Data capture and analysis Books

Book SynopsisThis comprehensive and authoritative guide will teach you the DAX language for business intelligence, data modeling, and analytics. Leading Microsoft BI consultants Marco Russo and Alberto Ferrari help you master everything from table functions through advanced code and model optimization. You’ll learn exactly what happens under the hood when you run a DAX expression, how DAX behaves differently from other languages, and how to use this knowledge to write fast, robust code. If you want to leverage all of DAX’s remarkable power and flexibility, this no-compromise “deep dive” is exactly what you need. Perform powerful data analysis with DAX for Microsoft SQL Server Analysis Services, Excel, and Power BI Master core DAX concepts, including calculated columns, measures, and error handling Understand evaluation contexts and the CALCULATE and CALCULATETABLE functions Perform time-based calculations: YTD, MTD, previous year, working days, and more Work with expanded tables, complex functions, and elaborate DAX expressions Perform calculations over hierarchies, including parent/child hierarchies Use DAX to express diverse and unusual relationships Measure DAX query performance with SQL Server Profiler and DAX Studio Table of Contents Introduction Chapter 1: What is DAX? Chapter 2: Introducing DAX Chapter 3: Using basic table functions Chapter 4: Understanding evaluation contexts Chapter 5: Understanding CALCULATE and CALCULATETABLE Chapter 6: DAX examples Chapter 7: Time intelligence calculations Chapter 8: Statistical functions Chapter 9: Advanced table functions Chapter 10: Advanced evaluation context Chapter 11: Handling hierarchies Chapter 12: Advanced relationships Chapter 13: The VertiPaq engine Chapter 14: Optimizing data models Chapter 15: Analyzing DAX query plans Chapter 16: Optimizing DAX Index

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Book SynopsisThis approachable text teaches all the concepts, the context, and the code needed to understand deep learning. Suitable for students and professionals, the book doesn't require any previous background in machine learning or deep learning. Interactive examples feature throughout, with runnable code and executable Jupyter notebooks available online.Trade Review'In less than a decade, the AI revolution has swept from research labs to broad industries to every corner of our daily life. Dive into Deep Learning is an excellent text on deep learning and deserves attention from anyone who wants to learn why deep learning has ignited the AI revolution: the most powerful technology force of our time.' Jensen Huang, Founder and CEO, NVIDIA'This is a timely, fascinating book, providing not only a comprehensive overview of deep learning principles but also detailed algorithms with hands-on programming code, and moreover, a state-of-the-art introduction to deep learning in computer vision and natural language processing. Dive into this book if you want to dive into deep learning!' Jiawei Han, Michael Aiken Chair Professor, University of Illinois at Urbana-Champaign'This is a highly welcome addition to the machine learning literature, with a focus on hands-on experience implemented via the integration of Jupyter notebooks. Students of deep learning should find this invaluable to become proficient in this field.' Bernhard Schölkopf,, Director, Max Planck Institute for Intelligent Systems'Dive into Deep Learning strikes an excellent balance between hands-on learning and in-depth explanation. I've used it in my deep learning course and recommend it to anyone who wants to develop a thorough and practical understanding of deep learning.' Colin Raffel, Assistant Professor, University of North Carolina, Chapel HillTable of ContentsInstallation; Notation; 1. Introduction; 2. Preliminaries; 3. Linear neural networks for regression; 4. Linear neural networks for classification; 5. Multilayer perceptrons; 6. Builders guide; 7. Convolutional neural networks; 8. Modern convolutional neural networks; 9. Recurrent neural networks; 10. Modern recurrent neural networks; 11. Attention mechanisms and transformers; Appendix. Tools for deep learning; Bibliography; Index.

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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

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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.

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Book SynopsisTrade Review"John Ladley’s latest book is the second edition of his Data Governance book, first published in 2012. If you know the first edition of this book, you’ll certainly appreciate this updated second edition. Reading this book will provide you with a comprehensive overview of why data governance is needed, how to design, initiate, and execute a data governance program, and how to keep the program sustainable. There is a ton of new content in this second edition, including new case studies, updated industry details, and updated coverage of the available data governance tools that can help.The book will be useful to you whether you are a novice or a seasoned professional. At the heart of this book is the framework that Ladley communicates that you can follow to build and maintain successful data governance at your organization. In combination with the use cases that he walks through in the book, you have a powerful guide for launching your data governance program. Useful for both small and large organizations, be sure to pick up a copy of this book if you are charged with any aspect of data management and data governance within your shop." --Data and Technology TodayTable of Contents1. Prologue – An Executive Overview 2. Introduction 3. Data Literacy and Concepts 4 Overview – A Day in the Life of a data governance program and its capabilities 5. The data governance business case 6. Process overview for deploying data governance 7. Scope and initiation 8. Assess 9. Vision 10. Align and business value 11. Functional design 12. Governing framework design 13. Road map 14. Rollout and sustain 15. Data governance artifacts and tools 16. Final remarks Appendix Data Governance Charters Template Data Governance Orientation and ongoing knowledge transfer template Stakeholder Analysis Leadership Alignment Assessment Communications Plan Training Plan Example Post-rollout Checklist

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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.

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Book SynopsisStatistical methods are a key tool for all scientists working with data, but learning the basics continues to challenge successive generations of students. This accessible textbook provides an up-to-date introduction to the classical techniques and modern extensions of linear model analysis-one of the most useful approaches for investigating scientific data in the life and environmental sciences. While some of the foundational analyses (e.g. t tests, regression, ANOVA) are as useful now as ever, best practice moves on and there are many new general developments that offer great potential. The book emphasizes an estimation-based approach that takes account of recent criticisms of over-use of probability values and introduces the alternative approach that uses information criteria.This new edition includes the latest advances in R and related software and has been thoroughly road-tested over the last decade to create a proven textbook that teaches linear and generalized linear model analTrade ReviewReview from previous edition The book is suitable for undergraduate and graduate students, researchers and practitioners in biological sciences. I found it refreshing and worthy of wide use. * Basil Jarvis, The Biologist *[T]his book is of great interest ... it is important to evaluate its value as a teaching tool for R for biologists. ... [T]he book's strength is that it takes an applied scientist through the necessary basic statistics, and shows step by step how to work with real data. The New Statistics with R is, furthermore, a great textbook for computer exercise sessions in any introductory statistical class (especially for the life sciences). With its help, one should be able to design a very attractive course for both applied and more theoretical students. * Krzysztof Bartoszek, Systematic Biology *... overall the book gives useful, ecumenical, and reliable statistical advice. I would recommend it for courses that are trying to equip students who already know elementary statistics with the basic tools they need to understand and perform analyses of real, messy data. * Ben Bolker, Quarterly Review of Biology *Table of Contents1: Introduction 2: Motivation 3: Description 4: Reproducible Research 5: Estimation 6: Linear Models 7: Regression 8: Prediction 9: Testing 10: Intervals 11: Analysis of Variance 12: Factorial Designs 13: Analysis of Covariance 14: Linear Model Complexities 15: Generalized Linear Models 16: GLMs for Count Data 17: Binomial GLMs 18: GLMs for Binary Data 19: Conclusions 20: A Very Short Introduction to R

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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.

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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

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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.

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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

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Book SynopsisCommon data science algorithms and predictive analytics tools treat customer behavioral data, such as clicks on a website or purchases in a supermarket, the same as any other data. Instead, this practical guide introduces powerful methods specifically tailored for behavioral data analysis.

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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

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Book SynopsisIdeal for developers and data scientists, this book gets you creating packages ASAP, then shows you how to get progressively better over time. You'll learn to focus on what you want your package to do, rather than thinking about package structure.

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Book SynopsisThe R language is widely used by statisticians for data analysis, and the popularity of R programming has therefore increased substantially in recent years. The emerging Internet of Things (IoT) gathers increasing amounts of data that can be analyzed to gain useful insights into trends.R for Data Analysis in easy steps, 2nd edition has an easy-to-follow style that will appeal to anyone who wants to produce graphic visualizations to gain insights from gathered data. The book begins by explaining core programming principles of the R programming language, which stores data in vectors from which simple graphs can be plotted. Next, it describes how to create matrices to store and manipulate data from which graphs can be plotted to provide better insights. This book then demonstrates how to create data frames from imported data sets, and how to employ the Grammar of Graphics to produce advanced visualizations that can best illustrate useful insights from your data.R for Data Analysis in easy steps, 2nd edition contains separate chapters on the major features of the R programming language. There are complete example programs that demonstrate how to create Line graphs, Bar charts, Histograms, Scatter graphs, Box plots, and more. The code for each R script is listed, together with screenshots that illustrate the actual output when that script has been executed. The free, downloadable example R code is provided for clearer understanding. By the end of this book you will have gained a sound understanding of R programming, and be able to write your own scripts that can be executed to produce graphic visualizations for data analysis. You need have no previous knowledge of any programming language, so it''s ideal for the newcomer to computer programming.Updated for the latest version of R.

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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

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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.

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Book SynopsisThis book lays out the roles everyone, up and down the organisation chart, can and must play to ensure that data is up to the demands of its use, in day-in, day-out work, decision-making, planning, and analytics. By now, everyone knows that bad data extorts an enormous toll, adding huge (though often hidden) costs, and making it more difficult to make good decisions and leverage advanced analyses. While the problems are pervasive and insidious, they are also solvable! As Tom Redman, the Data Doc explains, the secret lies in getting the right people in the right roles to get in front of the management and social issues that lead to bad data in the first place. Everyone should see himself or herself in this book. We are all both data customers and data creators -- after all, we use data created by others and create data used by others. And all of us must step up to these roles. As data customers, we must clarify our most important needs and communicate them to data creators. As data creators, we must strive to meet those needs by finding and eliminating the root causes of error. This book proposes new roles for data professionals as: embedded data managers, in helping data customers and creators complete their work, DQ team leads, in connecting customers and creators, pulling the entire program together, and training people on their new roles, data maestros, in providing deep expertise on the really tough problems, chief data architects, in establishing common data definitions, and technologists, in increasing scale and decreasing unit cost. The book introduces a new role, the data provocateur, the motive force in attacking data quality properly! This book urges everyone to unleash their inner provocateur. Finally, it crystallises what senior leaders must do if their entire organisations are to enjoy the benefits of high-quality data!

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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

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Book SynopsisTable of Contents1. Introduction2. Providing Structure to Unstructured Data3. Identification, Deidentification, and Reidentification4. Metadata, Semantics, and Triples5. Classifications and Ontologies6. Introspection7. Data Integration and Software Interoperability8. Immutability and Immortality9. Assessing the Adequacy of a Big Data Resource10. Measurement11. Indispensable Tips for Fast and Simple Big Data Analysis12. Finding the Clues in Large Collections of Data13. Using Random Numbers to Bring Your Big Data Analytic Problems Down to Size14. Special Considerations in Big Data Analysis15. Big Data Failures and How to Avoid (Some of) Them16. Legalities17. Data Sharing18. Data Reanalysis: Much More Important than Analysis19. Repurposing Big Data

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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

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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

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Book SynopsisInterest in applying analytics, machine learning, and artificial intelligence to sales and marketing has grown dramatically, with no signs of slowing down. This book provides essential guidance to apply advanced analytics and data mining techniques to real-world business applications. The foundation of this text is the author's 20-plus years of developing and delivering big data and artificial intelligence solutions across multiple industries: financial services, pharmaceuticals, consumer packaged goods, media, and retail. He provides guidelines and summarized cases for those studying or working in the fields of data science, data engineering, and business analytics. The book also offers a distinctive style: a series of essays, each of which summarizes a critical lesson or provides a step-by-step business process, with specific examples of successes and failures.Sales and marketing executives, project managers, business and engineering professionals, and graduate studeTrade Review"This book should be considered required reading for analysts, managers and executives seeking to drive business performance and gain competitive advantage through data-driven strategy and decision making. Dr. Haimowitz has distilled his considerable experience into a practical how-to guide that is sure to become a fundamental reference text for the modern analytics practitioner or manager, regardless of industry." Zaheer Benjamin, Group Leader in Business Intelligence & Analytics across Life Sciences, Broadcasting, and Major League SportsThis Data Analytics for Business written by Dr. Ira Haimowitz is an experience laden, practical book with real world examples for data analyst, marketing and business executives. The book is well written and provides a good background in design of the big data organization, data analytical applications, and implementation and delivery of the projects in easy-to- read format. The book is a must read for anyone who is interested in data analytics related to sales, marketing and strategy. Thani Jambulingam Ph.D, Professor, Department of Pharmaceutical and Healthcare Marketing, Haub School of Business, Saint Joseph’s University, Philadelphia Table of ContentsOrganizational Design Principles 1. Linking Business Challenges to Big Data Solutions 2. Selling the Big Data Analytics Initiative 3. Organizational Structures for Advanced Analytics 4. Lessons Learned Managing Big Data Departments Analytics Business Applications 5. Segmentation: Categorizing Your Customers 6. Targeting: Getting it "Right" 7. Campaign Measurement with Learning Objectives 8. Strategic Text Mining 9. Predictive Modeling for Business Implementation and Delivery 10. Privacy Considerations for Big Data Analytics 11. Delivering Results with Actionable Insights 12. Scalability and Long Term Success

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Book SynopsisThe primary goal of this text is a practical one. Equipping students with enough knowledge and creating an independent research platform, the author strives to prepare students for professional careers. Providing students with a marketable skill set requires topics from many areas of optimization. The initial goal of this text is to develop a marketable skill set for mathematics majors as well as for students of engineering, computer science, economics, statistics, and business. Optimization reaches into many different fields.This text provides a balance where one is needed. Mathematics optimization books are often too heavy on theory without enough applications; texts aimed at business students are often strong on applications, but weak on math. The book represents an attempt at overcoming this imbalance for all students taking such a course.The book contains many practical applications but also explains the mathematics behind the techniques, including stating definitTable of Contents1. 1. Preamble. 2. The Language of Optimization. 3. Computational Complexity. 4. Algebra Review. 5. Matrix Factorization. 6. Linear Programming. 7. Sensitivity Analysis. 8. Integer Linear Programing. 9. Calculus Review. 10. A Calculus Approach to Nonlinear Programming. 11. Constrained Nonlinear Programming: Lagrange Multipliers and the KKT Conditions. 12. Optimization involving Quadratic Forms. 13. Iterative Methods. 14. Derivative-Free Methods. 15. Search Algorithms. 16. Important Sets for Optimization. 17. The Fundamental Theorem of Linear Programming. 18. Convex Functions. 19. Convex Optimization. 20. An Introduction to Combinatorics. 21. An Introduction to Graph Theory. 22. Network Flows. 23. Minimum-Weight Spanning Trees and Shortest Paths. 24. Network Modeling and the Transshipment Problem. 25. The Traveling Salesperson Problem. Probability. 27. Regression Analysis via Least Squares. 28. Forecasting. 29. Introduction to Machine Learning.

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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 bookâs experiential approach, combined with chapter objectives, summaries, and review questions, will engage readers, deepening their learning by helping them to think outside the box.Filled with engaging, interactive exercises and interesting insights from industry experts, this book will appeal to undergraduate and postgraduate students of digital marketing, online marketing, and analytics.Online support materials for this book include an instructorâs manual, test bank, and PowerPoint slides.Table 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

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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

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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

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Book SynopsisThe kernel functions methodology described here provides a powerful and unified framework for disciplines ranging from neural networks and pattern recognition to machine learning and data mining. This book provides practitioners with a large toolkit of algorithms, kernels and solutions ready to be implemented, suitable for standard pattern discovery problems.Trade Review'Kernel methods form an important aspect of modern pattern analysis, and this book gives a lively and timely account of such methods. … if you want to get a good idea of the current research in this field, this book cannot be ignored.' SIAM Review'… the book provides an excellent overview of this growing field. I highly recommend it to those who are interested in pattern analysis and machine learning, and especailly to those who want to apply kernel-based methods to text analysis and bioinformatics problems.' Computing Reviews' … I enjoyed reading this book and am happy about is addition to my library as it is a valuable practitioner's reference. I especially liked the presentation of kernel-based pattern analysis algorithms in terse mathematical steps clearly identifying input data, output data, and steps of the process. The accompanying Matlab code or pseudocode is al extremely useful.' IAPR NewsletterTable of ContentsPreface; Part I. Basic Concepts: 1. Pattern analysis; 2. Kernel methods: an overview; 3. Properties of kernels; 4. Detecting stable patterns; Part II. Pattern Analysis Algorithms: 5. Elementary algorithms in feature space; 6. Pattern analysis using eigen-decompositions; 7. Pattern analysis using convex optimisation; 8. Ranking, clustering and data visualisation; Part III. Constructing Kernels: 9. Basic kernels and kernel types; 10. Kernels for text; 11. Kernels for structured data: strings, trees, etc.; 12. Kernels from generative models; Appendix A: proofs omitted from the main text; Appendix B: notational conventions; Appendix C: list of pattern analysis methods; Appendix D: list of kernels; References; Index.

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Book SynopsisStatistical graphing was born in the seventeenth century as a scientific tool, but it quickly escaped all disciplinary bounds. Today graphics are ubiquitous in daily life. Michael Friendly and Howard Wainer detail the history of data visualization and argue that it has not only helped us solve problems, but it has also changed the way we think.Trade ReviewThe invention of graphs and charts was a much quieter affair than that of the telescope, but these tools have done just as much to change how and what we see. -- Hannah Fry * New Yorker *An indispensable account of how important practitioners of data visualizations write the history of their field. -- Crystal Lee * Information & Culture *We live in an era of data dependence—never before have graphic representations of data been as essential and sought after as at this moment…There has not been a publication of this scope on the evolution of graphic representation of qualitative and quantitative data since Funkhouser’s work…Scholars, practitioners, lovers of statistics and data visualization, and anyone interested in understanding the methods and techniques of today will benefit from understanding the innovations that brought us to where we are. -- María del Mar Navarro * Journal of Design, Economics, and Innovation *A thoughtful and well-written introduction to the world of data visualization and its history. -- Bill Satzer * MAA Reviews *An intellectually fascinating book…The audience for this book is wide. It would be useful to professionals and to professors in many departments such as psychology, sociology, economics, biology, physics, and any department that uses graphs to display quantitative information. It is a book to broaden your knowledge and offer interesting asides for lectures and meetings…Consult it frequently to learn of the stories of the developers of the many graphic methods we use today. -- Malcolm James Ree * Personnel Psychology *A marvel of research scholarship…This is the sort of book that one can browse and sample in bite-size chunks as the mood seizes, encountering curious delights while doing so. -- Bert Gunter * Significance *A masterly study of graphic innovations, their context, and their scientific use. This brilliant book, without equivalent, is an indispensable read. -- Gilles Palsky, coauthor of An Atlas of Geographical WondersFriendly and Wainer are the Watson and Crick of statistical graphics, showing us the history of the DNA structure that is the code of life for innovative visualizations. -- Ben Shneiderman, founder of the Human-Computer Interaction Lab, University of MarylandData expertise is a fundamental prerequisite for success in our digital age. But exactly how, and when, have we learned to draw conclusions from data? For decades, Michael Friendly and Howard Wainer have been studying how data has informed decision-making, through visualization and statistical analysis. Replete with mesmerizing visual examples, this book is an eye-opening distillation of their research. -- Sandra Rendgen, author of History of Information GraphicsMichael Friendly and Howard Wainer have given us a wonderful history of the dazzling field of data visualization. They bring new life to ancient death statistics and describe the artistic poetry used to display numbers. An intriguing story of how we have learned to communicate data of all types. -- Stephen M. Stigler, author of The Seven Pillars of Statistical WisdomTwo of the most distinguished scholars of data visualization give us a glimpse of ancient attempts to quantify the world, before revealing the century-long revolution that led to the invention of modern statistics and many of the graphical methods we use today. I learned a lot from this book, and I think you will too. -- Alberto Cairo, author of How Charts Lie: Getting Smarter about Visual InformationFriendly and Wainer demonstrate the amazing progress that has been made in data graphics over the past two hundred years. Understanding this history—where graphs came from and how they developed—will be valuable as we move forward. -- Andrew Gelman, coauthor of Regression and Other Stories

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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'

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Book SynopsisThe Average is Always Wrong is a completely pragmatic and hands-on guide to harnessing data to transform your business for the better.

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Book SynopsisThis accessible guide to data modeling introduces basic probabilistic concepts, gradually building toward state-of-the art data modeling and analysis techniques. Aimed at students and researchers in the sciences, the text is self-contained and pedagogical, including practical examples and end of chapter problems.Table of ContentsPart I. Concepts from Modeling, Inference, and Computing: 1. Probabilistic modeling and inference; 2. Dynamical systems and Markov processes; 3. Likelihoods and latent variables; 4. Bayesian inference; 5. Computational inference; Part II. Statistical Models: 6. Regression models; 7. Mixture models; 8. Hidden Markov models; 9. State-space models; 10. Continuous time models*; Part III. Appendix: Appendix A: Notation and other conventions; Appendix B: Numerical random variables; Appendix C: The Kronecker and Dirac deltas; Appendix D: Memoryless distributions; Appendix E: Foundational aspects of probabilistic modeling; Appendix F: Derivation of key relations; References; Index.

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Book SynopsisA hands-on textbook teaching how to carry out large-scale data analytics and implement machine learning solutions for big data. Including copious real-world examples, it offers a coherent teaching package with lab assignments, exercises, solutions for instructors, and lecture slides.Trade Review'With the growing ubiquity of large and complex datasets, MapReduce and Spark's dataflow programming models have become mission-critical skills for data scientists, data engineers, and ML engineers. Triguero and Galar leverage their extensive teaching experience on this topic to deliver this tour de force deep dive into both the technical concepts and programming knowhow needed for such modern large-scale data analytics. They interleave intuitive exposition of the concepts and examples from data engineering and classical ML pipelines with well-thought-out hands-on code and outputs. This book not only shows how all this knowledge is useful in practice today but also sets up the reader to be able to successfully 'generalize' to future workloads.' Arun Kumar, University of California, San DiegoTable of ContentsPart I. Understanding and Dealing with Big Data: 1. Introduction; 2. MapReduce; Part II. Big Data Frameworks: 3. Hadoop; 4. Spark; 5. Spark SQL and DataFrames; Part III. Machine Learning for Big Data: 6. Machine Learning with Spark; 7. Machine Learning for Big Data; 8. Implementing Classical Methods: k-means and Linear Regression; 9. Advanced Examples: Semi-supervised, Ensembles, Deep Learning Model Deployment.

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Book SynopsisOriented matroids appear throughout discrete geometry, with applications in algebra, topology, physics, and data analysis. This introduction to oriented matroids is intended for graduate students, scientists wanting to apply oriented matroids, and researchers in pure mathematics. The presentation is geometrically motivated and largely self-contained, and no knowledge of matroid theory is assumed. Beginning with geometric motivation grounded in linear algebra, the first chapters prove the major cryptomorphisms and the Topological Representation Theorem. From there the book uses basic topology to go directly from geometric intuition to rigorous discussion, avoiding the need for wider background knowledge. Topics include strong and weak maps, localizations and extensions, the Euclidean property and non-Euclidean properties, the Universality Theorem, convex polytopes, and triangulations. Themes that run throughout include the interplay between combinatorics, geometry, and topology, and the idea of oriented matroids as analogs to vector spaces over the real numbers and how this analogy plays out topologically.

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Book SynopsisThe ethics of data and analytics, in many ways, is no different than any endeavor to find the right answer. When a business chooses a supplier, funds a new product, or hires an employee, managers are making decisions with moral implications. The decisions in business, like all decisions, have a moral component in that people can benefit or be harmed, rules are followed or broken, people are treated fairly or not, and rights are enabled or diminished. However, data analytics introduces wrinkles or moral hurdles in how to think about ethics. Questions of accountability, privacy, surveillance, bias, and power stretch standard tools to examine whether a decision is good, ethical, or just. Dealing with these questions requires different frameworks to understand what is wrong and what could be better.Ethics of Data and Analytics: Concepts and Cases does not search for a new, different answer or to ban all technology in favor of human decision-making. TheTable of ContentsIntroduction. 1 Value-Laden Biases in Data Analytics. 2 Ethical Theories and Data Analytics. 3 Privacy, Data, and Shared Responsibility. 4 Surveillance and Power. 5 The Purpose of the Corporation and Data Analytics. 6 Fairness and Justice in Data Analytics. 7 Discrimination and Data Analytics. 8 Creating Outcomes and Accuracy in Data Analytics. 9 Gamification, Manipulation, and Data Analytics. 10 Transparency and Accountability in Data Analytics. 11 Ethics, AI, Research, and Corporations. Index.

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Book SynopsisThe last decade has witnessed the rise of big data in game development as the increasing proliferation of Internet-enabled gaming devices has made it easier than ever before to collect large amounts of player-related data. At the same time, the emergence of new business models and the diversification of the player base have exposed a broader potential audience, which attaches great importance to being able to tailor game experiences to a wide range of preferences and skill levels. This, in turn, has led to a growing interest in data mining techniques, as they offer new opportunities for deriving actionable insights to inform game design, to ensure customer satisfaction, to maximize revenues, and to drive technical innovation. By now, data mining and analytics have become vital components of game development. The amount of work being done in this area nowadays makes this an ideal time to put together a book on this subject.Data Analytics Applications in Gaming andTable of ContentsPart 1 – Introduction to game data mining. Part 2 – Data mining for games user research. Part 3 – Data mining for game technology.Part 4 – Visualization of large-scale game data.

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Book SynopsisData Analytics and Adaptive Learning offers new insights into the use of emerging data analysis and adaptive techniques in multiple learning settings. In recent years, both analytics and adaptive learning have helped educators become more responsive to learners in virtual, blended, and personalized environments. This set of rich, illuminating, international studies spans quantitative, qualitative, and mixed-methods research in higher education, K12, and adult/continuing education contexts. By exploring the issues of definition and pedagogical practice that permeate teaching and learning and concluding with recommendations for the future research and practice necessary to support educators at all levels, this book will prepare researchers, developers, and graduate students of instructional technology to produce evidence for the benefits and challenges of data-driven learning.Trade Review"This book is an essential guide to the promise and practice of data analytics and adaptive learning in higher education. These pioneers and practitioners share valuable insights all institutions can use to enhance learning and student success."—Diana G. Oblinger, Ph.D., President Emeritus, EDUCAUSE"Impacts on learning we called overdetermined can now be parsed but need an informed judgment rising to the complexities involved. Data Analytics and Adaptive Learning, a collection of the very best thinking about both, provides just that, rendering the (potentially) all-seeing and hyper-focused approaches of DA and AL fruitful, humane, transformative."—George Otte, former University Director of Academic Technology, The City University of New York"At last: a book by education experts about the use of digital Information and Communication Technologies (ICTs) not only for lowering the friction in data, but for processing information to help teachers and students. This can be the beginning of a more radical change in education."—Anders Norberg, Coordinator of the ERASMUS+ALBATTS Blueprint project for development of innovative education for the emerging European battery and electromobility value chain (ERASMUS is a European Union Initiative for Education Mobility and Development)"Digital learning is the new normal in higher education. The group of experts assembled in this book share important ideas and trends related to learning analytics and adaptive learning that will surely influence all of our digital learning environments in the future."—Charles R. Graham, Professor, Department of Instructional Psychology and Technology, Brigham Young University "The concept of personalized and adaptive learning has long been touted but seldom enacted in education at scale. Data Analytics and Adaptive Learning brings together a compelling set of experts that provide novel and research informed insights into contemporary education spaces."—Professor Shane Dawson, Executive Dean Education Futures, University of South Australia"Moskal, Dziuban, and Picciano challenge the reader to keep the student at the center and imagine how data analytics and adaptive learning can be mutually reinforcing in closing the gap between students from different demographics."—Susan Rundell Singer, Vice President for Academic Affairs and Provost, Rollins College, former Division Director for Undergraduate Education at the National Science Foundation"We are currently living in a digital age where higher education institutions have an abundance of accessible data. This book contains a series of chapters that provide insight and strategies for using data analytics and adaptive learning to support student success and satisfaction."—Dr. Norman Vaughan, Professor of Education, Mount Royal University, Calgary, Alberta, Canada"An important book that comes at a critical moment in higher education. We are swimming in an ocean of data and this book from some of the country’s top researchers and practitioners will help us make sense of it and put it in the service of student success." —Thomas Cavanagh, Ph.D. Vice Provost for Digital Learning, University of Central Florida"Data Analytics and Adaptive Learning is an excellent addition to the canon of literature in this field. The book offers several valuable perspectives and innovative ways of approaching both new and old problems to improve organizational outcomes."—Jeffrey S. Russell, P.E., Ph.D., Dist.M.ASCE, NAC, F.NSPE, Vice Provost for Lifelong Learning, Dean for Div. of Continuing Studies, University of Wisconsin-Madison"Data is used to customize experiences from buying an item to booking travel. What about learning—a uniquely human endeavor? This book contextualizes the complex answers to that question shedding light on areas with promise: learning analytics, adaptive learning and the use of big data."—Dale Whittaker, Senior Program Officer in Post-Secondary Success, Bill and Melinda Gates Foundation"Data Analytics and Adaptive Learning presents a timely and wide-ranging consideration of the progress of adaptive learning and analytics in levelling the educational playing field, while providing necessary cautions regarding the drawing of too many conclusions in what is still a nascent area."—Frank Claffey, Chief Product Officer, Realizeit Learning"Data Analytics and Adaptive Learning provides insights and best practices from leaders in digital learning who outline considerations for strategies, change management, and effective decision making related to data. As higher education expands its work in digital learning and utilizing data for decisions, this book is a must read!"—Dr. Connie Johnson, Chancellor, Colorado Technical University"Data analytics and adaptive learning compromise two of the most relevant educational challenges. This book provides excellent research approaches and analysis to answer practical questions related to digital education involving teachers and learners"—Josep M Duart & Elsa Rodriguez, Editor-in-Chief & Editorial Manager of the International Journal of Educational technology in Higher Education, the Universitat Oberta de Catalunya (UOC)"Data, analytics, and machine learning are impacting all jobs and industries. For education, the opportunities are immense, but so are the challenges. This book provides an essential view into the possibilities and pitfalls. If you want to use data to impact learners positively, this book is a must-read."—Colm Howlin, PhD, Chief Data Scientist, ReliaQuest"Data Analytics and Adaptive Learning helps the educational community broaden its understanding of these two technology-based opportunities to enhance education, looking at very different complementary contributions. Congratulations to the authors."—Alvaro Galvis, professor at University of Los Andes, Bogotá"The menus, dashboards, and pathways to effective data analytics and adaptive learning can be found in this massively timely and hugely impactful juggernaut."—Curtis J. Bonk, Professor of Instructional Systems Technology and adjunct in the School of Informatics, Indiana University Bloomington"Adaptive learning and learning analytics – should we use both or choose one? Do they imply organizational transformation? What works and what does not? In my opinion, the book is valuable reading for those seeking the answers to their questions."—Maria Zajac, Associate Professor (emeritus) at Pedagogical University Cracow and SGH Warsaw School of Economics, Certified Instructional Designer, Poland "Data analytics and adaptive learning platforms can direct support as needed to at-risk students, helping to create more equitable outcomes. This volume contains a timely collection of studies that examine the impact of these approaches."—John Kane, Director of the Center for Excellence in Learning and Teaching at SUNY Oswego."This book shines a spotlight on the potential for data analytics, adaptive learning and big data to transform higher education. The volume lights the way for those brave enough to embrace a new paradigm of teaching and learning that enacts a more equitable and person-centered experience for all learners."—Paige McDonald, Associate Professor and Vice Chair, Department of Clinical Research and Leadership, The George Washington School of Medicine and Health Sciences"Deftly weaving adaptive learning and analytic theory and practice together, the authors offer numerous examples of how these methods can help us address academic barriers to student success. Their work significantly strengthens the fabric of knowledge on how adaptive learning can benefit students (and faculty)." —Dale P. Johnson, Director of Digital Innovation, University Design Institute, Arizona State University"The authors of this book convince us that the concepts of data analytics and adaptive learning are tightly integrated, and the book provides insights on different aspects related to utilization of intelligent technologies and how to approach the learning cycle at different stages." —Eli Hustad, Professor in Information Systems, The University of Agder"Student success is a fundamental mission for all educational institutions. This book explores the current opportunities within analytics, adaptive Learning, and organizational transformation to generate wide-scale and equitable learning outcomes."—John Campbell, Associate Professor, Higher Education Administration, School of Education, West Virginia University"This book brings together top scholars making the connection between data analytics and adaptive learning, all while keeping pedagogical theory on the central stage. It’s a powerhouse driven in equal parts by excellence and innovation providing vision for educators on the quest for learner success across the spectrum."—Kimberly Arnold, Director of Learning Analytics Center of Excellence"Once again, a dream team of faculty, researchers, thought leaders and practitioners come up with this defining, must-read book for every institutional leader and teacher that is invested in the success of every student. This book based on years of research and practice gives the "how-to"."—Manoj Kulkarni, CEO at Realizeit Learning"The chapters in this book bring a desperately needed clarity and a depth of understanding to the topic of data and analytics, adaptive learning and learning more generally in higher education. You will leave this book smarter about these topics than you started and both you and higher education will be the beneficiary."—Glenda MorganTable of ContentsSection 1: Introduction 1. Data Analytics and Adaptive Learning: Increasing the Odds Section 2: Analytics 2. What We Want Versus What We Have: Transforming Teacher Performance Analytics to Personalize Professional Development 3. System-Wide Momentum 4. A Precise and Consistent Early Warning System for Identifying At-Risk Students 5. Predictive Analytics, Artificial Intelligence and the Impact of Delivering Personalized Supports to Students from Underserved Backgrounds 6. Predicting Student Success with Self-regulated Behaviors: A Seven-year Data Analytics Study on a Hong Kong University English Course 7. Back to Bloom: Why Theory Matters in Closing the Achievement Gap 8. The Metaphors We Learn By: Toward a Philosophy of Learning Analytics Section 3: Adaptive Learning 9. A Cross-Institutional Survey of the Instructor Use of Data Analytics in Adaptive Courses 10. Data Analytics in Adaptive Learning for Equitable Outcomes 11. Banking on Adaptive Questions to Nudge Student Responsibility for Learning in General Chemistry 12. 3-Year Experience with Adaptive Learning: Faculty and Student Perspectives 13. Analyzing Question Items with Limited Data 14. When Adaptivity and Universal Design for Learning are Not Enough: Bayesian Network Recommendations for Tutoring Section 4: Organizational Transformation 15. Sprint to 2027: Corporate Analytics in the Digital Age 16. Academic Digital Transformation: Focused on Data, Equity and Learning Science Section 5: Closing 17. Future Technological Trends and Research – Tony Picciano

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Book SynopsisData Analytics and Adaptive Learning offers new insights into the use of emerging data analysis and adaptive techniques in multiple learning settings. In recent years, both analytics and adaptive learning have helped educators become more responsive to learners in virtual, blended, and personalized environments. This set of rich, illuminating, international studies spans quantitative, qualitative, and mixed-methods research in higher education, K12, and adult/continuing education contexts. By exploring the issues of definition and pedagogical practice that permeate teaching and learning and concluding with recommendations for the future research and practice necessary to support educators at all levels, this book will prepare researchers, developers, and graduate students of instructional technology to produce evidence for the benefits and challenges of data-driven learning.Trade Review"This book is an essential guide to the promise and practice of data analytics and adaptive learning in higher education. These pioneers and practitioners share valuable insights all institutions can use to enhance learning and student success."—Diana G. Oblinger, Ph.D., President Emeritus, EDUCAUSE"Impacts on learning we called overdetermined can now be parsed but need an informed judgment rising to the complexities involved. Data Analytics and Adaptive Learning, a collection of the very best thinking about both, provides just that, rendering the (potentially) all-seeing and hyper-focused approaches of DA and AL fruitful, humane, transformative."—George Otte, former University Director of Academic Technology, The City University of New York"At last: a book by education experts about the use of digital Information and Communication Technologies (ICTs) not only for lowering the friction in data, but for processing information to help teachers and students. This can be the beginning of a more radical change in education."—Anders Norberg, Coordinator of the ERASMUS+ALBATTS Blueprint project for development of innovative education for the emerging European battery and electromobility value chain (ERASMUS is a European Union Initiative for Education Mobility and Development)"Digital learning is the new normal in higher education. The group of experts assembled in this book share important ideas and trends related to learning analytics and adaptive learning that will surely influence all of our digital learning environments in the future."—Charles R. Graham, Professor, Department of Instructional Psychology and Technology, Brigham Young University "The concept of personalized and adaptive learning has long been touted but seldom enacted in education at scale. Data Analytics and Adaptive Learning brings together a compelling set of experts that provide novel and research informed insights into contemporary education spaces."—Professor Shane Dawson, Executive Dean Education Futures, University of South Australia"Moskal, Dziuban, and Picciano challenge the reader to keep the student at the center and imagine how data analytics and adaptive learning can be mutually reinforcing in closing the gap between students from different demographics."—Susan Rundell Singer, Vice President for Academic Affairs and Provost, Rollins College, former Division Director for Undergraduate Education at the National Science Foundation"We are currently living in a digital age where higher education institutions have an abundance of accessible data. This book contains a series of chapters that provide insight and strategies for using data analytics and adaptive learning to support student success and satisfaction."—Dr. Norman Vaughan, Professor of Education, Mount Royal University, Calgary, Alberta, Canada"An important book that comes at a critical moment in higher education. We are swimming in an ocean of data and this book from some of the country’s top researchers and practitioners will help us make sense of it and put it in the service of student success." —Thomas Cavanagh, Ph.D. Vice Provost for Digital Learning, University of Central Florida"Data Analytics and Adaptive Learning is an excellent addition to the canon of literature in this field. The book offers several valuable perspectives and innovative ways of approaching both new and old problems to improve organizational outcomes."—Jeffrey S. Russell, P.E., Ph.D., Dist.M.ASCE, NAC, F.NSPE, Vice Provost for Lifelong Learning, Dean for Div. of Continuing Studies, University of Wisconsin-Madison"Data is used to customize experiences from buying an item to booking travel. What about learning—a uniquely human endeavor? This book contextualizes the complex answers to that question shedding light on areas with promise: learning analytics, adaptive learning and the use of big data."—Dale Whittaker, Senior Program Officer in Post-Secondary Success, Bill and Melinda Gates Foundation"Data Analytics and Adaptive Learning presents a timely and wide-ranging consideration of the progress of adaptive learning and analytics in levelling the educational playing field, while providing necessary cautions regarding the drawing of too many conclusions in what is still a nascent area."—Frank Claffey, Chief Product Officer, Realizeit Learning"Data Analytics and Adaptive Learning provides insights and best practices from leaders in digital learning who outline considerations for strategies, change management, and effective decision making related to data. As higher education expands its work in digital learning and utilizing data for decisions, this book is a must read!"—Dr. Connie Johnson, Chancellor, Colorado Technical University"Data analytics and adaptive learning compromise two of the most relevant educational challenges. This book provides excellent research approaches and analysis to answer practical questions related to digital education involving teachers and learners"—Josep M Duart & Elsa Rodriguez, Editor-in-Chief & Editorial Manager of the International Journal of Educational technology in Higher Education, the Universitat Oberta de Catalunya (UOC)"Data, analytics, and machine learning are impacting all jobs and industries. For education, the opportunities are immense, but so are the challenges. This book provides an essential view into the possibilities and pitfalls. If you want to use data to impact learners positively, this book is a must-read."—Colm Howlin, PhD, Chief Data Scientist, ReliaQuest"Data Analytics and Adaptive Learning helps the educational community broaden its understanding of these two technology-based opportunities to enhance education, looking at very different complementary contributions. Congratulations to the authors."—Alvaro Galvis, professor at University of Los Andes, Bogotá"The menus, dashboards, and pathways to effective data analytics and adaptive learning can be found in this massively timely and hugely impactful juggernaut."—Curtis J. Bonk, Professor of Instructional Systems Technology and adjunct in the School of Informatics, Indiana University Bloomington"Adaptive learning and learning analytics – should we use both or choose one? Do they imply organizational transformation? What works and what does not? In my opinion, the book is valuable reading for those seeking the answers to their questions."—Maria Zajac, Associate Professor (emeritus) at Pedagogical University Cracow and SGH Warsaw School of Economics, Certified Instructional Designer, Poland "Data analytics and adaptive learning platforms can direct support as needed to at-risk students, helping to create more equitable outcomes. This volume contains a timely collection of studies that examine the impact of these approaches."—John Kane, Director of the Center for Excellence in Learning and Teaching at SUNY Oswego."This book shines a spotlight on the potential for data analytics, adaptive learning and big data to transform higher education. The volume lights the way for those brave enough to embrace a new paradigm of teaching and learning that enacts a more equitable and person-centered experience for all learners."—Paige McDonald, Associate Professor and Vice Chair, Department of Clinical Research and Leadership, The George Washington School of Medicine and Health Sciences"Deftly weaving adaptive learning and analytic theory and practice together, the authors offer numerous examples of how these methods can help us address academic barriers to student success. Their work significantly strengthens the fabric of knowledge on how adaptive learning can benefit students (and faculty)." —Dale P. Johnson, Director of Digital Innovation, University Design Institute, Arizona State University"The authors of this book convince us that the concepts of data analytics and adaptive learning are tightly integrated, and the book provides insights on different aspects related to utilization of intelligent technologies and how to approach the learning cycle at different stages." —Eli Hustad, Professor in Information Systems, The University of Agder"Student success is a fundamental mission for all educational institutions. This book explores the current opportunities within analytics, adaptive Learning, and organizational transformation to generate wide-scale and equitable learning outcomes."—John Campbell, Associate Professor, Higher Education Administration, School of Education, West Virginia University"This book brings together top scholars making the connection between data analytics and adaptive learning, all while keeping pedagogical theory on the central stage. It’s a powerhouse driven in equal parts by excellence and innovation providing vision for educators on the quest for learner success across the spectrum."—Kimberly Arnold, Director of Learning Analytics Center of Excellence"Once again, a dream team of faculty, researchers, thought leaders and practitioners come up with this defining, must-read book for every institutional leader and teacher that is invested in the success of every student. This book based on years of research and practice gives the "how-to"."—Manoj Kulkarni, CEO at Realizeit Learning"The chapters in this book bring a desperately needed clarity and a depth of understanding to the topic of data and analytics, adaptive learning and learning more generally in higher education. You will leave this book smarter about these topics than you started and both you and higher education will be the beneficiary."—Glenda MorganTable of ContentsSection 1: Introduction 1. Data Analytics and Adaptive Learning: Increasing the Odds Section 2: Analytics 2. What We Want Versus What We Have: Transforming Teacher Performance Analytics to Personalize Professional Development 3. System-Wide Momentum 4. A Precise and Consistent Early Warning System for Identifying At-Risk Students 5. Predictive Analytics, Artificial Intelligence and the Impact of Delivering Personalized Supports to Students from Underserved Backgrounds 6. Predicting Student Success with Self-regulated Behaviors: A Seven-year Data Analytics Study on a Hong Kong University English Course 7. Back to Bloom: Why Theory Matters in Closing the Achievement Gap 8. The Metaphors We Learn By: Toward a Philosophy of Learning Analytics Section 3: Adaptive Learning 9. A Cross-Institutional Survey of the Instructor Use of Data Analytics in Adaptive Courses 10. Data Analytics in Adaptive Learning for Equitable Outcomes 11. Banking on Adaptive Questions to Nudge Student Responsibility for Learning in General Chemistry 12. 3-Year Experience with Adaptive Learning: Faculty and Student Perspectives 13. Analyzing Question Items with Limited Data 14. When Adaptivity and Universal Design for Learning are Not Enough: Bayesian Network Recommendations for Tutoring Section 4: Organizational Transformation 15. Sprint to 2027: Corporate Analytics in the Digital Age 16. Academic Digital Transformation: Focused on Data, Equity and Learning Science Section 5: Closing 17. Future Technological Trends and Research – Tony Picciano

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Book SynopsisDeveloping and implementing a systematic analytics strategy can result in a sustainable competitive advantage within the sport business industry. This timely and relevant book provides practical strategies to collect data and then convert that data into meaningful, value-added information and actionable insights. Its primary objective is to help sport business organizations utilize data-driven decision-making to generate optimal revenue from such areas as ticket sales and corporate partnerships. To that end, the book includes in-depth case studies from such leading sports organizations as the Orlando Magic, Tampa Bay Buccaneers, Duke University, and the Aspire Group.The core purpose of sport business analytics is to convert raw data into information that enables sport business professionals to make strategic business decisions that result in improved company financial performance and a measurable and sustainable competitive advantage. Readers will learn about the role of big Table of ContentsEvolution and Impact of Business Analytics in Sport. Analytics and Ticketing Innovations at the Orlando Magic. Ticket Markets in Sport: Is the Secondary Market the Primary Market?. Customer Relationship Management and Fan Engagement Analytics. The Aspire Group’s Ticket Marketing, Sales and Service Philosophy. Empirical Research Methods: Season Ticket Holder Management and Fan Engagement. Developing and Measuring the Effectiveness of Data-Driven Direct Marketing Initiatives. Fan Engagement, Social Media, and Digital Marketing Analytics at Duke University. Leveraging Digital Marketing to Engage Customers and Drive Revenue. Communicating the Value of Sports Sponsorship. Market Research Analytics and Data Driven Innovation. Talent Analytics: Utilizing Analytics to Evaluate Performance . Data Visualization and Data-Driven Storytelling. Teaching a Sport Business Analytics Class. Index.

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Book SynopsisData Analytics in Football provides students, researchers, and coaches with a firm grounding in the principles of modern performance analysis. It offers an insight into the use of positional data, exploring how they can be collected, modeled, analyzed, and interpreted. Introducing cutting-edge methods, the book challenges long-held assumptions and encourages a new way of thinking about football analysis. The book seeks to define the role of positional data in football match analysis by exploring topics such as the following: What is positional data analysis, and how did it emerge from conventional match analysis? How can positional data be collected, and which technologies can be used? What key performance indicators based on positional data should be used? How can traditional match analysis be complemented by using positional data and advanced KPIs? How can these new methods evolve in the future? Based on data collectTable of Contents1. Where Is the Revolution? 2. A Historical Perspective on Positional Data 3. Technological Background 4. Collecting Data in the Bundesliga 5. In Search of the Holy Grail 6. Betting and Sports Analytics 7. Match Intensity 8. From Media to Storytelling 9. Key Properties of Long-Term Success in Football 10. The Key to Success 11. Reasons For Dominance 12. FCB Versus FCB 13. Home Advantage 14. Managerial Influence 15. All on Attack 16. Laws of a Derby 17. Women Vs. Men – Draw in Tactical Performance 18. Experimental Tactics Research 19. Positional Data Meet Sport Psychology 20. Summary

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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

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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.

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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.

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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.

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