Data capture and analysis Books

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 SynopsisDemonstrates how odd and unusual math problems can be solved by bringing together basic physics ideas and powerful computers. This title looks at how the art of number-crunching has changed since the advent of computers, and how high-speed technology helps to solve conundrums such as the three-body, Monte Carlo, and gambler's ruin problems.Trade Review"Great stories. Interesting and challenging problems. Instructive MATLAB code. Lots of physics. That's my in-a-nutshell assessment... Nahin takes on the subject of using computers to solve difficult problems, many in physics, that couldn't be solved before computers... This is one of those books that one can read as a spectator, enjoying the scenery, taking in the landscape, appreciating the rich stories--my relationship with the book--or one can dive in, study the many equations, run the code, and have a personal experience of how problems that were unsolvable just a few decades ago have succumbed to computers."--Sol Lederman, Wild About Math "Number Crunching is most timely, given the escalating scale of economic, commercial, and financial transactions, necessitating thinking about, evaluating, and communicating on a much larger scale... The presentation in Number Crunching is simultaneously accessible, readable, entertaining, daunting, sophisticated, and technical."--Stephen E. Roulac, New York Journal of Books "Number-Crunching is packed with copious notes and references and augmented by significant challenge problems that take the reader beyond the text and which would make good undergraduate projects... Nahin's aim is clearly to convey enthusiasm for the subject to a younger reader and to give a glimpse of what is technically possible... He looks to convey the excitement that he and many of us had when first attracted to the physical sciences as we were growing up--the excitement at the realisation that, given a few tools, even an awkward teenager can make quantitative statements about the world."--C.J. Howls, Times Higher Education "Paul Nahin, a prolific and knowledgeable expository writer, is a professor emeritus of electrical engineering at the University of New Hampshire. What he offers in Number-Crunching might be described as a mix of (1) supplementary readings for courses in mathematics, physics, or electrical engineering, (2) 'challenge problems' intended as a brain-jogging call to prospective professionals, and (3) a garage sale of mathematical miscellania and esoterica."--Philip J. Davis, SIAM News "[Number-Crunching] is impressive for several reasons. First, Nahin has found the right level--not too easy and not too hard. Second, the problem selections and topics are interesting and in several cases give surprising results. Finally, the book is just plain fun."--Choice "[A] highly entertaining and rewarding read."--Dean Rickles, Mathematical Reviews "Unlike writers of popular mathematics books, Nahin is more then happy to spray the pages with equations! Since these are surrounded by lucid and informal explanations, they add significant value to the book... Whether or not you are the computing type, there is a lot to enjoy about this book."--Alan Stevens, Mathematics TODAY "Many of these challenge problems would make excellent projects--both in mathematics or physics--for undergraduates to explore. Number-Crunching's greatest strength is providing a perspective on how three unique yet vastly intertwined fields interact while keeping readers more and more intrigued as they progress through the text. Readers will find abundant resources to quench their thirst for knowledge of computational mathematics and physics."--Brandon Milanovich, Mathematics TeacherTable of ContentsIntroduction x Chapter 1: FEYNMAN MEETS FERMAT 1 1.1 The Physicist as Mathematician 1 1.2 Fermat's Last Theorem 2 1.3 "Proof" by Probability 3 1.4 Feynman's Double Integral 6 1.5 Things to come 10 1.6 Challenge Problems 11 1.7 Notes and References 13 Chapter 2: Just for Fun: Two Quick Number-Crunching Problems 16 2.1 Number-Crunching in the Past 16 2.2 A Modern Number-Cruncher 20 2.3 Challenge Problem 25 2.4 Notes and References 25 Chapter 3: Computers and Mathematical Physics 27 3.1 When Theory Isn't Available 27 3.2 The Monte Carlo Technique 28 3.3 The Hot Plate Problem 34 3.4 Solving the Hot Plate Problem with Analysis 38 3.5 Solving the Hot Plate Problem by Iteration 44 3.6 Solving the Hot Plate Problem with the Monte Carlo Technique 50 3.7 ENIAC and MANIAC-I: the Electronic Computer Arrives 55 3.8 The Fermi-Pasta-Ulam Computer Experiment 58 3.9 Challenge Problems 73 3.10 Notes and References 74 Chapter 4: The Astonishing Problem of the Hanging Masses 82 4.1 Springs and Harmonic Motion 82 4.2 A Curious Oscillator 87 4.3 Phase-Plane Portraits 96 4.4 Another (Even More?) Curious Oscillator 99 4.5 Hanging Masses 104 4.6 Two Hanging Masses and the Laplace Transform 108 4.7 Hanging Masses and MATLAB 113 4.8 Challenge Problems 124 4.9 Notes and References 124 Chapter 5: The Three-Body Problem and Computers 131 5.1 Newton's Theory of Gravity 131 5.2 Newton's Two-Body Solution 139 5.3 Euler's Restricted Three-Body Problem 147 5.4 Binary Stars 155 5.5 Euler's Problem in Rotating Coordinates 166 5.6 Poincare and the King Oscar II Competition 177 5.7 Computers and the Pythagorean Three-Body Problem 184 5.8 Two Very Weird Three-Body Orbits 195 5.9 Challenge Problems 205 5.10 Notes and References 207 Chapter 6: Electrical Circuit Analysis and Computers 218 6.1 Electronics Captures a Teenage Mind 218 6.2 My First Project 220 6.3 "Building" Circuits on a Computer 230 6.4 Frequency Response by Computer Analysis 234 6.5 Differential Amplifiers and Electronic Circuit Magic 249 6.6 More Circuit Magic: The Inductor Problem 260 6.7 Closing the Loop: Sinusoidal and Relaxation Oscillators by Computer 272 6.8 Challenge Problems 278 6.9 Notes and References 281 Chapter 7: The Leapfrog Problem 288 7.1 The Origin of the Leapfrog Problem 288 7.2 Simulating the Leapfrog Problem 290 7.3 Challenge Problems 296 7.4 Notes and References 296 Chapter 8: Science Fiction: When Computers Become Like Us 297 8.1 The Literature of the Imagination 297 8.2 Science Fiction "Spoofs" 300 8.3 What If Newton Had Owned a Calculator? 305 8.4 A Final Tale: the Artificially Intelligent Computer 314 8.5 Notes and References 324 Chapter 9: A Cautionary Epilogue 328 9.1 The Limits of Computation 328 9.2 The Halting Problem 330 9.3 Notes and References 333 Appendix (FPU Computer Experiment MATLAB Code) 335 Solutions to the Challenge Problems 337 Acknowledgments 371 Index 373 Also by Paul J. Nahin 377

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Book SynopsisAn innovative application of economic methods to the study of art history, demonstrating that new insights can be uncovered by using quantitative and qualitative methods together, which sheds light on longstanding disciplinary inequitiesTrade Review"Winner of a Millard Meiss Publication Fund Grant, College Art Association""Painting by Numbers…[is] careful and systematic…it is a solid demonstration that “counting things” matters. It leaves audiences to wonder what work the book will inspire as other researchers draw from the quantitative foundation Greenwald has established… [I]t’s clear that the author’s expertise in art and data pair brilliantly” –Lydia Pyne, Hyperallergic""The real power of [Painting by Numbers] is. . . . prompting art historians to ask questions about the values underpinning their definition of their objects of study. . . . [Diana Greenwald] has done a valuable service to the field in asking us to rethink our fundamental categories of disciplinary concern and our responsibilities to the vast range of visual and material culture that might fall within their purview." * CAA Reviews *"Diana Seave Greenwald’s Painting by Numbers: Data-Driven Histories of Nineteenth-Century Art is an ambitious study that synthesizes two disparate approaches of scholarship: art history and economic analysis. . . . Greenwald is a pioneer in the field who is willing to explore new perspectives and challenge past presumptions. The book paves the way for similar interdisciplinary studies to follow. . . . Painting by Numbers shows the promise of what can be achieved when an abundance of information is wedded with insightful scholarship."---Matt Garklavs, ARLIS/NA Reviews"[Diana Greenwald] presents novel evidence on the artistic production of the nineteenth-century in France, the USA, and England and focusses on crucial topics in the art history of that period, namely, industrialization, gender, and the history of empire, providing new points of view. . . . [Painting by Numbers] represents a concrete application of the benefits of an interdisciplinary approach in humanities and social sciences."---Laura Paganl, Journal of Cultural Economics"[A] great benefit to art historians unpracticed in economic theory."---Elizabeth L. Block, Panorama"Painting By Numbers offers methods and interpretations that may revise art historians’ assumptions about what we do and how we do it."---Julie Codell, Winterthur Portfolio"Using hard, quantitative data in order to test, critique or support conventional wisdom is very unusual in art-historical research. Painting by Numbers succeeds in making a convincing case for that kind of study, which makes it a model of methodological innovation, and a very welcome one."---Jorge Sebastián Lozano, Art History

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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 SynopsisThis text describes the development of magnetic recording over the past century, by selecting major product designs and the innovative technology that they introduced. Separated into three parts, the book deals specifically with the history of audio recording, video recording and data recording.Table of ContentsAcknowledgments. Contributors. Introduction (C. Mee & E. Daniel). AUDIO RECORDING. The Magnetic Recording of Sound (M. Clark). The Telegraphone (M. Clark & H. Nielsen). Steel Tape and Wire Recorders (M. Clark). The Introduction of the Magnetophon (F. Engel). Building on the Magnetophon (B. Gooch). Product Diversification (M. Clark). The History of Digital Audio (J. Watkinson). VIDEO RECORDING. The Challenge of Recording Video (F. Remley). Early Fixed-Head Video Recorders (F. Jorgensen). The Ampex Quadruplex Recorders (J. Mallinson). Helical-Scan Recorders for Broadcasting (H. Sugaya). Consumer Video Recorders (H. Sugaya). Digital Video Recording (K. Sadashige). DATA RECORDING. Capturing Data Magnetically (J. Monson). Data Storage on Drums (S. Rubens). Data Storage on Tape (W. Phillips). Data Storage on Hard Magnetic Disks (L. Stevens). Data Storage on Floppy Disks (D. Noble). Instrumentation Recording on Magnetic Tape (F. Jorgensen). Index. About the Editors.

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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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Book SynopsisWith the surge in big data and AI, organizations can rapidly create data products. However, the effectiveness of their analytics and machine learning models depends on the data's quality. Delta Lake's open source format offers a robust lakehouse framework over platforms like Amazon S3, ADLS, and GCS.

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Book SynopsisSentiment analysis is the computational study of people''s opinions, sentiments, emotions, moods, and attitudes. This fascinating problem offers numerous research challenges, but promises insight useful to anyone interested in opinion analysis and social media analysis. This comprehensive introduction to the topic takes a natural-language-processing point of view to help readers understand the underlying structure of the problem and the language constructs commonly used to express opinions, sentiments, and emotions. The book covers core areas of sentiment analysis and also includes related topics such as debate analysis, intention mining, and fake-opinion detection. It will be a valuable resource for researchers and practitioners in natural language processing, computer science, management sciences, and the social sciences. In addition to traditional computational methods, this second edition includes recent deep learning methods to analyze and summarize sentiments and opinions, and alTrade Review'As a whole, this book serves as a useful introduction to sentiment analysis along with in-depth discussions of linguistic phenomena related to sentiments, opinions, and emotions. Although many sentiment analysis methods are based on machine learning as in other NLP [Natural Language Processing] tasks, sentiment analysis is much more than just a classification or regression problem, because the natural language constructs used to express opinions, sentiments, and emotions are highly sophisticated, including sentiment shift, implicated expression, sarcasm, and so on. Liu has described these issues and problems very clearly. Readers will find this book to be inspiring and it will arouse their interests in sentiment analysis.' Jun Zhao, Chinese Academy of SciencesTable of Contents1. Introduction; 2. The Problem of Sentiment Analysis; 3. Document Sentiment Classification; 4. Sentence Subjectivity and Sentiment Classification; 5. Aspect Sentiment Classification; 6. Aspect and Entity Extraction; 7. Sentiment Lexicon Generation; 8. Analysis of Comparative Opinions; 9. Opinion Summarization and Search; 10. Analysis of Debates and Comments; 11. Mining Intents; 12. Detecting Fake or Deceptive Opinions; 13. Quality of Reviews; 14. Conclusions.

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Book SynopsisIf you are a biologist and want to get the best out of the powerful methods of modern computational statistics, this is your book. You can visualize and analyze your own data, apply unsupervised and supervised learning, integrate datasets, apply hypothesis testing, and make publication-quality figures using the power of R/Bioconductor and ggplot2. This book will teach you ''cooking from scratch'', from raw data to beautiful illuminating output, as you learn to write your own scripts in the R language and to use advanced statistics packages from CRAN and Bioconductor. It covers a broad range of basic and advanced topics important in the analysis of high-throughput biological data, including principal component analysis and multidimensional scaling, clustering, multiple testing, unsupervised and supervised learning, resampling, the pitfalls of experimental design, and power simulations using Monte Carlo, and it even reaches networks, trees, spatial statistics, image data, and microbial ecology. Using a minimum of mathematical notation, it builds understanding from well-chosen examples, simulation, visualization, and above all hands-on interaction with data and code.Trade Review'This is a gorgeous book, both visually and intellectually, superbly suited for anyone who wants to learn the nuts and bolts of modern computational biology. It can also be a practical, hands-on starting point for life scientists and students who want to break out of 'canned packages' into the more versatile world of R coding. Much richer than the typical statistics textbook, it covers a wide range of topics in machine learning and image processing. The chapter on making high-quality graphics is alone worth the price of the book.' William H. Press, University of Texas, Austin'The book is a timely, comprehensive and practical reference for anyone working with modern quantitative biotechnologies. It can be read at multiple levels. For scientists with a statistics background, it is a thorough review of key methods for design and analysis of high-throughput experiments. For life scientists with a limited exposure to statistics, it offers a series of examples with relevant data and R code. Avoiding buzzwords and hype, the book advocates appropriate statistical practice for reproducible research. I expect it to be as influential for the life sciences community as Modern Applied Statistics with S, by Venables and Ripley or Introduction to Statistical Learning, by James, Witten, Hastie and Tibshirani are for applied statistics.' Olga Vitek, Northeastern University, Boston'Navigating rich data to arrive at sensible insight requires confidence in our biological understanding, informatic ability, statistical sophistication, and skills at effective communication. Fortunately the wisdom and effort of the worldwide research community has been distilled into accessible and rich collections of R and Bioconductor software packages. Holmes and Huber provide a comprehensive guide to navigating modern statistical methods for working with complex, large, and nuanced biological data. The presentation provides a firm conceptual foundation coupled with worked practical examples, extended analysis, and refined discussion of practical and theoretical challenges facing the modern practitioner. This book provides us with the confidence and tools necessary for the analysis and comprehension of modern biological data using modern statistical methods.' Martin Morgan, Roswell Park Comprehensive Cancer Center, leader of the Bioconductor project'Holmes and Huber take an integrated approach to presenting the key statistical concepts and methods needed for the analysis of biological data. Specifically, they do a wonderful job of building these foundations in the context of modern computational tools, genuine scientific questions, and real-world datasets. The code showcases many of the newest features of R and its dynamic package ecosystem, such as using ggplot2 for visualization and dplyr for data manipulation.' Jenny Bryan, RStudio and University of British Columbia'... the book is extremely readable and engaging, it explains complicated concepts in simple terms, and uses illuminating graphics and examples. Any researcher who wants to learn or teach up-to-date statistics to biologists will find this an essential volume for modern teaching of modern statistics to modern biologists.' Noa Pinter-Wollman, The Quarterly Review of BiologyTable of ContentsIntroduction; 1. Generative models for discrete data; 2. Statistical modeling; 3. High-quality graphics in R; 4. Mixture models; 5. Clustering; 6. Testing; 7. Multivariate analysis; 8. High-throughput count data; 9. Multivariate methods for heterogeneous data; 10. Networks and trees; 11. Image data; 12. Supervised learning; 13. Design of high-throughput experiments and their analyses; Statistical concordance; Bibliography; Index.

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Book SynopsisIn the age of data-driven problem-solving, applying sophisticated computational tools for explaining substantive phenomena is a valuable skill. Yet, application of methods assumes an understanding of the data, structure, and patterns that influence the broader research program. This Element offers researchers and teachers an introduction to clustering, which is a prominent class of unsupervised machine learning for exploring and understanding latent, non-random structure in data. A suite of widely used clustering techniques is covered in this Element, in addition to R code and real data to facilitate interaction with the concepts. Upon setting the stage for clustering, the following algorithms are detailed: agglomerative hierarchical clustering, k-means clustering, Gaussian mixture models, and at a higher-level, fuzzy C-means clustering, DBSCAN, and partitioning around medoids (k-medoids) clustering.Table of Contents1. Introduction; 2. Setting the stage for clustering; 3. Agglomerative hierarchical clustering; 4. k-means clustering; 5. Gaussian mixture models; 6. Advanced methods; 7. Conclusion.

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Book SynopsisModeling and Analysis of Compositional Data presents a practical and comprehensive introduction to the analysis of compositional data along with numerous examples to illustrate both theory and application of each method.Table of ContentsPreface xi About the Authors xv Acknowledgments xix 1 Introduction 1 2 Compositional Data and Their Sample Space 8 2.1 Basic concepts 8 2.2 Principles of compositional analysis 12 2.2.1 Scale invariance 12 2.2.2 Permutation invariance 15 2.2.3 Subcompositional coherence 16 2.3 Zeros, missing values, and other irregular components 16 2.3.1 Kinds of irregular components 16 2.3.2 Strategies to analyze irregular data 19 2.4 Exercises 21 3 The Aitchison Geometry 23 3.1 General comments 23 3.2 Vector space structure 24 3.3 Inner product, norm and distance 26 3.4 Geometric figures 28 3.5 Exercises 30 4 Coordinate Representation 32 4.1 Introduction 32 4.2 Compositional observations in real space 33 4.3 Generating systems 33 4.4 Orthonormal coordinates 36 4.5 Balances 38 4.6 Working on coordinates 43 4.7 Additive logratio coordinates (alr) 46 4.8 Orthogonal projections 48 4.9 Matrix operations in the simplex 54 4.9.1 Perturbation-linear combination of compositions 54 4.9.2 Linear transformations of óKòù: endomorphisms 55 4.9.3 Other matrix transformations on óKòù: nonlinear transformations 57 4.10 Coordinates leading to alternative Euclidean structures 59 4.11 Exercises 61 5 Exploratory Data Analysis 65 5.1 General remarks 65 5.2 Sample center, total variance, and variation matrix 66 5.3 Centering and scaling 68 5.4 The biplot: a graphical display 70 5.4.1 Construction of a biplot 70 5.4.2 Interpretation of a 2D compositional biplot 72 5.5 Exploratory analysis of coordinates 76 5.6 A geological example 79 5.7 Linear trends along principal components 85 5.8 A nutrition example 89 5.9 A political example 96 5.10 Exercises 100 6 Random Compositions 103 6.1 Sample space 103 6.1.1 Conventional approach to the sample space of compositions 105 6.1.2 A compositional approach to the sample space of compositions 106 6.1.3 Definitions related to random compositions 107 6.2 Variability and center 108 6.3 Probability distributions on the simplex 112 6.3.1 The normal distribution on the simplex 114 6.3.2 The Dirichlet distribution 121 6.3.3 Other distributions 127 6.4 Exercises 128 7 Statistical Inference 130 7.1 Point estimation of center and variability 130 7.2 Testing hypotheses on compositional normality 135 7.3 Testing hypotheses about two populations 136 7.4 Probability and confidence regions for normal data 142 7.5 Bayesian estimation with count data 144 7.6 Exercises 147 8 Linear Models 149 8.1 Linear regression with compositional response 150 8.2 Regression with compositional covariates 156 8.3 Analysis of variance with compositional response 160 8.4 Linear discrimination with compositional predictor 163 8.5 Exercises 165 9 Compositional Processes 172 9.1 Linear processes 173 9.2 Mixture processes 176 9.3 Settling processes 178 9.4 Simplicial derivative 183 9.5 Elementary differential equations 186 9.5.1 Constant derivative 187 9.5.2 Forced derivative 189 9.5.3 Complete first-order linear equation 194 9.5.4 Harmonic oscillator 200 9.6 Exercises 204 10 Epilogue 206 References 211 Appendix A Practical Recipes 222 A.1 Plotting a ternary diagram 222 A.2 Parameterization of an elliptic region 224 A.3 Matrix expressions of change of representation 226 Appendix B Random Variables 228 B.1 Probability spaces and random variables 228 B.2 Description of probability 232 List of Abbreviations and Symbols 234 Author Index 237 General Index 241

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Book SynopsisThis revised and updated edition of the bestseller provides a complete library of dimensional modeling techniques, the most comprehensive collection ever written.Table of ContentsIntroduction xxvii 1 Data Warehousing, Business Intelligence, and Dimensional Modeling Primer 1 Different Worlds of Data Capture and Data Analysis 2 Goals of Data Warehousing and Business Intelligence 3 Dimensional Modeling Introduction 7 Kimball’s DW/BI Architecture 18 Alternative DW/BI Architectures 26 Dimensional Modeling Myths 30 More Reasons to Think Dimensionally 32 Agile Considerations 34 Summary 35 2 Kimball Dimensional Modeling Techniques Overview 37 Fundamental Concepts 37 Basic Fact Table Techniques 41 Basic Dimension Table Techniques 46 Integration via Conformed Dimensions 50 Dealing with Slowly Changing Dimension Attributes 53 Dealing with Dimension Hierarchies 56 Advanced Fact Table Techniques 58 Advanced Dimension Techniques 62 Special Purpose Schemas 67 3 Retail Sales 69 Four-Step Dimensional Design Process 70 Retail Case Study 72 Dimension Table Details 79 Retail Schema in Action 94 Retail Schema Extensibility 95 Factless Fact Tables 97 Dimension and Fact Table Keys 98 Resisting Normalization Urges 104 Summary 109 4 Inventory 111 Value Chain Introduction 111 Inventory Models 112 Fact Table Types 119 Value Chain Integration 122 Enterprise Data Warehouse Bus Architecture 123 Conformed Dimensions 130 Conformed Facts 138 Summary 139 5 Procurement 141 Procurement Case Study 141 Procurement Transactions and Bus Matrix 142 Slowly Changing Dimension Basics 147 Hybrid Slowly Changing Dimension Techniques 159 Slowly Changing Dimension Recap 164 Summary 165 6 Order Management 167 Order Management Bus Matrix 168 Order Transactions 168 Invoice Transactions 187 Accumulating Snapshot for Order Fulfillment Pipeline 194 Summary 199 7 Accounting 201 Accounting Case Study and Bus Matrix 202 General Ledger Data 203 Budgeting Process 210 Dimension Attribute Hierarchies 214 Consolidated Fact Tables 224 Role of OLAP and Packaged Analytic Solutions 226 Summary 227 8 Customer Relationship Management 229 CRM Overview 230 Customer Dimension Attributes 233 Bridge Tables for Multivalued Dimensions 245 Complex Customer Behavior 249 Customer Data Integration Approaches 256 Low Latency Reality Check 260 Summary 261 9 Human Resources Management 263 Employee Profile Tracking 263 Headcount Periodic Snapshot 267 Bus Matrix for HR Processes 268 Packaged Analytic Solutions and Data Models 270 Recursive Employee Hierarchies 271 Multivalued Skill Keyword Attributes 274 Survey Questionnaire Data 277 Summary 279 10 Financial Services 281 Banking Case Study and Bus Matrix 282 Dimension Triage to Avoid Too Few Dimensions 283 Supertype and Subtype Schemas for Heterogeneous Products 293 Hot Swappable Dimensions 296 Summary 296 11 Telecommunications 297 Telecommunications Case Study and Bus Matrix 297 General Design Review Considerations 299 Design Review Guidelines 304 Draft Design Exercise Discussion 306 Remodeling Existing Data Structures 309 Geographic Location Dimension 310 Summary 310 12 Transportation 311 Airline Case Study and Bus Matrix 311 Extensions to Other Industries 317 Combining Correlated Dimensions 318 More Date and Time Considerations 321 Localization Recap 324 Summary 324 13 Education 325 University Case Study and Bus Matrix 325 Accumulating Snapshot Fact Tables 326 Factless Fact Tables 329 More Educational Analytic Opportunities 336 Summary 336 14 Healthcare 339 Healthcare Case Study and Bus Matrix 339 Claims Billing and Payments 342 Electronic Medical Records 348 Facility/Equipment Inventory Utilization 351 Dealing with Retroactive Changes 351 Summary 352 15 Electronic Commerce 353 Clickstream Source Data 353 Clickstream Dimensional Models 357 Integrating Clickstream into Web Retailer’s Bus Matrix 368 Profitability Across Channels Including Web 370 Summary 373 16 Insurance 375 Insurance Case Study 376 Policy Transactions 379 Premium Periodic Snapshot 385 More Insurance Case Study Background 388 Claim Transactions 390 Claim Accumulating Snapshot 392 Policy/Claim Consolidated Periodic Snapshot 395 Factless Accident Events 396 Common Dimensional Modeling Mistakes to Avoid 397 Summary 401 17 Kimball DW/BI Lifecycle Overview 403 Lifecycle Roadmap 404 Lifecycle Launch Activities 406 Lifecycle Technology Track 416 Lifecycle Data Track 420 Lifecycle BI Applications Track 422 Lifecycle Wrap-up Activities 424 Common Pitfalls to Avoid 426 Summary 427 18 Dimensional Modeling Process and Tasks 429 Modeling Process Overview 429 Get Organized 431 Design the Dimensional Model 434 Summary 441 19 ETL Subsystems and Techniques 443 Round Up the Requirements 444 The 34 Subsystems of ETL 449 Extracting: Getting Data into the Data Warehouse 450 Cleaning and Conforming Data 455 Delivering: Prepare for Presentation 463 Managing the ETL Environment 483 Summary 496 20 ETL System Design and Development Process and Tasks 497 ETL Process Overview 497 Develop the ETL Plan 498 Develop One-Time Historic Load Processing 503 Develop Incremental ETL Processing 512 Real-Time Implications 520 Summary 526 21 Big Data Analytics 527 Big Data Overview 527 Recommended Best Practices for Big Data 531 Summary 542 Index 543

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Book SynopsisProvides an important framework for data analysts in assessing the quality of data and its potential to provide meaningful insights through analysis Analytics and statistical analysis have become pervasive topics, mainly due to the growing availability of data and analytic tools. Technology, however, fails to deliver insights with added value if the quality of the information it generates is not assured. Information Quality (InfoQ) is a tool developed by the authors to assess the potential of a dataset to achieve a goal of interest, using data analysis. Whether the information quality of a dataset is sufficient is of practical importance at many stages of the data analytics journey, from the pre-data collection stage to the post-data collection and post-analysis stages. It is also critical to various stakeholders: data collection agencies, analysts, data scientists, and management. This book: Explains how to integrate the notions of goal, datTable of ContentsForeword ix About the authors xi Preface xii Quotes about the book xv About the companion website xviii PART I THE INFORMATION QUALITY FRAMEWORK 1 1 Introduction to information quality 3 2 Quality of goal, data quality, and analysis quality 18 3 Dimensions of information quality and InfoQ assessment 31 4 InfoQ at the study design stage 53 5 InfoQ at the postdata collection stage 67 PART II APPLICATIONS OF InfoQ 79 6 Education 81 7 Customer surveys 109 8 Healthcare 134 9 Risk management 160 10 Official statistics 181 PART III IMPLEMENTING InfoQ 219 11 InfoQ and reproducible research 221 12 InfoQ in review processes of scientific publications 234 13 Integrating InfoQ into data science analytics programs, research methods courses, and more 252 14 InfoQ support with R 265 15 InfoQ support with Minitab 295 16 InfoQ support with JMP 324 Index 351

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Book SynopsisA valuable new edition of a standard reference The use of statistical methods for categorical data has increased dramatically, particularly for applications in the biomedical and social sciences. An Introduction to Categorical Data Analysis, Third Edition summarizes these methods and shows readers how to use them using software. Readers will find a unified generalized linear models approach that connects logistic regression and loglinear models for discrete data with normal regression for continuous data. Adding to the value in the new edition is: Illustrations of the use of R software to perform all the analyses in the book A new chapter on alternative methods for categorical data, including smoothing and regularization methods (such as the lasso), classification methods such as linear discriminant analysis and classification trees, and cluster analysis New sections in many chapters introducing the Bayesian approach for the methodTable of ContentsPreface ix About the Companion Website xiii 1 Introduction 1 1.1 Categorical Response Data 1 1.2 Probability Distributions for Categorical Data 3 1.3 Statistical Inference for a Proportion 5 1.4 Statistical Inference for Discrete Data 10 1.5 Bayesian Inference for Proportions * 13 1.6 Using R Software for Statistical Inference about Proportions * 17 Exercises 21 2 Analyzing Contingency Tables 25 2.1 Probability Structure for Contingency Tables 26 2.2 Comparing Proportions in 2 × 2 Contingency Tables 29 2.3 The Odds Ratio 31 2.4 Chi-Squared Tests of Independence 36 2.5 Testing Independence for Ordinal Variables 42 2.6 Exact Frequentist and Bayesian Inference * 46 2.7 Association in Three-Way Tables 52 Exercises 56 3 Generalized Linear Models 65 3.1 Components of a Generalized Linear Model 66 3.2 Generalized Linear Models for Binary Data 68 3.3 Generalized Linear Models for Counts and Rates 72 3.4 Statistical Inference and Model Checking 76 3.5 Fitting Generalized Linear Models 82 Exercises 84 4 Logistic Regression 89 4.1 The Logistic Regression Model 89 4.2 Statistical Inference for Logistic Regression 94 4.3 Logistic Regression with Categorical Predictors 98 4.4 Multiple Logistic Regression 102 4.5 Summarizing Effects in Logistic Regression 107 4.6 Summarizing Predictive Power: Classification Tables, ROC Curves, and Multiple Correlation 110 Exercises 113 5 Building and Applying Logistic Regression Models 123 5.1 Strategies in Model Selection 123 5.2 Model Checking 130 5.3 Infinite Estimates in Logistic Regression 136 5.4 Bayesian Inference, Penalized Likelihood, and Conditional Likelihood for Logistic Regression * 140 5.5 Alternative Link Functions: Linear Probability and Probit Models * 145 5.6 Sample Size and Power for Logistic Regression * 150 Exercises 151 6 Multicategory Logit Models 159 6.1 Baseline-Category Logit Models for Nominal Responses 159 6.2 Cumulative Logit Models for Ordinal Responses 167 6.3 Cumulative Link Models: Model Checking and Extensions * 176 6.4 Paired-Category Logit Modeling of Ordinal Responses * 184 Exercises 187 7 Loglinear Models for Contingency Tables and Counts 193 7.1 Loglinear Models for Counts in Contingency Tables 194 7.2 Statistical Inference for Loglinear Models 200 7.3 The Loglinear – Logistic Model Connection 207 7.4 Independence Graphs and Collapsibility 210 7.5 Modeling Ordinal Associations in Contingency Tables 214 7.6 Loglinear Modeling of Count Response Variables * 217 Exercises 221 8 Models for Matched Pairs 227 8.1 Comparing Dependent Proportions for Binary Matched Pairs 228 8.2 Marginal Models and Subject-Specific Models for Matched Pairs 230 8.3 Comparing Proportions for Nominal Matched-Pairs Responses 235 8.4 Comparing Proportions for Ordinal Matched-Pairs Responses 239 8.5 Analyzing Rater Agreement * 243 8.6 Bradley–Terry Model for Paired Preferences * 247 Exercises 249 9 Marginal Modeling of Correlated, Clustered Responses 253 9.1 Marginal Models Versus Subject-Specific Models 254 9.2 Marginal Modeling: The Generalized Estimating Equations (GEE) Approach 255 9.3 Marginal Modeling for Clustered Multinomial Responses 260 9.4 Transitional Modeling, Given the Past 263 9.5 Dealing with Missing Data * 266 Exercises 268 10 Random Effects: Generalized Linear Mixed Models 273 10.1 Random Effects Modeling of Clustered Categorical Data 273 10.2 Examples: Random Effects Models for Binary Data 278 10.3 Extensions to Multinomial Responses and Multiple Random Effect Terms 284 10.4 Multilevel (Hierarchical) Models 288 10.5 Latent Class Models * 291 Exercises 295 11 Classification and Smoothing * 299 11.1 Classification: Linear Discriminant Analysis 300 11.2 Classification: Tree-Based Prediction 302 11.3 Cluster Analysis for Categorical Responses 306 11.4 Smoothing: Generalized Additive Models 310 11.5 Regularization for High-Dimensional Categorical Data (Large p) 313 Exercises 321 12 A Historical Tour of Categorical Data Analysis * 325 Appendix: Software for Categorical Data Analysis 331 A.1 R for Categorical Data Analysis 331 A.2 SAS for Categorical Data Analysis 332 A.3 Stata for Categorical Data Analysis 342 A.4 SPSS for Categorical Data Analysis 346 Brief Solutions to Odd-Numbered Exercises 349 Bibliography 363 Examples Index 365 Subject Index 369

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Book SynopsisAchieve best-in-class metrics and get more from your data with JMP JMP Connections is the small- and medium-sized business owner''s guide to exceeding customer expectations by getting more out of your data using JMP. Uniquely bifunctional, this book is divided into two parts: the first half of the book shows you what JMP can do for you. You''ll discover how to wring every last drop of insight out of your data, and let JMP parse reams of raw numbers into actionable insight that leads to better strategic decisions. You''ll also discover why it works so well; clear explanations break down the Connectivity platform and metrics in business terms to demystify data analysis and JMP while giving you a macro view of the benefits that come from optimal implementation. The second half of the book is for your technical team, demonstrating how to implement specific solutions relating to data set development and data virtualization. In the end, your organizationTable of ContentsPreface xv Chapter 1 Generalized Context for Decision Process Improvement 1 1.1 Situational Assessment (Current State) 3 1.2 Problem Statement 11 1.3 Visualizing State Transition 15 1.4 Metrics On-Demand 20 Chapter 2 Real-Time Metrics Business Case 25 2.1 Project Description and Objectives—A Case Study 27 2.2 Solution Description 31 2.3 Cost and Benefit Analysis 34 2.4 Financial Assessment 37 2.5 Implementation Timeline 42 2.5.1 Contemplating Startup 42 2.5.2 Skills Dependencies and Timeline Consideration 44 2.5.3 Implementation Starting Point 46 2.5.4 Implementation to Deployment 49 2.6 Critical Assumptions and Risk Assessment 50 2.6.1 Critical Assumptions 50 2.6.2 Risk Assessment 51 2.7 Recommendations: Transmigrate the Enterprise 58 Chapter 3 Technical Details and Practical Implementation 63 3.1 Hardware Foundations 69 3.2 Solution Stack 70 3.3 Integration of Hardware and Software Infrastructure 72 3.4 Build Out 72 3.5 The Construction of a Metric 79 3.6 Metric Case Study 80 Chapter 4 Harvesting Benefits and Extensibility 99 4.1 Benefits Example 100 4.2 Extensibility 101 4.3 Configuration Management Version Control 102 Chapter 5 So What About a Bad Economy? 107 5.1 Overachievement—Data Virtualization 110 5.2 JMP Connection as the Universal Server 114 5.3 Well-Formed Data 117 5.4 Linked Data 120 Chapter 6 Decision Streams 133 Chapter 7 Delivery and Presentations 139 7.1 Push Versus Pull Delivery 140 7.2 Presentation 143 7.3 DIY, But Leave the Poor Bi Person Alone! 156 7.4 Advanced Presentation Method 157 Chapter 8 In Closing (As-Built) 163 Glossary 169 Appendix A Server-Side PHP Code 173 Appendix B JMP JSL Time Constant Learning Curve Script 175 Appendix C JMP GUI User Interface Code Example 181 Appendix D Resource Description Framework File Example 185 Appendix E Sample Hardware Requirements 191 Appendix F Early Warning Deliverable 193 Appendix G JMP PRO Connections: The Transversality of the Capability Maturity Model 203 G.1 Tangential Concept 204 G.2 Transversal Concept 205 G.3 Univariate to Multivariate Process Control 206 G.4 JMP Process Screening 208 G.5 Transversal Maturity Space in Relation to JMP PRO Features 210 G.6 Summary 212 References 213 Suggested Reading 217 Index 219

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Book Synopsis

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Book SynopsisAdvanced Analytics and Deep Learning Models The book provides readers with an in-depth understanding of concepts and technologies related to the importance of analytics and deep learning in many useful real-world applications such as e-healthcare, transportation, agriculture, stock market, etc. Advanced analytics is a mixture of machine learning, artificial intelligence, graphs, text mining, data mining, semantic analysis. It is an approach to data analysis. Beyond the traditional business intelligence, it is a semi and autonomous analysis of data by using different techniques and tools. However, deep learning and data analysis both are high centers of data science. Almost all the private and public organizations collect heavy amounts of data, i.e., domain-specific data. Many small/large companies are exploring large amounts of data for existing and future technology. Deep learning is also exploring large amounts of unsupervised data making it beneficial and effective for big data. DeeTable of ContentsPreface xix Part 1: Introduction to Computer Vision 1 1 Artificial Intelligence in Language Learning: Practices and Prospects 3Khushboo Kuddus 1.1 Introduction 4 1.2 Evolution of CALL 5 1.3 Defining Artificial Intelligence 7 1.4 Historical Overview of AI in Education and Language Learning 7 1.5 Implication of Artificial Intelligence in Education 8 1.5.1 Machine Translation 9 1.5.2 Chatbots 9 1.5.3 Automatic Speech Recognition Tools 9 1.5.4 Autocorrect/Automatic Text Evaluator 11 1.5.5 Vocabulary Training Applications 12 1.5.6 Google Docs Speech Recognition 12 1.5.7 Language MuseTM Activity Palette 13 1.6 Artificial Intelligence Tools Enhance the Teaching and Learning Processes 13 1.6.1 Autonomous Learning 13 1.6.2 Produce Smart Content 13 1.6.3 Task Automation 13 1.6.4 Access to Education for Students with Physical Disabilities 14 1.7 Conclusion 14 References 15 2 Real Estate Price Prediction Using Machine Learning Algorithms 19Palak Furia and Anand Khandare 2.1 Introduction 20 2.2 Literature Review 20 2.3 Proposed Work 21 2.3.1 Methodology 21 2.3.2 Work Flow 22 2.3.3 The Dataset 22 2.3.4 Data Handling 23 2.3.4.1 Missing Values and Data Cleaning 23 2.3.4.2 Feature Engineering 24 2.3.4.3 Removing Outliers 25 2.4 Algorithms 27 2.4.1 Linear Regression 27 2.4.2 LASSO Regression 27 2.4.3 Decision Tree 28 2.4.4 Support Vector Machine 28 2.4.5 Random Forest Regressor 28 2.4.6 XGBoost 29 2.5 Evaluation Metrics 29 2.6 Result of Prediction 30 References 31 3 Multi-Criteria–Based Entertainment Recommender System Using Clustering Approach 33Chandramouli Das, Abhaya Kumar Sahoo and Chittaranjan Pradhan 3.1 Introduction 34 3.2 Work Related Multi-Criteria Recommender System 35 3.3 Working Principle 38 3.3.1 Modeling Phase 39 3.3.2 Prediction Phase 39 3.3.3 Recommendation Phase 40 3.3.4 Content-Based Approach 40 3.3.5 Collaborative Filtering Approach 41 3.3.6 Knowledge-Based Filtering Approach 41 3.4 Comparison Among Different Methods 42 3.4.1 MCRS Exploiting Aspect-Based Sentiment Analysis 42 3.4.1.1 Discussion and Result 43 3.4.2 User Preference Learning in Multi-Criteria Recommendation Using Stacked Autoencoders by Tallapally et al. 46 3.4.2.1 Dataset and Evaluation Matrix 46 3.4.2.2 Training Setting 49 3.4.2.3 Result 49 3.4.3 Situation-Aware Multi-Criteria Recommender System: Using Criteria Preferences as Contexts by Zheng 49 3.4.3.1 Evaluation Setting 50 3.4.3.2 Experimental Result 50 3.4.4 Utility-Based Multi-Criteria Recommender Systems by Zheng 51 3.4.4.1 Experimental Dataset 51 3.4.4.2 Experimental Result 52 3.4.5 Multi-Criteria Clustering Approach by Wasid and Ali 53 3.4.5.1 Experimental Evaluation 53 3.4.5.2 Result and Analysis 53 3.5 Advantages of Multi-Criteria Recommender System 54 3.5.1 Revenue 57 3.5.2 Customer Satisfaction 57 3.5.3 Personalization 57 3.5.4 Discovery 58 3.5.5 Provide Reports 58 3.6 Challenges of Multi-Criteria Recommender System 58 3.6.1 Cold Start Problem 58 3.6.2 Sparsity Problem 59 3.6.3 Scalability 59 3.6.4 Over Specialization Problem 59 3.6.5 Diversity 59 3.6.6 Serendipity 59 3.6.7 Privacy 60 3.6.8 Shilling Attacks 60 3.6.9 Gray Sheep 60 3.7 Conclusion 60 References 61 4 Adoption of Machine/Deep Learning in Cloud With a Case Study on Discernment of Cervical Cancer65Jyothi A. P., S. Usha and Archana H. R. 4.1 Introduction 66 4.2 Background Study 69 4.3 Overview of Machine Learning/Deep Learning 72 4.4 Connection Between Machine Learning/Deep Learning and Cloud Computing 74 4.5 Machine Learning/Deep Learning Algorithm 74 4.5.1 Supervised Learning 74 4.5.2 Unsupervised Learning 77 4.5.3 Reinforcement or Semi-Supervised Learning 77 4.5.3.1 Outline of ML Algorithms 77 4.6 A Project Implementation on Discernment of Cervical Cancer by Using Machine/Deep Learning in Cloud 93 4.6.1 Proposed Work 94 4.6.1.1 MRI Dataset 94 4.6.1.2 Pre Processing 95 4.6.1.3 Feature Extraction 96 4.6.2 Design Methodology and Implementation 97 4.6.3 Results 100 4.7 Applications 101 4.7.1 Cognitive Cloud 102 4.7.2 Chatbots and Smart Personal Assistants 103 4.7.3 IoT Cloud 103 4.7.4 Business Intelligence 103 4.7.5 AI-as-a-Service 104 4.8 Advantages of Adoption of Cloud in Machine Learning/ Deep Learning 104 4.9 Conclusion 105 References 106 5 Machine Learning and Internet of Things–Based Models for Healthcare Monitoring 111Shruti Kute, Amit Kumar Tyagi, Aswathy S.U. and Shaveta Malik 5.1 Introduction 112 5.2 Literature Survey 113 5.3 Interpretable Machine Learning in Healthcare 114 5.4 Opportunities in Machine Learning for Healthcare 116 5.5 Why Combining IoT and ML? 119 5.5.1 ML-IoT Models for Healthcare Monitoring 119 5.6 Applications of Machine Learning in Medical and Pharma 121 5.7 Challenges and Future Research Direction 122 5.8 Conclusion 123 References 123 6 Machine Learning–Based Disease Diagnosis and Prediction for E-Healthcare System 127Shruti Suhas Kute, Shreyas Madhav A. V., Shabnam Kumari and Aswathy S. U. 6.1 Introduction 128 6.2 Literature Survey 129 6.3 Machine Learning Applications in Biomedical Imaging 132 6.4 Brain Tumor Classification Using Machine Learning and IoT 134 6.5 Early Detection of Dementia Disease Using Machine Learning and IoT-Based Applications 135 6.6 IoT and Machine Learning-Based Diseases Prediction and Diagnosis System for EHRs 137 6.7 Machine Learning Applications for a Real-Time Monitoring of Arrhythmia Patients Using IoT 140 6.8 IoT and Machine Learning–Based System for Medical Data Mining 141 6.9 Conclusion and Future Works 143 References 144 Part 2: Introduction to Deep Learning and its Models 149 7 Deep Learning Methods for Data Science 151K. Indira, Kusumika Krori Dutta, S. Poornima and Sunny Arokia Swamy Bellary 7.1 Introduction 152 7.2 Convolutional Neural Network 152 7.2.1 Architecture 154 7.2.2 Implementation of CNN 154 7.2.3 Simulation Results 157 7.2.4 Merits and Demerits 158 7.2.5 Applications 159 7.3 Recurrent Neural Network 159 7.3.1 Architecture 160 7.3.2 Types of Recurrent Neural Networks 161 7.3.2.1 Simple Recurrent Neural Networks 161 7.3.2.2 Long Short-Term Memory Networks 162 7.3.2.3 Gated Recurrent Units (GRUs) 164 7.3.3 Merits and Demerits 167 7.3.3.1 Merits 167 7.3.3.2 Demerits 167 7.3.4 Applications 167 7.4 Denoising Autoencoder 168 7.4.1 Architecture 169 7.4.2 Merits and Demerits 169 7.4.3 Applications 170 7.5 Recursive Neural Network (RCNN) 170 7.5.1 Architecture 170 7.5.2 Merits and Demerits 172 7.5.3 Applications 172 7.6 Deep Reinforcement Learning 173 7.6.1 Architecture 174 7.6.2 Merits and Demerits 174 7.6.3 Applications 174 7.7 Deep Belief Networks (DBNS) 175 7.7.1 Architecture 176 7.7.2 Merits and Demerits 176 7.7.3 Applications 176 7.8 Conclusion 177 References 177 8 A Proposed LSTM-Based Neuromarketing Model for Consumer Emotional State Evaluation Using EEG 181Rupali Gill and Jaiteg Singh 8.1 Introduction 182 8.2 Background and Motivation 183 8.2.1 Emotion Model 183 8.2.2 Neuromarketing and BCI 184 8.2.3 EEG Signal 185 8.3 Related Work 185 8.3.1 Machine Learning 186 8.3.2 Deep Learning 191 8.3.2.1 Fast Feed Neural Networks 193 8.3.2.2 Recurrent Neural Networks 193 8.3.2.3 Convolutional Neural Networks 194 8.4 Methodology of Proposed System 195 8.4.1 DEAP Dataset 196 8.4.2 Analyzing the Dataset 196 8.4.3 Long Short-Term Memory 197 8.4.4 Experimental Setup 197 8.4.5 Data Set Collection 197 8.5 Results and Discussions 198 8.5.1 LSTM Model Training and Accuracy 198 8.6 Conclusion 199 References 199 9 An Extensive Survey of Applications of Advanced Deep Learning Algorithms on Detection of Neurodegenerative Diseases and the Tackling Procedure in Their Treatment Protocol 207Vignesh Baalaji S., Vergin Raja Sarobin M., L. Jani Anbarasi, Graceline Jasmine S. and Rukmani P. 9.1 Introduction 208 9.2 Story of Alzheimer’s Disease 208 9.3 Datasets 210 9.3.1 ADNI 210 9.3.2 OASIS 210 9.4 Story of Parkinson’s Disease 211 9.5 A Review on Learning Algorithms 212 9.5.1 Convolutional Neural Network (CNN) 212 9.5.2 Restricted Boltzmann Machine 213 9.5.3 Siamese Neural Networks 213 9.5.4 Residual Network (ResNet) 214 9.5.5 U-Net 214 9.5.6 LSTM 214 9.5.7 Support Vector Machine 215 9.6 A Review on Methodologies 215 9.6.1 Prediction of Alzheimer’s Disease 215 9.6.2 Prediction of Parkinson’s Disease 221 9.6.3 Detection of Attacks on Deep Brain Stimulation 223 9.7 Results and Discussion 224 9.8 Conclusion 224 References 227 10 Emerging Innovations in the Near Future Using Deep Learning Techniques 231Akshara Pramod, Harsh Sankar Naicker and Amit Kumar Tyagi 10.1 Introduction 232 10.2 Related Work 234 10.3 Motivation 235 10.4 Future With Deep Learning/Emerging Innovations in Near Future With Deep Learning 236 10.4.1 Deep Learning for Image Classification and Processing 237 10.4.2 Deep Learning for Medical Image Recognition 237 10.4.3 Computational Intelligence for Facial Recognition 238 10.4.4 Deep Learning for Clinical and Health Informatics 238 10.4.5 Fuzzy Logic for Medical Applications 239 10.4.6 Other Intelligent-Based Methods for Biomedical and Healthcare 239 10.4.7 Other Applications 239 10.5 Open Issues and Future Research Directions 244 10.5.1 Joint Representation Learning From User and Item Content Information 244 10.5.2 Explainable Recommendation With Deep Learning 245 10.5.3 Going Deeper for Recommendation 245 10.5.4 Machine Reasoning for Recommendation 246 10.5.5 Cross Domain Recommendation With Deep Neural Networks 246 10.5.6 Deep Multi-Task Learning for Recommendation 247 10.5.7 Scalability of Deep Neural Networks for Recommendation 247 10.5.8 Urge for a Better and Unified Evaluation 248 10.6 Deep Learning: Opportunities and Challenges 249 10.7 Argument with Machine Learning and Other Available Techniques 250 10.8 Conclusion With Future Work 251 Acknowledgement 252 References 252 11 Optimization Techniques in Deep Learning Scenarios: An Empirical Comparison 255Ajeet K. Jain, PVRD Prasad Rao and K. Venkatesh Sharma 11.1 Introduction 256 11.1.1 Background and Related Work 256 11.2 Optimization and Role of Optimizer in DL 258 11.2.1 Deep Network Architecture 259 11.2.2 Proper Initialization 260 11.2.3 Representation, Optimization, and Generalization 261 11.2.4 Optimization Issues 261 11.2.5 Stochastic GD Optimization 262 11.2.6 Stochastic Gradient Descent with Momentum 263 11.2.7 SGD With Nesterov Momentum 264 11.3 Various Optimizers in DL Practitioner Scenario 265 11.3.1 AdaGrad Optimizer 265 11.3.2 RMSProp 267 11.3.3 Adam 267 11.3.4 AdaMax 269 11.3.5 AMSGrad 269 11.4 Recent Optimizers in the Pipeline 270 11.4.1 EVE 270 11.4.2 RAdam 271 11.4.3 MAS (Mixing ADAM and SGD) 271 11.4.4 Lottery Ticket Hypothesis 272 11.5 Experiment and Results 273 11.5.1 Web Resource 273 11.5.2 Resource 277 11.6 Discussion and Conclusion 278 References 279 Part 3: Introduction to Advanced Analytics 283 12 Big Data Platforms 285Sharmila Gaikwad and Jignesh Patil 12.1 Visualization in Big Data 286 12.1.1 Introduction to Big Data 286 12.1.2 Techniques of Visualization 287 12.1.3 Case Study on Data Visualization 302 12.2 Security in Big Data 305 12.2.1 Introduction of Data Breach 305 12.2.2 Data Security Challenges 306 12.2.3 Data Breaches 307 12.2.4 Data Security Achieved 307 12.2.5 Findings: Case Study of Data Breach 309 12.3 Conclusion 309 References 309 13 Smart City Governance Using Big Data Technologies 311K. Raghava Rao and D. Sateesh Kumar 13.1 Objective 312 13.2 Introduction 312 13.3 Literature Survey 314 13.4 Smart Governance Status 314 13.4.1 International 314 13.4.2 National 316 13.5 Methodology and Implementation Approach 318 13.5.1 Data Generation 319 13.5.2 Data Acquisition 319 13.5.3 Data Analytics 319 13.6 Outcome of the Smart Governance 322 13.7 Conclusion 323 References 323 14 Big Data Analytics With Cloud, Fog, and Edge Computing 325Deepti Goyal, Amit Kumar Tyagi and Aswathy S. U. 14.1 Introduction to Cloud, Fog, and Edge Computing 326 14.2 Evolution of Computing Terms and Its Related Works 330 14.3 Motivation 332 14.4 Importance of Cloud, Fog, and Edge Computing in Various Applications 333 14.5 Requirement and Importance of Analytics (General) in Cloud, Fog, and Edge Computing 334 14.6 Existing Tools for Making a Reliable Communication and Discussion of a Use Case (with Respect to Cloud, Fog, and Edge Computing) 335 14.6.1 CloudSim 335 14.6.2 SPECI 336 14.6.3 Green Cloud 336 14.6.4 OCT (Open Cloud Testbed) 337 14.6.5 Open Cirrus 337 14.6.6 GroudSim 338 14.6.7 Network CloudSim 338 14.7 Tools Available for Advanced Analytics (for Big Data Stored in Cloud, Fog, and Edge Computing Environment) 338 14.7.1 Microsoft HDInsight 338 14.7.2 Skytree 339 14.7.3 Splice Machine 339 14.7.4 Spark 339 14.7.5 Apache SAMOA 339 14.7.6 Elastic Search 339 14.7.7 R-Programming 339 14.8 Importance of Big Data Analytics for Cyber-Security and Privacy for Cloud-IoT Systems 340 14.8.1 Risk Management 340 14.8.2 Predictive Models 340 14.8.3 Secure With Penetration Testing 340 14.8.4 Bottom Line 341 14.8.5 Others: Internet of Things-Based Intelligent Applications 341 14.9 An Use Case with Real World Applications (with Respect to Big Data Analytics) Related to Cloud, Fog, and Edge Computing 341 14.10 Issues and Challenges Faced by Big Data Analytics (in Cloud, Fog, and Edge Computing Environments) 342 14.10.1 Cloud Issues 343 14.11 Opportunities for the Future in Cloud, Fog, and Edge Computing Environments (or Research Gaps) 344 14.12 Conclusion 345 References 346 15 Big Data in Healthcare: Applications and Challenges 351V. Shyamala Susan, K. Juliana Gnana Selvi and Ir. Bambang Sugiyono Agus Purwono 15.1 Introduction 352 15.1.1 Big Data in Healthcare 352 15.1.2 The 5V’s Healthcare Big Data Characteristics 353 15.1.2.1 Volume 353 15.1.2.2 Velocity 353 15.1.2.3 Variety 353 15.1.2.4 Veracity 353 15.1.2.5 Value 353 15.1.3 Various Varieties of Big Data Analytical (BDA) in Healthcare 353 15.1.4 Application of Big Data Analytics in Healthcare 354 15.1.5 Benefits of Big Data in the Health Industry 355 15.2 Analytical Techniques for Big Data in Healthcare 356 15.2.1 Platforms and Tools for Healthcare Data 357 15.3 Challenges 357 15.3.1 Storage Challenges 357 15.3.2 Cleaning 358 15.3.3 Data Quality 358 15.3.4 Data Security 358 15.3.5 Missing or Incomplete Data 358 15.3.6 Information Sharing 358 15.3.7 Overcoming the Big Data Talent and Cost Limitations 359 15.3.8 Financial Obstructions 359 15.3.9 Volume 359 15.3.10 Technology Adoption 360 15.4 What is the Eventual Fate of Big Data in Healthcare Services? 360 15.5 Conclusion 361 References 361 16 The Fog/Edge Computing: Challenges, Serious Concerns, and the Road Ahead 365Varsha. R., Siddharth M. Nair and Amit Kumar Tyagi 16.1 Introduction 366 16.1.1 Organization of the Work 368 16.2 Motivation 368 16.3 Background 369 16.4 Fog and Edge Computing–Based Applications 371 16.5 Machine Learning and Internet of Things–Based Cloud, Fog, and Edge Computing Applications 374 16.6 Threats Mitigated in Fog and Edge Computing–Based Applications 376 16.7 Critical Challenges and Serious Concerns Toward Fog/Edge Computing and Its Applications 378 16.8 Possible Countermeasures 381 16.9 Opportunities for 21st Century Toward Fog and Edge Computing 383 16.9.1 5G and Edge Computing as Vehicles for Transformation of Mobility in Smart Cities 383 16.9.2 Artificial Intelligence for Cloud Computing and Edge Computing 384 16.10 Conclusion 387 References 387 Index 391

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Book SynopsisA step-by-step guide for business leaders who need to manage successful big data projects Leading in Analytics: The Critical Tasks for Executives to Master in the Age of Big Data takes you through the entire process of guiding an analytics initiative from inception to execution. You'll learn which aspects of the project to pay attention to, the right questions to ask, and how to keep the project team focused on its mission to produce relevant and valuable project. As an executive, you can't control every aspect of the process. But if you focus on high-impact factors that you can control, you can ensure an effective outcome. This book describes those factors and offers practical insight on how to get them right. Drawn from best-practice research in the field of analytics, the Manageable Tasks described in this book are specific to the goal of implementing big data tools at an enterprise level. A dream team of analytics and business experts have contributed their knowledge to show you how to choose the right business problem to address, put together the right team, gather the right data, select the right tools, and execute your strategic plan to produce an actionable result. Become an analytics-savvy executive with this valuable book. Ensure the success of analytics initiatives, maximize ROI, and draw value from big dataLearn to define success and failure in analytics and big data projectsSet your organization up for analytics success by identifying problems that have big data solutionsBring together the people, the tools, and the strategies that are right for the jobBy learning to pay attention to critical tasks in every analytics project, non-technical executives and strategic planners can guide their organizations to measurable results.Table of ContentsForeword by Dr. Tim Rahschulte xi Acknowledgments xv Introduction: The Last Analytics Mile 1 The Last Mile to Analytics Success 1 Expert Contributors 4 Task 0 Analytics Leadership 11 Knowledge Begins in Failure 12 From Failure to Success 17 The Seven Tasks for Analytics Success 22 Chapter Summary and Exercises 24 Task 1 The Problem 27 Solve the Right Problem 28 The DAD Framework for Analytics Action 28 Finding Valuable Problems to Solve 39 The Problem Statement 45 Checking for Project Viability 50 Prioritizing Viable Projects 52 Chapter Summary and Exercises 54 Task 2 The Team 61 Building a Winning Analytics Team 62 Building and Managing Your Team 73 Managing the Technical Team 81 Engaging Your Team 86 Chapter Summary and Exercises 89 Task 3 The Data 91 Amorphous Asset 92 Understanding Data’s Value 92 Identifying Valuable Data 97 Harnessing Data’s Value 101 A Few Vs to Enhance Value 107 Quality Data 114 Chapter Summary and Exercises 121 Task 4 The Tools 125 Analytics Mindset 126 Executives’ Role in Tools 127 Categories of Analytics 131 Predictive Analytics Tools 146 Prescriptive Analytics Tools 151 Tool Synergies 155 Limits of Analytics Tools 157 Chapter Summary and Exercises 158 Task 5 Execution 161 Execute = Action 162 Process 163 People 177 Problems 186 Chapter Summary and Exercises 189 Task 6 Analytics Maturity 191 Defining Analytics Maturity 192 Visualizing Analytics Maturity 194 Growing Analytics Maturity 210 Tools for Maturity 218 Chapter Summary and Exercises 225 Task 7 Responsible Analytics 227 Our Analytics Responsibility 228 Analytics Discernment 229 INFORMS Ethical Guidelines 232 Analytics for Good 241 Being Responsible for Our Analytics Future 248 Chapter Summary and Exercises 261 Conclusion: Crossing the Last Mile 265 We Must Cross It Together 265 Additional Learning Opportunities 268 Lasting Principles for Success 269 Afterword: Dr. Karl Kempf’s Legacy 271 Pioneering Analytics with Formula One Racing 271 Teaching Superman to Fly 273 Automating Aerospace Manufacturing 273 Making Better Decisions at Intel 273 Author’s Tribute 274 About the Author 275 Why Read Leading in Analytics 277 Author Index 279 Subject Index 283

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Book SynopsisOptimization techniques are at the core of data science, including data analysis and machine learning. An understanding of basic optimization techniques and their fundamental properties provides important grounding for students, researchers, and practitioners in these areas. This text covers the fundamentals of optimization algorithms in a compact, self-contained way, focusing on the techniques most relevant to data science. An introductory chapter demonstrates that many standard problems in data science can be formulated as optimization problems. Next, many fundamental methods in optimization are described and analyzed, including: gradient and accelerated gradient methods for unconstrained optimization of smooth (especially convex) functions; the stochastic gradient method, a workhorse algorithm in machine learning; the coordinate descent approach; several key algorithms for constrained optimization problems; algorithms for minimizing nonsmooth functions arising in data science; foundTrade Review'This delightful compact tome gives the reader all the results they should have in their pocket to contribute to optimization and statistical learning. With the clean, elegant derivations of many of the foundational optimization methods underlying modern large-scale data analysis, everyone from students just getting started to researchers knowing this book inside and out will be well-positioned for both using the algorithms and developing new ones for machine learning, optimization, and statistics.' John C. Duchi, Stanford University'Optimization algorithms play a vital role in the rapidly evolving field of machine learning, as well as in signal processing, statistics and control. Numerical optimization is a vast field, however, and a student wishing to learn the methods required in the world of data science could easily get lost in the literature. This book does a superb job of presenting the most important algorithms, providing both their mathematical foundations and lucid motivations for their development. Written by two of the foremost experts in the field, this book gently guides a reader without prior knowledge of optimization towards the methods and concepts that are central in modern data science applications.' Jorge Nocedal, Northwestern University'This timely introductory book gives a rigorous view of continuous optimization techniques which are being used in machine learning. It is an excellent resource for those who are interested in understanding the mathematical concepts behind commonly used machine learning techniques.' Shai Shalev-Shwartz, Hebrew University of Jerusalem'This textbook is a much-needed exposition of optimization techniques, presented with conciseness and precision, with emphasis on topics most relevant for data science and machine learning applications. I imagine that this book will be immensely popular in university courses across the globe, and become a standard reference used by researchers in the area.' Amitabh Basu, Johns Hopkins UniversityTable of Contents1. Introduction; 2. Foundations of smooth optimization; 3. Descent methods; 4. Gradient methods using momentum; 5. Stochastic gradient; 6. Coordinate descent; 7. First-order methods for constrained optimization; 8. Nonsmooth functions and subgradients; 9. Nonsmooth optimization methods; 10. Duality and algorithms; 11. Differentiation and adjoints.

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Book SynopsisData assimilation is a hugely important mathematical technique, relevant in fields as diverse as geophysics, data science, and neuroscience. This modern book provides an authoritative treatment of the field as it relates to several scientific disciplines, with a particular emphasis on recent developments from machine learning and its role in the optimisation of data assimilation. Underlying theory from statistical physics, such as path integrals and Monte Carlo methods, are developed in the text as a basis for data assimilation, and the author then explores examples from current multidisciplinary research such as the modelling of shallow water systems, ocean dynamics, and neuronal dynamics in the avian brain. The theory of data assimilation and machine learning is introduced in an accessible and unified manner, and the book is suitable for undergraduate and graduate students from science and engineering without specialized experience of statistical physics.Table of Contents1. Prologue: linking 'The Future' with the present; 2. A data assimilation reminder; 3. Remembrance of things path; 4. SDA variational principles; Euler–Lagrange equations and Hamiltonian formulation; 5. Using waveform information; 6. Annealing in the model precision Rf; 7. Discrete time integration in data assimilation variational principles; Lagrangian and Hamiltonian formulations; 8. Monte Carlo methods; 9. Machine learning and its equivalence to statistical data assimilation; 10. Two examples of the practical use of data assimilation; 11. Unfinished business; Bibliography; Index.

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Book SynopsisDiscover how to achieve business goals by relying on high-quality, robust data In Data Quality: Empowering Businesses with Analytics and AI, veteran data and analytics professional delivers a practical and hands-on discussion on how to accelerate business results using high-quality data. In the book, you'll learn techniques to define and assess data quality, discover how to ensure that your firm's data collection practices avoid common pitfalls and deficiencies, improve the level of data quality in the business, and guarantee that the resulting data is useful for powering high-level analytics and AI applications. The author shows you how to: Profile for data quality, including the appropriate techniques, criteria, and KPIs Identify the root causes of data quality issues in the business apart from discussing the 16 common root causes that degrade data quality in the organization. Formulate the reference architecture for data quality, inTable of ContentsForeword by Bill Inmon Preface About the Book Quality Principles Applied in This Book Organization of the Book Who Should Read This Book? References Acknowledgments Define Phase Chapter 1: Introduction Introduction Data, Analytics, AI, and Business Performance Data as a Business Asset or Liability Data Governance, Data Management, and Data Quality Leadership Commitment to Data Quality Key Takeaways Conclusion References Chapter 2: Business Data Introduction Data in Business Telemetry Data Purpose of Data in Business Business Data Views Key Characteristics of Business Data Critical Data Elements (CDE) Key Takeaways Conclusion References Chapter 3: Data Quality in Business Introduction Data Quality Dimensions Context in Data Quality Consequences and Costs of Poor Data Quality Data Depreciation and Its Factors Data in IT Systems Data Quality and Trusted Information Key Takeaways Conclusion References Analyze Phase Chapter 4: Causes for Poor Data Quality Introduction Data Quality RCA Techniques Typical Causes of Poor Data Quality Key Takeaways Conclusion References Chapter 5: Data Lifecycle and Lineage Introduction Business-Enabled DLC Stages IT Business-Enabled DLC Stages Data Lineage Key Takeaways Conclusion References Chapter 6: Profiling for Data Quality Introduction Criteria for Data Profiling Data Profiling Techniques for Measures of Centrality Data Profiling Techniques for Measures of Variation Integrating Centrality and Variation KPIs Key Takeaways Conclusion References Realize Phase Chapter 7: Reference Architecture for Data Quality Introduction Options to Remediate Data Quality DataOps Data Product Data Fabric and Data Mesh Data Enrichment Key Takeaways Conclusion References Chapter 8: Best Practices to Realize Data Quality Introduction Overview of Best Practices BP 1: Identify the Business KPIs and the Ownership of These KPIs and the Pertinent Data BP 2: Build and Improve the Data Culture and Literacy in the Organization BP 3: Define the Current and Desired state of Data Quality BP 4: Follow the Minimalistic Approach to Data Capture BP 5: Select and Define the Data Attributes for Data Quality BP 6: Capture and Manage Critical Data with Data Standards in MDM Systems Key Takeaways Conclusion References Chapter 9: Best Practices to Realize Data Quality Introduction BP 7: Automate the Integration of Critical Data Elements BP 8: Define the SoR and Securely Capture Transactional Data in the SoR/OLTP System BP 9: Build and Manage Robust Data Integration Capabilities BP 10: Distribute Data Sourcing and Insight Consumption Key Takeaways Conclusion References Sustain Phase Chapter 10: Data Governance Introduction Data Governance Principles Data Governance Design Components Implementing the Data Governance Program Data Observability Data Compliance – ISO 27001 and SOC2 Key Takeaways Conclusion References Chapter 11: Protecting Data Introduction Data Classification Data Safety Data Security Key Takeaways Conclusion References Chapter 12: Data Ethics Introduction Data Ethics Importance of Data Ethics Principles of Data Ethics Model Drift in Data Ethics Data Privacy Managing Data Ethically Key Takeaways Conclusion References Appendix 1: Abbreviations and Acronyms Appendix 2: Glossary Appendix 3: Data Literacy Competencies About the Author Index

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Book SynopsisDiscover the utility of your organization's data with Microsoft Power BI In Beginning Power BI for Business Users: Learning to Turn Data into Insights, accomplished data professional and business intelligence expert Paul Fuller delivers an intuitive and accessible handbook for professionals seeking to use Microsoft's Power BI to access, analyze, understand, report, and act on the data available to their organizations. In the book, you'll discover Power BI's robust feature set, learn to ingest and model data, visualize and report on that data, and even use the DAX scripting language to unlock still more utility from Microsoft's popular program. Beginning with general principles geared to readers with no or little experience with reporting or data analytics tools, the author walks you through how to manipulate common, publicly available data sourcesincluding Excel files and relational databases. You'll also learn to: Use the included and testedTable of ContentsIntroduction xxi Part 1: Introduction Chapter 1: Introducing Power BI 3 What Is Power BI? 3 Why Would I Use Power BI? 7 But Why? 7 Disconnected Data 8 Fixed Formulas 9 Ten Reasons Why Power BI Transforms Your Work 10 Will I Still Use Excel? 12 How Much Is this Going to Cost? 14 Where Do We Go from Here? 16 Chapter 2: From Data to Insight 17 Three Guiding Principles 17 Distinguishing Data, Information, and Knowledge 18 Follow a Consistent, Proven Method 21 Data Sources 21 Files 21 Databases 22 Online Services 23 Plan Ahead 23 Import Data 25 Prepare Data 26 Model Data 28 Report Data 29 Analyze Data 30 Use Visualizations Well 31 Three Ways to Ruin Visual Analytics 32 Use as Many Cool Gadgets as You Can Find 32 Overwhelm the User with Tons of Detail 32 Pick the Wrong Tool for the Job 34 Four Ways to Improve Visual Analytics 35 Reduce the Nondata Pixels 35 Enhance the Data Pixels 36 Conclusion 38 Part 2: Power BI Guided Walk-Throughs Chapter 3: Let’s Take a Flyover! 41 Getting Connected 41 A Quick Tour of the Power BI Service 42 Frequented Hotspots of Power BI 42 Adding Sample Content 43 Understanding the Power BI Service Interface 44 Highlighting 47 Slicers 50 Filters 52 Drill-Down 54 Dashboards 55 Drill-Through 58 Visibility 59 Show as Table 59 Sorting Visuals 60 Filtering— One More Time 61 Power BI Building Blocks 61 Flyover of Power BI Desktop 62 Navigating Power BI Desktop 63 Conclusion 65 Chapter 4: Building Your First Report 67 Connecting to a Data Source 67 Examining the Data Model 68 Exploring the Data 75 Building Your Report 85 Designing for Mobile Devices 101 Conclusion 102 Chapter 5: Preparing Data 103 GDP Data 103 Power Query Overview 105 Data Arrangement Steps 106 Rename Query 106 Remove Rows 106 Remove Columns 107 Applied Steps So Far 107 Promote Headers 108 Renaming Columns 108 Empty Values 109 Handling Aggregated Data 109 Filtering Rows 110 Unpivoting Data 110 Change Column Data Type 112 Changing Values in Columns 112 Duplicating Columns 113 Multiply by a Million 113 Removing Implicit Measures 114 Column Formatting 115 Calculated Columns 115 Add Previous Year Column 116 Hiding Columns 117 LOOKUPVALUE Function 118 DIVIDE Function 119 Visualize Our GDP Data 120 Where Are We? 121 Cleaning Messy Data 122 Handle Many-to-Many Relationships 123 Duplicate a Query 125 Split Column 125 Format Column 127 Remove Duplicates 127 Index Column 128 Merge Data 128 Applying Changes 129 Finding the Power Query Button 131 Simple Cleanup Tasks 131 Replacing Values 132 Custom Columns 133 More Cleanup 135 Cleaning Date Columns 136 Conclusion 138 Chapter 6: Modeling Data 139 Dimensional Modeling 140 Why Does This Matter? 143 Makes Sense to You 143 Cross-Business Process Analysis 144 Flexible Grouping 144 Performance for Power BI 144 Facts 145 Types of Facts 146 Granularity 146 Relationships Between Facts (Never!) 147 Dimensions 148 Using Integer Keys for Relationships 149 Hierarchies in Dimensions 149 Date Dimensions 150 Power Query and Data Modeling 151 How to Build a Date Dimension 152 Import a Date Table from SQL Server Database 153 Create a Date Dimension with Power Query 155 Create a Date Dimension with DAX 158 Sort by Column 161 Mark It as a Date Table 161 How to Arrange into a Star- Schema 161 Create a State Dimension 162 Create a GDP Category Dimension 163 Add a New StateID Foreign Key to Our Fact Table 163 Unpivot the GDP Year Values 164 Putting It All into Action 166 Conclusion 167 Part 3: Going Deeper Into Power BI Chapter 7: Relationships and Filtering 171 Filtering Through Relationships 171 Visuals Interact Independently 174 The Direction of Relationships 176 Bidirectional Filtering 179 Report Filter Types 181 Visual Filters 181 Date Filtering 182 Numeric Filtering 182 Text Filtering 183 Page and Report Filters 185 Controlling the Use of Filters 185 Conclusion 186 Chapter 8: Enhancing Your Report 187 Configuring Drill- Down 187 Configuring Drill- Through 190 Buttons 193 Bookmarks 195 Personal Bookmarks vs. Report Bookmarks 199 Selections and Visibility 201 Conclusion 205 Chapter 9: Refreshing, Sharing, and Collaborating 207 Refreshing Data 207 Configuring Data Source Credentials 210 Power BI Data Gateway 213 Scheduling Refresh 214 Dataset Modes 216 Sharing and Collaborating 216 Workspaces 216 Creating Workspaces 217 Publishing 217 Security and Permission Levels 219 Power BI Apps 223 Sharing 230 Summary 232 Chapter 10: Introducing Dax 233 Setup 233 Calculated Columns 235 How to Create a Calculated Column 236 DAX Measures 240 COUNTROWS and DISTINCTCOUNT 244 Filter Context 246 CALCULATE Function 249 Conclusion 250 Chapter 11: Conclusion 251 Where Does My Organization Begin? 251 Delivery Strategy 251 Business-Led Self-Service 252 IT-Managed Self-Service 253 Corporate BI 253 Which Delivery Strategy Is Right for My Organization? 253 Where Do I Go from Here? 254 Top Gurus 254 Advice 254 Index 257

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Book SynopsisBen Eubanks is an HR industry analyst and influencer. He is the Principal Analyst at Lighthouse Research & Advisory where he oversees the development of research, assets and insights to support HR, learning and talent executives. Based in Huntsville, Alabama, he is also the founder of HR community upstartHR, the co-founder of the HRevolution movement and host of the We're Human podcast.Trade Review"I found Ben's book to be a very useful distillation of a complex and increasingly important area of HR capability and investment. My students, future HR leaders, gained tremendous insight into AI for HR and the facilitating role it will soon play in the best organizations for people. Artificial Intelligence for HR demystifies a complex topic and contextualizes what some regard as just the latest HR fad. It is a well-organized and well-written book worth reading by HR leaders, educators and students. Despite the title, it's really all about people!" * John Haggerty, Ph.D. Senior Lecturer, HR Studies at Cornell University *"The HR landscape has changed dramatically over the last three years. As the "War for Talent" continues to escalate, the value of the business minded, technologically aware HR professional has increased. What's needed are HR professionals who view AI and other technology as an augmentation to HR, giving them the ability to be business consultants versus the more common, reactive HR. Ben's book explores how HR professionals are using new technology to transform their businesses and the industry as a whole. If you are in search of a data-packed, transformational book, I'd encourage you to take the time to dive deep, take notes, and transform your own business with what you learn." * Trent Cotton, VP Talent Acquisition and Retention at Bureau Veritas and Author of Sprint Recruiting *"Ben Eubanks' Artificial Intelligence for HR is a must-read for those involved with human resources. Ben's dedication and passion for HR promotes instrumental success for organizations and leaders alike. This book offers insights into the ever-changing HR environment and navigation through new AI technology while still promoting the evolutionary development of human resource professionals." * Jamie McCall, Director, Talent Acquisition at The Henry M. Jackson Foundation for the Advancement of Military Medicine *"Ben does an excellent job helping HR leaders who have historically been trained with deep departmental expertise apply new AI technologies to their domains. More importantly, he helps them transition to strategically addressing the workforce with consumer experiences which earn their loyalty, net promotion, retention, and engagement the same as we experience in our personal lives." * Randy Womack, CEO, Socrates.ai *"Today's HR leaders must understand technology to thrive in the modern business environment and AI is one of the most transformational technologies of our age. This is often an intimidating reality for HR professionals since many signed up not realizing digital acumen was a top skill for career success. Fortunately, Ben has translated his wealth of experience and expertise into an easy-to-follow handbook. He simplifies the complexity of AI and highlights pragmatic opportunities for every HR function. It's a must-read for any HR professional looking to thrive in today's disruptive environment." * Christopher Lind, Chief Learning Officer at ChenMed and Founder, Learning Sharks *Table of Contents Chapter - 01: A snapshot of HR today; Chapter - 02: The basics of artificial intelligence; Chapter - 03: General AI Applications with HCM; Chapter - 04: Core HR and workforce management; Chapter - 05: Talent acquisition; Chapter - 06: Learning and development; Chapter - 07: Talent management; Chapter - 08: Challenges of adopting AI technology; Chapter - 09: HR skills of the future

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Book SynopsisToby Culshaw is the Talent Intelligence Leader at Worldwide Amazon Stores, leading a diverse global team of economists, consultants, business analysts and researchers in talent intelligence. Previously, he was Global Head of Talent Intelligence and Executive Recruitment Research at Royal Philips, the Dutch health technology group. He was named by Recruiter Magazine as one of the 11 Most Influential In-house Recruiters in 2017 and has consistently ranked every year from 2019 until 2023 and is an international speaker on sourcing, executive research and talent intelligence. Based in Brighton, UK he is also the founder of the Talent Intelligence Collective, a Talent Intelligence Mentor at Udder and a co-host of the Talent Intelligence Collective Podcast.Trade Review"Toby Culshaw wrote an insightful book to help you execute your talent strategy. What I like most about Talent Intelligence is how actionable it is. Toby shares years of his learnings and experience, and he explains in detail how you can apply it yourself through practical steps." * Anita Lettink, Keynote speaker and adviser on the future of work, partner at Strategic Management Centre and founder of HRTechRadar *"This is the first comprehensive discussion on Talent Intelligence I have seen. This is a topic much discussed, but little understood. Toby has finally given us a clear definition and a practical way to implement this powerful process." * Kevin Wheeler, Founder, The Future of Talent Institute *"Wow, from the maestro of TI, Toby himself. I was honoured when asked to read the book and comment and it is jam-packed with practical advice and real examples of talent intelligence in all its forms. A must read for business leaders and HR leaders alike who want to drive smarter business decisions. To quote from the book "the shifting mindset of operational to strategic is critical". Loved all of it - I will be buying the book for every member of our team for sure." * Alison Ettridge, Founder, Stratigens *"It's all about the data and the insights we can draw from it. I've felt this for a long time and this book and the work Toby has done confirms to me that this is a game changer! In an ever changing and highly competitive world the notion and discipline of talent intelligence is, for me, an essential part of an integrated talent strategy not only to compete but to win." * Denise Haylor, Former CHRO Royal Philips, Flextronics, Managing Director & Partner Boston Consulting Group *"Toby is a recognized & trusted expert in talent intelligence. Over the years he's proven to be one of the key leaders in this developing field. It's exciting to see how TI is developing and becoming more recognized as a valuable source of meaningful and actionable insights business leaders can leverage. In this text he brings together these experiences and a wide range of sources, it's a thorough essay on TI space and key reading for anyone interested in developing this knowledge." * Giles Harden, SVP People at INFARM *"Toby Culshaw and his insight on the function of Talent Intelligence as described in this text takes on and excels at creating a lexicon and foundational set of practices in the young and ever-growing space of Talent Intelligence. Creating a process is plenty hard, as is scoping a business case for change - both of which are in this text - yet defining a language for others to use in years to come is even harder. I am looking forward to applying many of these principles and labels to the products and services I use for the public and private sector companies we serve. Other leaders in recruiting, workforce planning, and analytics should review this lexicon and render into their own work so we can advance this ecosystem together as colleagues." * Andrew Gadonmski, Managing Director, Aspen Analytics *"The most inclusive and comprehensive work on Talent Intelligence I've seen to date. Toby's book captures the art and science of this continually evolving craft and emerging technology platforms complete with concrete and impactful examples. A must read for all leaders who see their competitive advantage coming from deeply understanding and acting on distilled insights from the internal and external talent landscape." * Cortney Erin, Vice President, Global Talent Acquisition Microsoft *"Timely and comprehensive examination of an often under-explored but critical area of talent strategy. Toby manages to come up with with something for everyone - from early to late adopters - as well as write a bit of a love letter to the subject." * Teresa Wykes, Global Head of Talent Intelligence, SAP *Table of Contents Chapter - 00: Introduction; Chapter - 01: Context; Chapter - 02: Types of Intelligence; Chapter - 03: The great debate; Chapter - 04: Building the case for Talent Intelligence; Chapter - 05: What type of work can TI functions support?; Chapter - 06: Metrics for Success and KPIs; Chapter - 07: Where to sit TI function within organizations; Chapter - 08: Talent Intelligence Maturity Model; Chapter - 09: Tooling and Resources; Chapter - 10: Potential structures of Talent Intelligence teams; Chapter - 11: Roles and skills needed in teams; Chapter - 12: Career pathing; Chapter - 13: In House and partner landscape; Chapter - 14: Examples of use of talent intelligence; Chapter - 15: What does good look like?; Chapter - 16: What is the future of Talent Intelligence?; Chapter - 17: Tales from the trenches; Chapter - 18: Well that’s a wrap

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Book SynopsisToby Culshaw is the Talent Intelligence Leader at Worldwide Amazon Stores, leading a diverse global team of economists, consultants, business analysts and researchers in talent intelligence. Previously, he was Global Head of Talent Intelligence and Executive Recruitment Research at Royal Philips, the Dutch health technology group. He was named by Recruiter Magazine as one of the 11 Most Influential In-house Recruiters in 2017 and has consistently ranked every year from 2019 until 2023 and is an international speaker on sourcing, executive research and talent intelligence. Based in Brighton, UK he is also the founder of the Talent Intelligence Collective, a Talent Intelligence Mentor at Udder and a co-host of the Talent Intelligence Collective Podcast.Trade Review"Toby Culshaw wrote an insightful book to help you execute your talent strategy. What I like most about Talent Intelligence is how actionable it is. Toby shares years of his learnings and experience, and he explains in detail how you can apply it yourself through practical steps." * Anita Lettink, Keynote speaker and adviser on the future of work, partner at Strategic Management Centre and founder of HRTechRadar *"This is the first comprehensive discussion on Talent Intelligence I have seen. This is a topic much discussed, but little understood. Toby has finally given us a clear definition and a practical way to implement this powerful process." * Kevin Wheeler, Founder, The Future of Talent Institute *"Wow, from the maestro of TI, Toby himself. I was honoured when asked to read the book and comment and it is jam-packed with practical advice and real examples of talent intelligence in all its forms. A must read for business leaders and HR leaders alike who want to drive smarter business decisions. To quote from the book "the shifting mindset of operational to strategic is critical". Loved all of it - I will be buying the book for every member of our team for sure." * Alison Ettridge, Founder, Stratigens *"It's all about the data and the insights we can draw from it. I've felt this for a long time and this book and the work Toby has done confirms to me that this is a game changer! In an ever changing and highly competitive world the notion and discipline of talent intelligence is, for me, an essential part of an integrated talent strategy not only to compete but to win." * Denise Haylor, Former CHRO Royal Philips, Flextronics, Managing Director & Partner Boston Consulting Group *"Toby is a recognized & trusted expert in talent intelligence. Over the years he's proven to be one of the key leaders in this developing field. It's exciting to see how TI is developing and becoming more recognized as a valuable source of meaningful and actionable insights business leaders can leverage. In this text he brings together these experiences and a wide range of sources, it's a thorough essay on TI space and key reading for anyone interested in developing this knowledge." * Giles Harden, SVP People at INFARM *"Toby Culshaw and his insight on the function of Talent Intelligence as described in this text takes on and excels at creating a lexicon and foundational set of practices in the young and ever-growing space of Talent Intelligence. Creating a process is plenty hard, as is scoping a business case for change - both of which are in this text - yet defining a language for others to use in years to come is even harder. I am looking forward to applying many of these principles and labels to the products and services I use for the public and private sector companies we serve. Other leaders in recruiting, workforce planning, and analytics should review this lexicon and render into their own work so we can advance this ecosystem together as colleagues." * Andrew Gadonmski, Managing Director, Aspen Analytics *"The most inclusive and comprehensive work on Talent Intelligence I've seen to date. Toby's book captures the art and science of this continually evolving craft and emerging technology platforms complete with concrete and impactful examples. A must read for all leaders who see their competitive advantage coming from deeply understanding and acting on distilled insights from the internal and external talent landscape." * Cortney Erin, Vice President, Global Talent Acquisition Microsoft *"Timely and comprehensive examination of an often under-explored but critical area of talent strategy. Toby manages to come up with with something for everyone - from early to late adopters - as well as write a bit of a love letter to the subject." * Teresa Wykes, Global Head of Talent Intelligence, SAP *Table of Contents Chapter - 00: Introduction; Chapter - 01: Context; Chapter - 02: Types of Intelligence; Chapter - 03: The great debate; Chapter - 04: Building the case for Talent Intelligence; Chapter - 05: What type of work can TI functions support?; Chapter - 06: Metrics for Success and KPIs; Chapter - 07: Where to sit TI function within organizations; Chapter - 08: Talent Intelligence Maturity Model; Chapter - 09: Tooling and Resources; Chapter - 10: Potential structures of Talent Intelligence teams; Chapter - 11: Roles and skills needed in teams; Chapter - 12: Career pathing; Chapter - 13: In House and partner landscape; Chapter - 14: Examples of use of talent intelligence; Chapter - 15: What does good look like?; Chapter - 16: What is the future of Talent Intelligence?; Chapter - 17: Tales from the trenches; Chapter - 18: Well that’s a wrap

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Book SynopsisEdosa Odaro is an AI and data transformation leader who has helped countless international organizations deliver significant impact through data analytics, transformation strategy and intelligent interventions. He is Chief Data and Analytics Officer at Tawuniya and is on the board for the UK's National Institute for Health Data Science (HDR UK). Odaro has been named a Financial Times Top 100 Most Influential Leader and one of the UK's 30 Most Influential Black Leaders in FinTech.Trade Review"A masterclass in how to unlock the true value of data for your organization. Value-Driven Data is a must read for all data leaders." * Hartnell Ndungi, Chief Data Officer, Absa Group *"Value-Driven Data is a timely and practical guide to support us all with the challenge of unlocking and measuring the value of data. This thought provoking book is filled with practical examples to support frameworks and theories. A must read for all executives." * Dr Johanna Hutchinson, Chief Data Officer, BAE Systems and Board Member, The Royal Statistics Society *"A powerful reminder that data is not just a valuable asset, but a critical driver of business success and unlocking new value pools sitting at the intersection of technology and sustainable business." * Lamé Verre, Head of Strategy, Innovation & Sustainability, SSE Energy Customer Solutions and Global Future Council Member, World Economic Forum *"Value-Driven Data is an excellent book and a valuable resource for anyone looking to cut through the noise. It provides clarity on how to quantify the financial impact of data initiatives and effectively communicates with senior and non-technical audiences using clear and concise language." * Amy Shi-Nash, Chief Analytics & Data Officer, Tabcorp and Data Board Member, MIT Sloan School of Management *"Edosa has masterfully stitched together a collection of great examples with a set of tangible principles to guide readers on how to enhance their potential with data. The insights that this book provides are unique, the advice practical and the success stories applicable across industry sectors." * Mona Soni, Chief Technology Officer, formerly at S&P Global and Dow Jones *"Value-Driven Data offers a combination of deep knowledge and practical value for leaders guiding organizations through the responsible use of data. Odaro brings together a variety of perspectives from data practitioners and consultants to executive leadership in global businesses. I hope his shared knowledge will reach data professionals around the world and contribute to their success." * Simone Steel, Chief Data and Analytics Officer & CIO for Enterprise Data Platforms, Nationwide Building Society *"Value Driven Data cuts through the rumours and hearsay with real-life, no-nonsense examples of creating a data vision and value in practice. This is a comprehensive guide for both data professionals and business leaders. Once you have read it you won't want to do research without it." * Graeme McDermott, Chief Data Officer, Tempcover *"Provides insightful frameworks and considerations for every organization that wants to get more value out of data and analytics." * Gero Martin Gunkel, Data Science Leader & Chief Operating Officer (ZCAM), Zurich Insurance *"Value-Driven Data provides a comprehensive framework for developing a data vision that aligns with the overall strategy of an organisation. One of the most impressive aspects of the book is how it breaks down complex concepts into easy-to-understand language, making it an enjoyable read for anyone interested in data strategy, regardless of their level of expertise." * Rowland Agidee, Head of Data Management, UK Intellectual Property Office *"Edosa brings his experience and expertise together to remind us all of how expressing data value is fundamental to data driven transformation." * JC Lionti, Managing Director & Chief Data Officer, formerly at BNP Paribas Americas *"Edosa has done terrific work in producing this masterpiece! I like the way he has used data visions as the starting point and has linked all chapters to it by creating a practical and actionable book to help organizations realize their full potential." * Ram Kumar, Chief Data & Analytics Officer, Cigna *"Finally, a book that makes delivering value through data the number one priority. Business Leaders, whilst interested, do not really care how we as data professionals do it. Influencing top line, cost avoidance and bottom line are central to 99.9% of business strategies and so should also be the main focus when creating data strategies. Using real-world and highly relatable examples, Edosa has delivered an essential read for both data and business professionals." * Sam Richmond, Group Head of Data, The Go-Ahead Group *"Value-Driven Data is an incredible resource, full of frameworks and tools to help navigate the elusive topic of data value in an easy to digest format, with stories drawn from Edosa's long professional career. A valuable instrument in an era of cost optimisation, providing knowledge to the reader to aid in directing and articulating vision, value and creating pathways to overcome obstacles." * Stylianos Taxidis, Head of Data Science & AI, Costain Group *Table of Contents Chapter - 00: Introduction Section - ONE: Vision: Discovering and capturing data value opportunities Chapter - 01: What is data vision? Chapter - 02: Capturing data visions Chapter - 03: Why data visions of all size matter Chapter - 04: The destructive impact of data vision misalignment Chapter - 05: Simplifying data vision misalignments Section - TWO: Obstacle: The things that stand between data visions and data value realisation Chapter - 06: Obstacles of the past Chapter - 07: Obstacles of the future Chapter - 08: Obstacles of the present Section - THREE: Value: Identifying, capturing and communicating data value Chapter - 09: Capturing data value propositions Chapter - 10: Measuring data value for business case and operational assurance Chapter - 11: The data value measurement lifecycle Chapter - 12: A data value account for data profits and losses Chapter - 13: Presenting data value to the CXO, EXCO and the board Chapter - 14: Conclusion: Bringing it all together

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