Data capture and analysis Books

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 SynopsisDESCRIPTION In a world where big data is the norm and near-real-time decisions are crucial, machine learning (ML) is a critical component of the data workflow. Machine learning systems can quickly crunch massive amounts of information to offer insights and make decisions in a way that matches or even surpasses human cognitive abilities. These systems use sophisticated computational and statistical tools to build models that can recognize and visualize patterns, predict outcomes, forecast values, and make recommendations. Real-World Machine Learning is a practical guide designed to teach developers the art of ML project execution. The book introduces the day-to-day practice of machine learning and prepares readers to successfully build and deploy powerful ML systems. Using the Python language and the R statistical package, it starts with core concepts like data acquisition and modeling, classification, and regression. Then it moves through the most important ML tasks, like model validation, optimization and feature engineering. It uses real-world examples that help readers anticipate and overcome common pitfalls. Along the way, they will discover scalable and online algorithms for large and streaming data sets. Advanced readers will appreciate the in-depth discussion of enhanced ML systems through advanced data exploration and pre-processing methods. KEY FEATURES Accessible and practical introduction to machine learning Contains big-picture ideas and real-world examples Prepares reader to build and deploy powerful predictive systems Offers tips & tricks and highlights common pitfalls AUDIENCE Code examples are in Python and R. No prior machine learning experience required. ABOUT THE TECHNOLOGY Machine learning has gained prominence due to the overwhelming successes of Google, Microsoft, Amazon, LinkedIn, Facebook, and others in their use of ML. The Gartner report predicts that big data analytics will be a $25 billion market by 2017, and financial firms, marketing organizations, scientific facilities, and Silicon Valley startups are all demanding machine learning skills from their developers.

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Book SynopsisThe connected world offers the potential for radical new business insights gleaned from previously unimaginable volumes of data. But business has got bogged down in the process of collecting and storing that data; money has been wasted on data lakes in which many IT departments have drowned without being able to deliver useful insights to business leaders. Big data has new and exciting answers to offer, but business leaders must first decide what questions it would like to see answered. Data may be the new oil, but to date we have only built oil depots. This book analyses the new, Fourth Wave of business transformation, which will build the refineries that turn data into useful products. Business has started from 'data up' and needs to start again from 'value down', going back to the drivers of real business value and deciding what insights would help realize that value. Only then can we begin to interrogate data with purpose.

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Book SynopsisInformationsgesellschaft, Information als Wettbewerbsfaktor, Informationsflut: Diese Stichworte verdeutlichen die unternehmerische und gesellschaftliche Bedeutung von Informationen. Doch nicht nur Information allein, sondern auch die Systeme, die Informationen verarbeiten, speichern und übertragen sowie die Technologien, auf denen sie beruhen, verdienen Aufmerksamkeit. Informationsmanagement hat die Aufgabe, den im Hinblick auf das Unternehmensziel bestmöglichen Einsatz der Ressource Information zu gewährleisten. Es zählt zu den wesentlichen Bestandteilen heutiger Unternehmensführung. Das Lehrbuch vermittelt in 13 Einheiten die Grundlagen des Informationsmanagements. Dabei werden neben den Managementaufgaben der Informationswirtschaft, der Systeme und der Technologien auch ausgewählte Führungsaufgaben des Informationsmanagementsbehandelt. Jede Lehreinheit beginnt mit einem Überblick über die behandelten Themen und schließt mit einer Zusammenfassung sowie Aufgaben zur Wiederholung ab. So richtet sich dieses Buch insbesondere an Bachelorstudenten in den Fächern Wirtschaftsinformatik, BWL und Informatik. Table of ContentsVorwörter.- Einleitung.- 1. Grundlagen des Informationsmanagement.- 2. Management der Informationswirtschaft.- 3. Management der Informationssysteme.- 4. Management der Informations- und Kommunikationstechnik.- 5. Ausgewählte Führungsaufgaben des Informationsmanagements.- Verzeichnisse.

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Book SynopsisThe increasing availability of data in our current, information overloaded society has led to the need for valid tools for its modelling and analysis. Data mining and applied statistical methods are the appropriate tools to extract knowledge from such data. This book provides an accessible introduction to data mining methods in a consistent and application oriented statistical framework, using case studies drawn from real industry projects and highlighting the use of data mining methods in a variety of business applications. Introduces data mining methods and applications. Covers classical and Bayesian multivariate statistical methodology as well as machine learning and computational data mining methods. Includes many recent developments such as association and sequence rules, graphical Markov models, lifetime value modelling, credit risk, operational risk and web mining. Features detailed case studies based on applied projects within industry. Trade Review“If I had to recommend a good introduction to data mining, I would choose this one.” (Stat Papers, 2011) Table of Contents1 Introduction. Part I Methodology. 2 Organisation of the data. 2.1 Statistical units and statistical variables. 2.2 Data matrices and their transformations. 2.3 Complex data structures. 2.4 Summary. 3 Summary statistics. 3.1 Univariate exploratory analysis. 3.2 Bivariate exploratory analysis of quantitative data. 3.3 Multivariate exploratory analysis of quantitative data. 3.4 Multivariate exploratory analysis of qualitative data. 3.5 Reduction of dimensionality. 3.6 Further reading. 4 Model specification. 4.1 Measures of distance. 4.2 Cluster analysis. 4.3 Linear regression. 4.4 Logistic regression. 4.5 Tree models. 4.6 Neural networks. 4.7 Nearest-neighbour models. 4.8 Local models. 4.9 Uncertainty measures and inference. 4.10 Non-parametric modelling. 4.11 The normal linear model. 4.12 Generalised linear models. 4.13 Log-linear models. 4.14 Graphical models. 4..15 Survival analysis models. 4.16 Further reading. 5 Model evaluation. 5.1 Criteria based on statistical tests. 5.2 Criteria based on scoring functions. 5.3 Bayesian criteria. 5.4 Computational criteria. 5.5 Criteria based on loss functions. 5.6 Further reading. Part II Business caste studies. 6 Describing website visitors. 6.1 Objectives of the analysis. 6.2 Description of the data. 6.3 Exploratory analysis. 6.4 Model building. 6.5 Model comparison. 6.6 Summary report. 7 Market basket analysis. 7.1 Objectives of the analysis. 7.2 Description of the data. 7.3 Exploratory data analysis. 7.4 Model building. 7.5 Model comparison. 7.6 Summary report. 8 Describing customer satisfaction. 8.1 Objectives of the analysis. 8.2 Description of the data. 8.3 Exploratory data analysis. 8.4 Model building. 8.5 Summary. 9 Predicting credit risk of small businesses. 9.1 Objectives of the analysis. 9.2 Description of the data. 9.3 Exploratory data analysis. 9.4 Model building. 9.5 Model comparison. 9.6 Summary report. 10 Predicting e-learning student performance. 10.1 Objectives of the analysis. 10.2 Description of the data. 10.3 Exploratory data analysis. 10.4 Model specification. 10.5 Model comparison. 10.6 Summary report. 11 Predicting customer lifetime value. 11.1 Objectives of the analysis. 11.2 Description of the data. 11.3 Exploratory data analysis. 11.4 Model specification. 11.5 Model comparison. 11.6 Summary report. 12 Operational risk management. 12.1 Context and objectives of the analysis. 12.2 Exploratory data analysis. 12.3 Model building. 12.4 Model comparison. 12.5 Summary conclusions. References. Index.

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Book SynopsisThe increasing availability of data in our current, information overloaded society has led to the need for valid tools for its modelling and analysis. Data mining and applied statistical methods are the appropriate tools to extract knowledge from such data. This book provides an accessible introduction to data mining methods in a consistent and application oriented statistical framework, using case studies drawn from real industry projects and highlighting the use of data mining methods in a variety of business applications. Introduces data mining methods and applications. Covers classical and Bayesian multivariate statistical methodology as well as machine learning and computational data mining methods. Includes many recent developments such as association and sequence rules, graphical Markov models, lifetime value modelling, credit risk, operational risk and web mining. Features detailed case studies based on applied projects within industry. Trade Review“If I had to recommend a good introduction to data mining, I would choose this one.” (Stat Papers, 2011) Table of Contents1 Introduction. Part I Methodology. 2 Organisation of the data. 2.1 Statistical units and statistical variables. 2.2 Data matrices and their transformations. 2.3 Complex data structures. 2.4 Summary. 3 Summary statistics. 3.1 Univariate exploratory analysis. 3.1.1 Measures of location. 3.1.2 Measures of variability. 3.1.3 Measures of heterogeneity. 3.1.4 Measures of concentration. 3.1.5 Measures of asymmetry. 3.1.6 Measures of kurtosis. 3.2 Bivariate exploratory analysis of quantitative data. 3.3 Multivariate exploratory analysis of quantitative data. 3.4 Multivariate exploratory analysis of qualitative data. 3.4.1 Independence and association. 3.4.2 Distance measures. 3.4.3 Dependency measures. 3.4.4 Model-based measures. 3.5 Reduction of dimensionality. 3.5.1 Interpretation of the principal components. 3.6 Further reading. 4 Model specification. 4.1 Measures of distance. 4.1.1 Euclidean distance. 4.1.2 Similarity measures. 4.1.3 Multidimensional scaling. 4.2 Cluster analysis. 4.2.1 Hierarchical methods. 4.2.2 Evaluation of hierarchical methods. 4.2.3 Non-hierarchical methods. 4.3 Linear regression. 4.3.1 Bivariate linear regression. 4.3.2 Properties of the residuals. 4.3.3 Goodness of fit. 4.3.4 Multiple linear regression. 4.4 Logistic regression. 4.4.1 Interpretation of logistic regression. 4.4.2 Discriminant analysis. 4.5 Tree models. 4.5.1 Division criteria. 4.5.2 Pruning. 4.6 Neural networks. 4.6.1 Architecture of a neural network. 4.6.2 The multilayer perceptron. 4.6.3 Kohonen networks. 4.7 Nearest-neighbour models. 4.8 Local models. 4.8.1 Association rules. 4.8.2 Retrieval by content. 4.9 Uncertainty measures and inference. 4.9.1 Probability. 4.9.2 Statistical models. 4.9.3 Statistical inference. 4.10 Non-parametric modelling. 4.11 The normal linear model. 4.11.1 Main inferential results. 4.12 Generalised linear models. 4.12.1 The exponential family. 4.12.2 Definition of generalised linear models. 4.12.3 The logistic regression model. 4.13 Log-linear models. 4.13.1 Construction of a log-linear model. 4.13.2 Interpretation of a log-linear model. 4.13.3 Graphical log-linear models. 4.13.4 Log-linear model comparison. 4.14 Graphical models. 4.14.1 Symmetric graphical models. 4.14.2 Recursive graphical models. 4.14.3 Graphical models and neural networks. 4.15 Survival analysis models. 4.16 Further reading. 5 Model evaluation. 5.1 Criteria based on statistical tests. 5.1.1 Distance between statistical models. 5.1.2 Discrepancy of a statistical model. 5.1.3 Kullback–Leibler discrepancy. 5.2 Criteria based on scoring functions. 5.3 Bayesian criteria. 5.4 Computational criteria. 5.5 Criteria based on loss functions. 5.6 Further reading. Part II Business case studies. 6 Describing website visitors. 6.1 Objectives of the analysis. 6.2 Description of the data. 6.3 Exploratory analysis. 6.4 Model building. 6.4.1 Cluster analysis. 6.4.2 Kohonen networks. 6.5 Model comparison. 6.6 Summary report. 7 Market basket analysis. 7.1 Objectives of the analysis. 7.2 Description of the data. 7.3 Exploratory data analysis. 7.4 Model building. 7.4.1 Log-linear models. 7.4.2 Association rules. 7.5 Model comparison. 7.6 Summary report. 8 Describing customer satisfaction. 8.1 Objectives of the analysis. 8.2 Description of the data. 8.3 Exploratory data analysis. 8.4 Model building. 8.5 Summary. 9 Predicting credit risk of small businesses. 9.1 Objectives of the analysis. 9.2 Description of the data. 9.3 Exploratory data analysis. 9.4 Model building. 9.5 Model comparison. 9.6 Summary report. 10 Predicting e-learning student performance. 10.1 Objectives of the analysis. 10.2 Description of the data. 10.3 Exploratory data analysis. 10.4 Model specification. 10.5 Model comparison. 10.6 Summary report. 11 Predicting customer lifetime value. 11.1 Objectives of the analysis. 11.2 Description of the data. 11.3 Exploratory data analysis. 11.4 Model specification. 11.5 Model comparison. 11.6 Summary report. 12 Operational risk management. 12.1 Context and objectives of the analysis. 12.2 Exploratory data analysis. 12.3 Model building. 12.4 Model comparison. 12.5 Summary conclusions. References. Index.

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Book SynopsisRequiring no specialized knowledge beyond a good grasp of matrix algebra and some acquaintance with straight line regression and simple analysis of variance models, this work covers the diagnostics and methods of model fitting.Trade Review"With excellent motivating and presenting style, this book is suitable for a beginning graduate level regression course." (Journal of Statistical Computation and Simulation, July 2005) "...revises and expands the standard text, providing extensive coverage of state-of-the-art theory..." (Zentralblatt Math, Vol. 1029, 2004) "...largely rewritten...very useful for self-study...an excellent choice for a course in linear models and researchers who are interested in recent literature in the fields..." (Technometrics, Vol. 45, No. 4, November 2003) “...rewritten to reflect current thinking, such as the major advances in computing during the past 25 years.” (Quarterly of Applied Mathematics, Vol. LXI, No. 3, September 2003)Table of ContentsPreface. Vectors of Random Variables. Multivariate Normal Distribution. Linear Regression: Estimation and Distribution Theory. Hypothesis Testing. Confidence Intervals and Regions. Straight-Line Regression. Polynomial Regression. Analysis of Variance. Departures from Underlying Assumptions. Departures from Assumptions: Diagnosis and Remedies. Computational Algorithms for Fitting a Regression. Prediction and Model Selection. Appendix A. Some Matrix Algebra. Appendix B. Orthogonal Projections. Appendix C. Tables. Outline Solutions to Selected Exercises. References. Index.

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Book SynopsisProvides a powerful and versatile methodology that enables researchers to design their investigations and analyse data effectively and safely, without the need for formal statistical training.Trade Review"This is an important book; the authors have done a quality job..." (N.I.R. News, Vol 12/1, 2001) "This book is recommended to students of chemical, biochemical and food engineering, scientists and industrial practitioners". (Chemical Biochemical Engineering, June 2001) "a possible source of inspiration" (Measurement Science Technology, October 2001) "a powerful and versatile methodology" (Chemie Plus, June 2001) "...should prove a very useful text for this target readership." (Short Book Reviews, Vol. 22, No. 1, April 2002) "...Through the book, there is a solid philosophy and opinions supported by the intelligence and experience of the couple [authors]..." (Applied Spectroscopy, Vol.56, No.8, 2002) "...The book is written by two experts in the field with nearly 30 years of experience, and this is reflected in every aspect of the book..." (Journal of Chemometrics, No.16, 2002)Table of ContentsPreface. Acknowledgements. OVERVIEW. Why Multivariate Data Analysis? Qualimetrics for Determining Quality. A Layman's Guide to Multivariate Data Analysis. METHODOLOGY. Some Estimation Concepts. Analysis of One Data Table X: Principle Component Analysis. Analysis of Two Data Tables X and Y: Partial Least Squares Regression (PLSR). Example of Multivariate Calibration Project. Interpretation of Many Types of Data X and Y: Exploring Relationships in Interdisciplinary Data Sets. Classification and Discrimination X_1, X_2, X_3: Handling Heterogeneous Sample Sets. Validation X and Y. Experimental Planning Y and X. APPLICATIONS. Multivariate Calibration: Quality Determination of Wheat From High-speed NIR Spectra. Analysis of Questionnaire Data: What Determines Quality of the Working Environment? Analysis of a Heterogeneous Sample Set: Predicting Toxicity From Quantum Chemistry. Multivariate Statistical Process Control: Quality Monitoring of a Sugar Production Process. Design and Analysis of Controlled Experiments: Reducing Loss of Quality in Stored Food. Appendix A1: How the Present Book Relates to Some Mathematical Modelling Traditions in Science. Appendix A2: Sensory Science. Appendix A3.1: Bi-linear Modelling Has Many Applications. Appendix A3.2: Common Problems and Pitfalls in Soft Modelling. Appendix A4: Mathematical Details. Appendix A5: PCA Details. Appendix A6: PLS Regression Details. Appendix A7: Modelling the Unknown. Appendix A8: Non-linearity and Weighting. Appendix A9: Classification and Outlier Detection. Appendix A10: Cross-validation Details. Appendix A11: Power Estimation Details. Appendix A12: What Makes NIR Data So Information-rich? Appendix A13: Consequences of the Working Environment Survey. Appendix A14: Details of the Molecule Class Models. Appendix A15: Forecasting the Future. Appendix A16: Significance Testing with Cross-validation vs. ANOVA. References. Index.

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Book SynopsisFuzzy clustering, which combines fuzzy logic and cluster analysis techniques, has experienced a spur of interest in recent years owing to its important applications in image recognition. This revised, updated, and expanded translation of the German book deals with the ideas and algorithms of fuzzy clustering and their applications.Table of ContentsIntroduction. Basic Concepts. Classical Fuzzy Clustering Algorithms. Linear and Ellipsoidal Prototypes Shell Prototypes. Polygonal Object Boundaries. Cluster Estimation Models. Cluster Validity. Rule Generation with Clustering. Appendix. Bibliography.

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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 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 Synopsis

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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 SynopsisTable of ContentsPreface xi Acknowledgments xv Part I The Foundation of Analytics 1 Chapter 1 Analytics Overview 3 Chapter 2 The People of Analytics 38 Chapter 3 Organizational Context for Analytics 68 Chapter 4 Data Strategy, Platforms, and Architecture 95 Part II Analytics Lifecycle Best Practices 127 Chapter 5 The Analytics Lifecycle Toolkit 129 Chapter 6 Problem Framing 148 Chapter 7 Data Sensemaking 185 Chapter 8 Analytics Model Development 218 Chapter 9 Results Activation 266 Chapter 10 Analytics Product Management 301 Part III Sustaining Analytics Success 349 Chapter 11 Actioning Analytics 351 Chapter 12 Core Competencies for Analytics Teams 386 Chapter 13 The Future of Analytics 424 About the Author 433 About the Companion Web Site 435 Index 437

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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 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 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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Book SynopsisKatherine O'Keefe works with Ireland's national water utility, Uisce Éireann, and is on the teaching faculty of the Law Society of Ireland's Diploma Centre. She has developed international professional accreditation schemes for information management, for which she was awarded the DAMA International Professional Achievement Award in 2017. Daragh O Brien is the founder and managing director of Castlebridge, a leading data strategyconsultancy. He lectures in Data Protection and Data Governance in the Sutherland School of Law at University College Dublin. Daragh is a founding member of the Strategic Advisory Board to the School of Business in Maynooth University.Trade Review"Ethics play an increasingly important role when considering how to collect and use personal data. This updated edition of Data Ethics clearly explains how to take ethics seriously and make it an integral part of business information management and governance. The combination of sound and up to date legal theories with practical tips and case studies makes it a useful handbook for anyone working with data on a regular basis." * Paul Breitbarth, Senior Visiting Fellow, European Centre on Privacy and Cybersecurity, Maastricht University *"In a world where AI is creating a growing wave of often dubious information, O Brien and O'Keefe's book should be mandatory reading for everybody in IT, media, regulatory bodies and beyond. This new edition of Data Ethics focuses on emerging topics of vital importance in a world where ethical decisions by IT may literally be, in the extreme, matters of life and death." * Dr Barry Devlin, Founder, 9 Sight Consulting and author of Business unIntelligence *"I can't think of a subject more relevant than data ethics. Given that we live in a data-dependent world, the most important question is not "Can I do something with data?" but "Should I do something with data?". These questions should be considered by teens learning to code, businesspeople gathering and exploiting customer data, scientists developing and releasing Artificial Intelligence (AI) applications, and anyone creating and using data. Daragh and Katherine provide an excellent groundwork for addressing these questions and give us the tools to think and act with our data in a responsible way. Read their book, share it and apply it!" * Danette McGilvray, President and Principal, Granite Falls Consulting, Inc. and author of Executing Data Quality Projects *"Reading Data Ethics gave me goosebumps. Impeccably researched, it is the definitive work on the topic. Simultaneously confronting and enlightening, it challenged my own ethical framework and validated the principles I hold dear in my practice as a Data Governance Executive. The foreword by John Ladley is delightful and sets the scene perfectly for what is to follow. I look forward to our DAMA community here in Australia, and internationally, having the opportunity to share their experiences after reading this outstanding book on data ethics." * Andrew Andrews, Data Governance Manager, ANZ Banking Group and Vice President of Marketing, DAMA International *Table of Contents Chapter - 00: Introduction - Why write a book on data ethics?; Chapter - 01: Ethics in the context of data management; Chapter - 02: Introduction to ethical concepts and frameworks; Chapter - 03: Ethical principles, standards and practice; Chapter - 04: Ethics, privacy and analytics; Chapter - 05: Ethics and data management (including AI); Chapter - 06: Developing an ethical architecture for information management; Chapter - 07: Introducing the Ethical Enterprise Information Management (E2IM) framework; Chapter - 08: Information ethics as an information quality system; Chapter - 09: Information ethics and data governance; Chapter - 10: Information ethics and risk - Tools and methods for identifying and managing ethical risk; Chapter - 11: Data ethics - the bigger picture; Chapter - 12: And in conclusion...;

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Book SynopsisBernard Marr is one of the leading voices in Technology and Innovation. A futurist and strategic performance consultant, he has advised many of the world's best-known organizations on their business and data strategies. A frequent keynote speaker, he also writes on the topic of data and analytics for various publications including Forbes and the Huffington Post. Bernard Marr is also the author of Data Strategy (2021) and The Intelligence Revolution (2020) published by Kogan Page.Trade Review"Without a doubt human capability (talent + leadership + organization + HR) increasingly delivers value to all stakeholders. This excellent book provides business and HR leaders the information required to improve decision making. Bernard's insights on analytics and AI will be the keys for progress." * Dave Ulrich, Rensis Likert Professor, Ross School of Business, University of Michigan Partner, The RBL Group *"If anyone was going to publish a book about the impact of the latest technology developments such as AI on the field of HR and People Analytics my bets were on Bernard Marr. And you won't be disappointed. The book offers a deep dive into the world of data of every kind, every possible use case, honest overview of technology and important considerations. It has never been more critical to educate ourselves about it." * Maja Luckos, VP, Employee Success, Salesforce *"This book propelled me into a world of possibilities for HR leaders in embracing the 'intelligence revolution' to shape people strategies that add value to their organizations and their people. It's enlightened me to the power of AI-enabled HR and how I might use it, and it's made me want to learn more. This is a must read for all HR leaders." * Linda Sleath, Group HR Director, Topps Tiles Plc. *"Data-Driven HR strikes a nice balance between exploring emerging trends in people analytics while primarily serving as a practical guide to HR professionals at any stage of their data journey. The second edition seamlessly weaves AI into a narrative that's easy to engage with and is packed full of examples that bring the theories to life." * Mark Ferrie, People Analytics Director, Meta *"Data-Driven HR is a terrific overview of the enormous world of people analytics and AI. For people trying to understand this important space, this book shows you the way." * Josh Bersin, Global Industry Analyst and CEO of The Josh Bersin Company *"Data, analytics and AI provides to elevate HR from its traditional role as a support function to one of a strategic partner creating value for the enterprise, its customers and its employees. There's a well-thumbed copy of the first edition of Data Driven HR on my bookshelf, and in this timely update Marr, one of the most knowledgeable people on the topic, explains how data and AI can enable HR to drive better decision making about people, deliver an enhanced service to employees; and make HR processes more efficient." * David Green, Managing Partner at Insight222, co-author of Excellence in People Analytics, and host of the Digital HR Leaders podcast. *"Bernard Marr has once again delivered an indispensable guide to harnessing the power of data, analytics and AI in HR. This updated edition thoroughly captures the latest innovations shaping human resources while still being accessible for HR professionals at any level. Through compelling examples and clear frameworks, Marr demonstrates how to drive business value through evidence-based talent practices. This is a must-read playbook for any HR leader looking to build capabilities in data-driven decision-making." * Professor Max Blumberg, PhD, University of Leeds *"This is a great guide for HR professionals who are grappling with the transition to becoming data led. It's easy to read, and with real examples and case studies across the employee lifecycle, it's also a pragmatic resource to have in your HR toolkit." * Ashish Sinha Korn Ferry Head of People Analytics, AI & Strategy EMEA Practice Leader *"AI is transforming the world of work and our personal lives. With a people-centric approach, Bernard Marr demystifies data driven AI enabled HR with context, thought provoking insights and examples of AI at the time this book was written. We all have a role to play when it comes to this rapidly evolving space as the output of AI will be a reflection of our culture and values. Staying on top of leading practices, lessons learned, emerging regulations and standards is critical so we can unlock AI's potential and value add to the business, our customers and employees while minimizing risk. This book sets the foundation so we can do just that!" * Terilyn Juarez Monroe, Terilyn Juarez Monroe, Chief People Officer *"Data-Driven HR is an indispensable resource for Career Services professionals looking to equip their students with cutting-edge strategies in today's competitive job market. This comprehensive book offers invaluable insights into recruitment and candidate selection, employer branding, pinpointing the most effective recruitment channels, and harnessing AI-enhanced automation to identify and assess the best candidates for businesses. It's a game-changer for career advisors committed to empowering their students with the knowledge and skills needed to excel in the evolving world of talent acquisition and HR." * Dr. Amber Wigmore Álvarez, Associate Professor, IE Business School and IE University *Table of Contents Chapter - 00: Preface; Section - ONE: Data, Analytics and AI in HR; Chapter - 01: How data and AI are transforming HR; Chapter - 02: How data and AI have come to revolutionise HR; Chapter - 03: The Data, Analytics and AI tools available to HR; Section - TWO: Data-Driven and AI-enabled HR in Practice; Chapter - 04: Better HR insights and decision-making; Chapter - 05: Recruitment and candidate selection; Chapter - 06: Employee Onboarding; Chapter - 07: Performance Monitoring and Management; Chapter - 08: Employee Training and Development; Chapter - 09: Performance monitoring and management; Chapter - 10: Identify the use cases; Chapter - 11: Building skills and aligning culture; Section - THREE: Making data-driven and AI enabled HR happen; Chapter - 12: Identifying the use cases for your organization; Chapter - 13: The future of HR

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Book SynopsisKamales Lardi is a global thought leader on digital transformation and an expert on emerging technology solutions including AI, blockchain and IoT. She is CEO of Lardi & Partner Consulting and has advised many multinational companies across various industries in Europe, Asia and Africa. Kamales is Chair of the FORBES Business Council Women Executives, a Teaching Fellow at Durham University Business School and has been listed in the "Top 10 Global Influencers & Thought Leaders in Digital Transformation" (Thinkers360). She is based near Schwyz, Switzerland.

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Book SynopsisThis book analyses societal trends and controversies related to developments in data ownership, access, construction, dissemination and interpretation, looking at the ways that society interacts with and uses statistical data.Table of ContentsBook Introduction ~ Humphrey Southall, Jeff Evans and Sally Ruane; 1: How Data are Changing; Introduction ~ Humphrey Southall and Jeff Evans; Statistical work: the changing occupational landscape ~ Kevin McConway; The creation and use of big administrative data ~ Harvey Goldstein and Ruth Gilbert Data Analytics ~ Ifan Shepherd and Gary Hearne; Social Media Data ~ Adrian Tear and Humphrey Southall; 2: Counting in a Globalised world; Introduction ~ Sally Ruane and Jeff Evans; Adult Skills Surveys and Transnational Organisations: Globalising Educational Policy ~ Jeff Evans; Poverty and health care surveys in the Global South: Towards making valid estimates ~ Roy Carr-Hill; Counting the Population in Need of International Protection Globally ~ Brad Blitz, Alessio D’Angelo and Eleonore Kofman; Tax justice and the challenges of measuring illicit financial flows ~ Richard Murphy; 3: The Changing Role of the State; Introduction ~ Sally Ruane and Humphrey Southall; The control and ‘fitness for purpose’ of UK official statistics ~ David Rhind; The Statistics of Devolution ~ David Byrne; Welfare reform: national policies with local impacts ~ Christina Beatty and Steve Fothergill; Social insecurity and the changing role of the (welfare) state: Public perceptions, social attitudes and political action ~ Christopher Deeming and Ron Johnston; Access to data and NHS privatisation: reducing public accountability ~ Sally Ruane; 4: Economic Life; Introduction ~ Humphrey Southall, Sally Ruane and Jeff Evans; The ‘distribution question’: the role of statistical analysis in measuring and evaluating trends in inequality ~ Stewart Lansley; Labour market statistics ~ Paul Bivand; The financial system ~ Rebecca Boden; The difficulty of building comprehensive tax avoidance data ~ Prem Sikka; Tax and spend decisions: did austerity improve financial numeracy and literacy? ~ David Walker; 5: Inequalities in Health and Well-being; Introduction ~ Sally Ruane and Humphrey Southall; Health Divides ~ Anonymous; Measuring Social Wellbeing ~ Roy Carr-Hill; Re-engineering health policy research to measure equity impacts ~ Tim Doran and Richard Cookson; The Generation Game: Ending the phony information war between young and old ~ Jay Ginn and Neil Duncan-Jordan; 6: Advancing social progress through critical statistical literacy; Introduction ~ Jeff Evans, Humphrey Southall and Sally Ruane; The Radical Statistics Group: Using Statistics for Progressive Social Change ~ Jeff Evans and Ludi Simpson; Lyme disease politics and evidence-based policy-making in the UK ~ Kate Bloor; Counting the uncounted: contestations over casualisation data in Australian universities ~ Nour Dados, James Goodman and Keiko Yasukawa; The quantitative crisis in UK Sociology ~ Malcolm Williams, Luke Sloan and Charlotte Brookfield; Critical Statistical Literacy and Interactive Data Visualisations ~ Jim Ridgway, James Nicholson, Sinclair Sutherland and Spencer Hedger; Full Fact ~ Amy Sippitt; What a difference a dataset makes? Data journalism and/as data activism ~ Jonathan Gray and Liliana Bounegru; Book Epilogue .

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Book SynopsisThis book contains a framework for productive discussion and thinking about ethics and Big Data in business environments. A framework provides you with a set of conceptual terms and tools that help decision-markers to engage difficult questions the expanding role Big Data plays in an increasing variety of products and services.

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Book SynopsisFocuses on data cleaning, which is used to refer to all kinds of tasks and activities to detect and repair errors in the data. Rather than focus on a particular data cleaning task, the book gives an overview of the end-to-end data cleaning process, describing various error detection and repair methods.Table of Contents Preface Figure and Table Credits Introduction Outlier Detection Data Deduplication Data Transformation Data Quality Rule Definition and Discovery Rule-Based Data Cleaning Machine Learning and Probabilistic Data Cleaning Conclusion and Future Thoughts References Index Author Biographies

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Book SynopsisReady to use statistical and machine-learning techniques across large data sets? This practical guide shows you why the Hadoop ecosystem is perfect for the job.

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Book SynopsisUsing real-world stories and situations, the authors show how NoSQL databases and Hadoop can solve a variety of business and research issues. They help you to learn about early decisions and pre-planning that can make the process easier and more productive.

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Book SynopsisThis hands-on guide teaches you how to use H20 with only minimal math and theory behind the learning algorithms.

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Book SynopsisGraph data closes the gap between the way humans and computers view the world. While computers rely on static rows and columns of data, people navigate and reason about life through relationships. This practical guide demonstrates how graph data brings these two approaches together.

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Book SynopsisWith this book, author and DATAcated founder Kate Strachnyi provides the ultimate guide to the correct use of color for representing data in graphs, charts, tables, and infographics.

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Book SynopsisWith this practical guide, author Sev Leonard provides a holistic approach to designing scalable data pipelines in the cloud. Intermediate data engineers, software developers, and architects will learn how to navigate cost/performance trade-offs and how to choose and configure compute and storage.

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Book SynopsisRichard D. Cramer started analysing baseball statistics as a hobby in the mid-1960s, not long after graduating from Harvard and MIT. In When Big Data Was Small Cramer recounts his life and remarkable contributions to baseball knowledge.Trade Review"When Big Data Was Small is one of the most consequential books on baseball history and the evolution of thinking on the game."—Jason Schott, Brooklyn Digest“Dick was one of a handful of people back in the 1970s who started the statistical revolution in baseball . . . in his spare time. He was also a respected scientist with a distinguished career, and he played a little jazz on the side. This book chronicles his life, with its ups and downs, both professional and personal, in an honest and unassuming way. It is an interesting journey, with the last chapter yet to be written.”—Pete Palmer, coauthor of The Hidden Game of Baseball: A Revolutionary Approach to Baseball and Its StatisticsTable of ContentsContentsForeword by John ThornAcknowledgmentsIntroduction1. Setting the Stage2. Baseball and Science Surface3. College4. Graduate School and the 1960s Computer5. Industrial Synthetic Chemist6. Harvard’s Research Computer7. Computer-Aided Drug Discovery8. Sabermetrics’ Infancy9. Scientific Recognition10. Twists of Fate11. Birth of STATS Inc.12. White Sox and Yankees13. Scientific Career Transition14. Rebirth of STATS Inc.15. Comparative Molecular Field Analysis16. STATS Soars17. Cheerlessness and Lyme Disease19. The Rise and Fall of TRPS19. Repudiated by STATS20. Tidying Up21. In My Humble Opinion22. Summing UpAppendix: Bamberg Mathematical Analysis of BaseballNotesBibliographyIndex

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Book SynopsisWhat is latent class analysis? If you asked that question thirty or forty years ago you would have gotten a different answer than you would today. Closer to its time of inception, latent class analysis was viewed primarily as a categorical data analysis technique, often framed as a factor analysis model where both the measured variable indicators and underlying latent variables are categorical. Today, however, it rests within much broader mixture and diagnostic modeling framework, integrating measured and latent variables that may be categorical and/or continuous, and where latent classes serve to define the subpopulations for whom many aspects of the focal measured and latent variable model may differ.For latent class analysis to take these developmental leaps required contributions that were methodological, certainly, as well as didactic. Among the leaders on both fronts was C. Mitchell “Chan” Dayton, at the University of Maryland, whose work in latent class analysis spanning several decades helped the method to expand and reach its current potential. The current volume in the Center for Integrated Latent Variable Research (CILVR) series reflects the diversity that is latent class analysis today, celebrating work related to, made possible by, and inspired by Chan’s noted contributions, and signaling the even more exciting future yet to come.

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Book SynopsisThe general theme of this book is to present innovative psychometric modeling and methods. In particular, this book includes research and successful examples of modeling techniques for new data sources from digital assessments, such as eye-tracking data, hint uses, and process data from game-based assessments. In addition, innovative psychometric modeling approaches, such as graphical models, item tree models, network analysis, and cognitive diagnostic models, are included. Chapters 1, 2, 4 and 6 are about psychometric models and methods for learning analytics. The first two chapters focus on advanced cognitive diagnostic models for tracking learning and the improvement of attribute classification accuracy. Chapter 4 demonstrates the use of network analysis for learning analytics. Chapter 6 introduces the conjunctive root causes model for the understanding of prerequisite skills in learning. Chapters 3, 5, 8, 9 are about innovative psychometric techniques to model process data. Specifically, Chapters 3 and 5 illustrate the usage of generalized linear mixed effect models and item tree models to analyze eye-tracking data. Chapter 8 discusses the modeling approach of hint uses and response accuracy in learning environment. Chapter 9 demonstrates the identification of observable outcomes in the game-based assessments. Chapters 7 and 10 introduce innovative latent variable modeling approaches, including the graphical and generalized linear model approach and the dynamic modeling approach. In summary, the book includes theoretical, methodological, and applied research and practices that serve as the foundation for future development. These chapters provide illustrations of efforts to model and analyze multiple data sources from digital assessments. When computer-based assessments are emerging and evolving, it is important that researchers can expand and improve the methods for modeling and analyzing new data sources. This book provides a useful resource to researchers who are interested in the development of psychometric methods to solve issues in this digital assessment age.Table of Contents Advances in Psychometric Methods for Uncovering Latent Structure and Cognitive Processes Improving Attribute Classification Accuracy in High Dimensional Data: A Four-Step Latent Regression Approach A Dynamic Generalized Mixed Effect Model for Intensive Binary Temporal-Spatio Data From an Eye-Tracking Technique Application of Network Analysis in Understanding Collaborative Problem Solving Processes and Skills IRTree Modeling of Cognitive Processes Based on Outcome and Intermediate Data Prerequisite Structure Finding Using the Conjunctive Root Causes Model A Graphical and Generalized Linear Model Approach to Latent Variable Modeling Modeling Hint Requests, Response Times, and Response Accuracy in Adaptive Learning Systems Identifying Observable Outcomes in Game-Based Assessments A Regime-Switching (RS) Framework for Formulating Multi-Phase Linear and Nonlinear Growth Curve Models About the Editors.

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Book SynopsisThe general theme of this book is to present innovative psychometric modeling and methods. In particular, this book includes research and successful examples of modeling techniques for new data sources from digital assessments, such as eye-tracking data, hint uses, and process data from game-based assessments. In addition, innovative psychometric modeling approaches, such as graphical models, item tree models, network analysis, and cognitive diagnostic models, are included. Chapters 1, 2, 4 and 6 are about psychometric models and methods for learning analytics. The first two chapters focus on advanced cognitive diagnostic models for tracking learning and the improvement of attribute classification accuracy. Chapter 4 demonstrates the use of network analysis for learning analytics. Chapter 6 introduces the conjunctive root causes model for the understanding of prerequisite skills in learning. Chapters 3, 5, 8, 9 are about innovative psychometric techniques to model process data. Specifically, Chapters 3 and 5 illustrate the usage of generalized linear mixed effect models and item tree models to analyze eye-tracking data. Chapter 8 discusses the modeling approach of hint uses and response accuracy in learning environment. Chapter 9 demonstrates the identification of observable outcomes in the game-based assessments. Chapters 7 and 10 introduce innovative latent variable modeling approaches, including the graphical and generalized linear model approach and the dynamic modeling approach. In summary, the book includes theoretical, methodological, and applied research and practices that serve as the foundation for future development. These chapters provide illustrations of efforts to model and analyze multiple data sources from digital assessments. When computer-based assessments are emerging and evolving, it is important that researchers can expand and improve the methods for modeling and analyzing new data sources. This book provides a useful resource to researchers who are interested in the development of psychometric methods to solve issues in this digital assessment age.Table of Contents Advances in Psychometric Methods for Uncovering Latent Structure and Cognitive Processes Improving Attribute Classification Accuracy in High Dimensional Data: A Four-Step Latent Regression Approach A Dynamic Generalized Mixed Effect Model for Intensive Binary Temporal-Spatio Data From an Eye-Tracking Technique Application of Network Analysis in Understanding Collaborative Problem Solving Processes and Skills IRTree Modeling of Cognitive Processes Based on Outcome and Intermediate Data Prerequisite Structure Finding Using the Conjunctive Root Causes Model A Graphical and Generalized Linear Model Approach to Latent Variable Modeling Modeling Hint Requests, Response Times, and Response Accuracy in Adaptive Learning Systems Identifying Observable Outcomes in Game-Based Assessments A Regime-Switching (RS) Framework for Formulating Multi-Phase Linear and Nonlinear Growth Curve Models About the Editors.

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Book SynopsisThis text provides an overview of the principles and practices involved in managing and operating data centers. It covers topics such as data center design, infrastructure management, virtualization, cloud computing, and security. The book is intended for IT professionals and data center managers who are responsible for the operation and maintenance of data centers. It provides valuable insights and best practices for optimizing data center performance, reliability, and efficiency.Table of Contents Chapter 1 Introduction to Data Center Management Chapter 2 Data Center Topologies and Network Architecture Chapter 3 Security and Compliance in Data Protection Chapter 4 Monitoring and Management Tools Chapter 5 Virtualization and Cloud Computing Chapter 6 Importance of Power and Cooling Management Chapter 7 Challenges in Data Center Management Chapter 8 Future Trends in Data Center Management

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