful predictions. You'll learn both traditional statistical and new deep learning models for time series forecasting, all fully illustrated with Python source code. Test your skills with hands-on projects for forecasting air travel, volume of drug prescriptions, and the earnings of Johnson & Johnson. By the time you're done, you'll be ready to build accurate and insightful forecasting models with tools from the Python ecosystem.Table of Contentstable of contents detailed TOC PART 1: TIME WAITS FOR NO ONE READ IN LIVEBOOK 1UNDERSTANDING TIME SERIES FORECASTING READ IN LIVEBOOK 2A NAÏVE PREDICTION OF THE FUTURE READ IN LIVEBOOK 3GOING ON A RANDOM WALK PART 2: FORECASTING WITH STATISTICAL MODELS READ IN LIVEBOOK 4MODELING A MOVING AVERAGE PROCESS READ IN LIVEBOOK 5MODELING AN AUTOREGRESSIVE PROCESS READ IN LIVEBOOK 6MODELING COMPLEX TIME SERIES READ IN LIVEBOOK 7FORECASTING NON-STATIONARY TIME SERIES READ IN LIVEBOOK 8ACCOUNTING FOR SEASONALITY READ IN LIVEBOOK 9ADDING EXTERNAL VARIABLES TO OUR MODEL READ IN LIVEBOOK 10FORECASTING MULTIPLE TIME SERIES READ IN LIVEBOOK 11CAPSTONE: FORECASTING THE NUMBER OF ANTIDIABETIC DRUG PRESCRIPTIONS IN AUSTRALIA PART 3: LARGE-SCALE FORECASTING WITH DEEP LEARNING READ IN LIVEBOOK 12INTRODUCING DEEP LEARNING FOR TIME SERIES FORECASTING READ IN LIVEBOOK 13DATA WINDOWING AND CREATING BASELINES FOR DEEP LEARNING READ IN LIVEBOOK 14BABY STEPS WITH DEEP LEARNING READ IN LIVEBOOK 15REMEMBERING THE PAST WITH LSTM READ IN LIVEBOOK 16FILTERING OUR TIME SERIES WITH CNN READ IN LIVEBOOK 17USING PREDICTIONS TO MAKE MORE PREDICTIONS READ IN LIVEBOOK 18CAPSTONE: FORECASTING THE ELECTRIC POWER CONSUMPTION OF A HOUSEHOLD PART 4: AUTOMATING FORECASTING AT SCALE READ IN LIVEBOOK 19AUTOMATING TIME SERIES FORECASTING WITH PROPHET READ IN LIVEBOOK 20CAPSTONE: FORECASTING THE MONTHLY AVERAGE RETAIL PRICE OF STEAK IN CANADA 21 GOING ABOVE AND BEYOND APPENDIX APPENDIX A: INSTALLATION INSTRUCTIONS
£43.69
Manning Publications DataDriven Cybersecurity
£43.95
