Data science and analysis Books

Book SynopsisThis is a book about how ecologists can integrate remote sensing and GIS in their research. It will allow readers to get started with the application of remote sensing and to understand its potential and limitations. Using practical examples, the book covers all necessary steps from planning field campaigns to deriving ecologically relevant information through remote sensing and modelling of species distributions. An Introduction to Spatial Data Analysis introduces spatial data handling using the open source software Quantum GIS (QGIS). In addition, readers will be guided through their first steps in the R programming language. The authors explain the fundamentals of spatial data handling and analysis, empowering the reader to turn data acquired in the field into actual spatial data. Readers will learn to process and analyse spatial data of different types and interpret the data and results. After finishing this book, readers will be able to address questions such as “What is the distance to the border of the protected area?”, “Which points are located close to a road?”, “Which fraction of land cover types exist in my study area?” using different software and techniques. This book is for novice spatial data users and does not assume any prior knowledge of spatial data itself or practical experience working with such data sets. Readers will likely include student and professional ecologists, geographers and any environmental scientists or practitioners who need to collect, visualize and analyse spatial data. The software used is the widely applied open source scientific programs QGIS and R. All scripts and data sets used in the book will be provided online at book.ecosens.org. This book covers specific methods including: what to consider before collecting in situ data how to work with spatial data collected in situ the difference between raster and vector data how to acquire further vector and raster data how to create relevant environmental information how to combine and analyse in situ and remote sensing data how to create useful maps for field work and presentations how to use QGIS and R for spatial analysis how to develop analysis scripts Table of ContentsPreface 1. Introduction and overview 1.1 Spatial data 1.2 First spatial data analysis 1.3 Next steps Part I. Data acquisition, data preparation and map creation 2. Data acquisition 2.1 Spatial data for a research question 2.2 AOI 2.3 Thematic raster map acquisition 2.4 Thematic vector map acquisition 2.5 Satellite sensor data acquisition 2.6 Summary and further reading 3. Data preparation 3.1 Deciding on a projection 3.2 Reprojecting raster and vector layers 3.3 Clipping to an AOI 3.4 Stacking raster layers 3.5 Visualizing a raster stack as RGB 3.6 Summary and further reading 4. Creating maps 4.1 Maps in QGIS 4.2 Maps for presentations 4.3 Maps with statistical information 4.4 Common mistakes and recommendations 4.5 Summary and further reading Part II. Spatial field data acquisition and auxiliary data 5. Field data planning and preparation 5.1 Field sampling strategies 5.2 From GIS to global positioning system (GPS) 5.3 On-screen digitization 5.4 Summary and further reading6. Field sampling using a global positioning system (GPS) 97 6.1 GPS in the field 98 6.2 GPX from GPS 101 6.3 Summary 102 7. From global positioning system (GPS) to geographic information system (GIS) 103 7.1 Joint coordinates and measurement sheet 104 7.2 Separate coordinates and measurement sheet 105 7.3 Point measurement to information 106 7.4 Summary 108 Part III. Data analysis and new spatial information 8. Vector data analysis 110 8.1 Percentage area covered 114 8.2 Spatial distances 118 8.3 Summary and further analyses 121 9. Raster analysis 122 9.1 Spectral landscape indices 122 9.2 Topographic indices 128 9.3 Spectral landscape categories 128 9.4 Summary and further analysis 133 10. Raster-vector intersection 134 10.1 Point statistics 135 10.2 Zonal statistics 136 10.3 Summary 138 Part IV. Spatial coding 11. Introduction to coding 140 11.1 Why use the command line and what is ‘R’? 140 11.2 Getting started 142 11.3 Your very first command 142 11.4 Classes of data 144 11.5 Data indexing (subsetting) 145 11.6 Importing and exporting data 147 11.7 Functions 148 11.8 Loops 149 11.9 Scripts 149 11.10 Expanding functionality 150 11.11 Bugs, problems and challenges 151 11.12 Notation 152 11.13 Summary and further reading 15212. Getting started with spatial coding 153 12.1 Spatial data in R 153 12.2 Importing and exporting data 158 12.3 Modifying spatial data 162 12.4 Downloading spatial data from within R 166 12.5 Organization of spatial analysis scripts 170 12.6 Summary 171 13. Spatial analysis in R 172 13.1 Vegetation indices 172 13.2 Digital elevation model (DEM) derivatives 174 13.3 Classification 175 13.4 Raster-vector interaction 179 13.5 Calculating and saving aggregated values 182 13.6 Summary and further reading 184 14. Creating graphs in R 185 14.1 Aggregated environmental information 185 14.2 Non-aggregated environmental information 189 14.3 Finalizing and saving the plot 194 14.4 Summary and further reading 195 15. Creating maps in R 196 15.1 Vector data 197 15.2 Plotting study area data 202 15.3 Summary and further reading 206 Afterword and acknowledgements 207 References 209 Index 210

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Book SynopsisWhen would a casino stop a gambler from playing his next hand? How could a company use statistical analysis to blackball you from the job you want? Why should you worry when customer services pay attention to your needs? Beginning with examples of the mathematician who out-predicted wine buffs in determining the best vintages, and the sports scouts who now use statistics rather than intuition to pick winners, Super Crunchers exposes the hidden patterns all around us. No businessperson, academic, student, or consumer (statistically that''s everyone) should make another move without getting to grips with thinking-by-numbers - the new way to be smart, savvy and statistically superior.Trade ReviewGroundbreaking ... Not only is it fun to read. It just may change the way you think' * Stephen D Levitt co-author of Freakonomics *'Entertaining and enlightening' * Financial Times *'Convincing' * Economist *

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Book SynopsisThis textbook presents an innovative new perspective on the economics of development, including insights from a broad range of disciplines. It starts with the current state of affairs, a discussion of data availability, reliability, and analysis, and an historic overview of the deep influence of fundamental factors on human prosperity. Next, it focuses on the role of human interaction in terms of trade, capital, and knowledge flows, as well as the associated implications for institutions, contracts, and finance. The book also highlights differences in the development paths of emerging countries in order to provide a better understanding of the concepts of development and the Millennium Development Goals. Insights from other disciplines are used help to understand human development with regard to other issues, such as inequalities, health, demography, education, and poverty. The book concludes by emphasizing the importance of connections, location, and human interaction in determining fTrade Review'This is a masterful textbook on development. It extensively discusses the root causes of development, and more recent topics such as randomized controlled trials. The book stands out by also providing a rich discussion of 'international' issues relevant for development, such as globalization, international trade, migration, and international financial flows.' Robert Lensink, University of Groningen'A splendid new textbook by van Marrewijk and Brakman! Their lucid exposition is wide ranging, deeply informed and up to date. The student will acquire a broad knowledge of developing and emerging economies and, more importantly, understand the data, theories, and methods that inform the authors' insights.' Donald Davis, Columbia University'This excellent new textbook on development economics is up to date in its coverage of research - history, data, and theories. It explains difficult concepts simply and clearly. The visual presentation - figures, charts, and use of color - is outstanding. It is balanced and thoughtful in its assessment of the issues and policies. A treasure for students and teachers alike.' Avinash Dixit, Princeton UniversityTable of ContentsPart I. Introduction and Deep Roots: 1. Economic Development Today; 2. Data and Methods; 3. Uneven Playing Field; 4. Geo-Human Interaction; Part II. Human Interaction: 5. Globalization and Development; 6. International Trade; 7. Economic Growth; 8. Institutions and Contracts; 9. Money and Finance; Part III. Human Development: 10. Poverty, Inequality, and Gender; 11. Poor Economics; 12. Population and Migration; 13. Education; 14. Health; Part IV. Connections and Interactions: 15. Agriculture and Development; 16. Urbanization and Agglomeration; 17. Geographical Economics and Development; 18. Heterogeneous and Multinational Firms; 19. Sustainability and Development.

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Book SynopsisTrade Review"I very much recommend this book, not only to all that teach statistics to (under)graduate students, but also those that use statistics for their own research, that would like to value the work of others, or engage in debates using actual or perceived facts."---Gijs Dekkers, International Statsitical Review

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Book SynopsisNew and expanded edition.An International Bestseller - Over One Million Copies Sold!Shortlisted for the Financial Times/Goldman Sachs Business Book of the Year Award.Since Aristotle, we have fought to understand the causes behind everything. But this ideology is fading. In the age of big data, we can crunch an incomprehensible amount of information, providing us with invaluable insights about the what rather than the why.We''re just starting to reap the benefits: tracking vital signs to foresee deadly infections, predicting building fires, anticipating the best moment to buy a plane ticket, seeing inflation in real time and monitoring social media in order to identify trends. But there is a dark side to big data. Will it be machines, rather than people, that make the decisions? How do you regulate an algorithm? What will happen to privacy? Will individuals be punished for acts they have yet to commit? Trade Review'Just as water is wet in a way that individual water molecules aren't, big data can reveal information in a way that individual bits of data can't. Mayer-Schonberger and Cukier show us the surprising ways that enormous, complex and messy collections of data can be used to predict everything from shopping patterns to flu outbreaks' - Clay Shirky, author of Cognitive Surplus and Here Comes Everybody'Every decade, there are a handful of books that change the way you look at everything. This is one of those books. Society has begun to reckon the change that big data will bring. This book is an incredibly important start' - Lawrence Lessig, Roy L. Furman Professor of Law, Harvard Law School, and author of Remix and Free Culture'An optimistic and practical look at the big data revolution - just the thing to get your head around the big changes already underway and the bigger changes to come' - Cory Doctorow, Boing Boing'In Big Data, Mayer-Schonberger and Cukier break new ground in identifying how today's avalanche of information fundamentally shifts our basic understanding of the world. Argued boldly and written beautifully, the book clearly shows how companies can unlock value, how policymakers need to be on guard, and how everyone's cognitive models need to change' - Joi Ito, Director of the MIT Media Lab'This brilliant book cuts through the mystery and the hype surrounding big data. A must-read for anyone in business, information technology, public policy, intelligence, and medicine. And anyone else who is just plain curious about the future' - John Seely Brown, former Chief Scientist, Xerox Corp. and head of Xerox Palo Alto Research Centre'The book teems with great insights on the new ways of harnessing information, and offers a convincing vision of the future. It is essential reading for anyone who uses - or is affected by - big data' - Jeff Jonas, IBM Fellow & Chief Scientist, IBM Entity Analytics'Big Data is a must-read for anyone who wants to stay ahead of one of the key trends defining the future of business' - Marc Benioff, Chairman and CEO, salesforce.comAn excellent primer - Financial Times

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Book SynopsisDirty data is a problem that costs businesses thousands, if not millions, every year. In organisations large and small across the globe you will hear talk of data quality issues. What you will rarely hear about is the consequences or how to fix it.Between the Spreadsheets: Classifying and Fixing Dirty Data draws on classification expert Susan Walsh’s decade of experience in data classification to present a fool-proof method for cleaning and classifying your data. The book covers everything from the very basics of data classification to normalisation and taxonomies, and presents the author’s proven COAT methodology, helping ensure an organisation’s data is Consistent, Organised, Accurate and Trustworthy. A series of data horror stories outlines what can go wrong in managing data, and if it does, how it can be fixed. After reading this book, regardless of your level of experience, not only will you be able to work with your data more efficiently, but you will also understand the impact the work you do with it has, and how it affects the rest of the organisation.Written in an engaging and highly practical manner, Between the Spreadsheets gives readers of all levels a deep understanding of the dangers of dirty data and the confidence and skills to work more efficiently and effectively with it. Trade Review'If you are teaching data science then all your students should be made aware of this book. When it comes to organisations. I can’t see any reason for not making sure that anyone managing an Excel data base has a copy to refer to.... Excellent value for the price' -Martin White, Informer 'I gained many practical tips for using a spreadsheet to clean data, and alternate ways of approaching classification while reading this book - there is hope for cleaner data!' - Mary Silvia Whittaker, SLA Taxonomy'I have rarely found such a brilliant argument about the importance of COAT - the overall approach to the management of data. The author approaches all her topics with palpable humour and presents them in lively and attractive style. A relevant acquisition for business information departments or their equivalents in public libraries as much as putting it on the desks of the people dealing with all kinds of business data.'Elena Maceviciute, Swedish School of Library and Information ScienceTable of ContentsBetween the Spreadsheets: Classifying and Fixing Dirty Data

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Book SynopsisMathematicians have skills that, if deepened in the right ways, would enable them to use data to answer questions important to them and others, and report those answers in compelling ways. Data science combines parts of mathematics, statistics, computer science. Gaining such power and the ability to teach has reinvigorated the careers of mathematicians. This handbook will assist mathematicians to better understand the opportunities presented by data science. As it applies to the curriculum, research, and career opportunities, data science is a fast-growing field. Contributors from both academics and industry present their views on these opportunities and how to advantage them.

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Book SynopsisWhen you know the cause of an event, you can affect its outcome. This accessible introduction to causal inference shows you how to determine causality and estimate effects using statistics and machine learning.In Causal Inference for Data Science you will learn how to: Model reality using causal graphs Estimate causal effects using statistical and machine learning techniques Determine when to use A/B tests, causal inference, and machine learning Explain and assess objectives, assumptions, risks, and limitations Determine if you have enough variables for your analysis It''s possible to predict events without knowing what causes them. Understanding causality allows you both to make data-driven predictions and also intervene to affect the outcomes. Causal Inference for Data Science shows you how to build data science tools that can identify the root cause of trends and events. You''ll learn how to interpret historical data, understand customer behaviors, and empower management to apply optimal decisions.

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Book SynopsisA year of HBR''s essential thinking on tech—all in one place.Generative AI, biometrics, spatial computing, electric vehicles—new technologies like these are reshaping organizations at the hybrid office, on factory floors, and in the C-suite. What should you and your company be doing now to take advantage of the new opportunities these technologies are creating—and avoid falling victim to disruption?The Year in Tech 2025: The Insights You Need from Harvard Business Review will help you understand what the latest and most important tech innovations mean for your organization and how you can use them to compete and win in today’s turbulent business environment.Business is changing. Will you adapt or be left behind?Get up to speed and deepen your understanding of the topics that are shaping your company''s future with the Insights You Need from Harvard Business Review series. Featuring HBR''s smartest thinki

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Book SynopsisData Science students and practitioners want to find a forecast that works and don't want to be constrained to a single forecasting strategy, Time Series for Data Science: Analysis and Forecasting discusses techniques of ensemble modelling for combining information from several strategies. Covering time series regression models, exponential smoothing, Holt-Winters forecasting, and Neural Networks. It places a particular emphasis on classical ARMA and ARIMA models that is often lacking from other textbooks on the subject.This book is an accessible guide that doesn't require a background in calculus to be engaging but does not shy away from deeper explanations of the techniques discussed.Features:Provides a thorough coverage and comparison of a wide array of time series models and methods: Exponential Smoothing, Holt Winters, ARMA and ARIMA, deep learning models including RNNs, LSTMs, GRUs, and ensemble models composed of combinations of thesTrade Review"A well-structured text aimed at undergraduates pursuing a data science curriculum, or MBA students. The authors draw upon their vast combined experience in research and teaching to a variety of audiences to present the classical material on ARMA-based Box-Jenkins methodology without assuming a calculus background. Yet, their approach manages to be heuristic, while not sacrificing relevant theoretical detail that enriches understanding. The authors complement this material with chapters on multivariate models, and, refreshingly, a very enlightening discussion on neural networks. The exposition is lucid, well-organized, and copiously illustrated to reinforce comprehension of concepts. The companion R package (tswge) finds a niche in the growing list of time series toolboxes, by providing clean, straightforward functionality on such essentials as spectrum reconstruction and model factor tables to glean the structure of AR and MA polynomials."- Alex Trindade, Texas Tech University Table of Contents1. Working with Data Collected Over Time, 2. Exploring Time Series Data, 3. Statistical Basics for Time Series Analysis, 4. The Frequency Domain, 5. ARMA Models, 6. ARMA Fitting and Forecasting, 7. ARIMA, Seasonal,and ARCH/GARCH Models, 8. Time Series Regression, 9. Model Assessment, 10. Multivariate Time Series, 11. Deep Neural Network Based Time Series Models

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Book SynopsisKiko Llaneras is a data journalist at El País and holds a Ph.D. in industrial engineering. He has taught at the University of Girona and the Polytechnic University of Valencia.

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Book SynopsisDiscusses methods available for longitudinal data analysis in non-technical language, allowing readers to apply techniques easily to their work. Aimed at non-statisticians and researchers working in medical science and utilising longitudinal studies, the interpretation of the results of various methods of analysis is emphasised.Table of Contents1. Introduction; 2. Continuous outcome variables; 3. Continuous outcome variables – regression based methods; 4. The modelling of time; 5. Models to disentangle the between- and within-subjects relationship; 6. Causality in observational longitudinal studies; 7. Dichotomous outcome variables; 8. Categorical and count outcome variables; 9. Outcome variables with floor or ceiling effects; 10. Analysis of longitudinal intervention studies; 11. Missing data in longitudinal studies; 12. Sample size calculations; 13. Software for longitudinal data analysis.

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Book SynopsisThis textbook is designed for students in statistics, data science, biostatistics, engineering, and physical science programs who need a solid course in the fundamental concepts, methods and theory of statistics to understand, use, and build on modern statistical techniques for complex problems. Examples and exercises incorporate data and R code.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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Book SynopsisThis is a book on public opinion told through the eyes of the pollster. It looks at public opinion as a concept and in historical context then turns to the use of public opinion practically as an analytic input; particularly, how to use it to forecast, convince, and assess.

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Book SynopsisCritical Theory and Qualitative Data Analysis in Education offers a path-breaking explanation of how critical theories can be used within the analysis of qualitative data to inform research processes, such as data collection, analysis, and interpretation. This contributed volume offers examples of qualitative data analysis techniques and exemplars of empirical studies that employ critical theory concepts in data analysis. By creating a clear and accessible bridge between data analysis and critical social theories, this book helps scholars and researchers effectively translate their research designs and findings to multiple audiences for more equitable outcomes and disruption of historical and contemporary inequality.Trade Review"In addition to offering ways for scholars to build on existing methodologies, contributors to this volume also offer new paradigms while inviting all scholars to consider their tools…. I am encouraged by education researchers desire to transcend contrived borders and employ the methods, forms of methods, analysis, and strategic reconstruction of these forms in order to ask an entirely new set of questions."—from the Foreword by Maisha T. Winn, Chancellor's Leadership Professor, and Co-Director of Transformative Justice in Education (TJE) at the University of California- Davis, USAThis book provides critical scholars interested in paying attention to the larger social structural issues and oppression with tangible "how to’s" for critical qualitative data analysis and, in this way, encourage researchers to work toward social change. Too often data analysis processes are reductive and void of theory and, importantly, the authors fill a gap in the existing literature. This thoughtful book is useful for introductory qualitative researchers and senior scholars alike. — Penny A. Pasque, Professor and Department Head, Educational Leadership, Policy, and Human Development, North Carolina State University, USATable of ContentsContentsForeword: Reflections on Research that is Humanizing Maisha T. WinnPreface Ashley N. Gaskew, Jamile Lee-Johnson, and Rachelle Winkle-WagnerSection I The Need for Partnering Critical Theory and Data Analysis in EducationChapter 1 The Missing Link in Data Analysis: An Introduction to the Use of Critical Theory to Guide Data Analysis Rachelle Winkle-Wagner, Ashley Gaskew, and Jamila Lee-JohnsonChapter 2 The Missing InfinitePhil Frances CarspeckenChapter 3 Trouble the Translating: Border Thinking, Indigenous Knowledges, and Undoing MethodNathaniel BeckChapter 4 De-Norming the Syllabus: An Analysis Situated in Critical and Caring PedagogiesVirginia M. Schwarz Section II Critical Theory and Analysis with Marginalized Populations Section Introduction Keon M. McGuire Chapter 5 Illuminating Systemic Inequality in Educations: Using Bourdieu in Critical Qualitative Data AnalysisTangela Blakely Reavis Chapter 6 "Wanna Be Startnin’ Somethin:" Answering the Methodological "Call" To Position Complex Blackness in Conversation with Hermeneutic PhenomenologySteve D. Mobley, Jr. Chapter 7 Thinking with Queer of Color Critique: A Multidimensional Approach to Analyzing and Interpreting DataReginald A. Blockett Chapter 8 Globalization, Higher Education, and Crisis: A Model for Applying Critical Geography toward Data AnalysisChelsea A. Blackburn CohenChapter 9 Context and Materiality: Inclusive Appropriations of New Materialism for Qualitative AnalysisBarbara Dennis Section III Critical Theories and Data Analysis in Institutions and Policies Section IntroductionChristina W. Yao Chapter 10 A Culture of Values: Rethinking School Quality and Culture through Fanonian Critical TheoryJacqueline M. ForbesChapter 11 Discourse, Representation, and "Othering": Postcolonial Analysis of Donald Trump’s Education ReformMercy AgyepongChapter 12 Analyzing Policy Critically: Using Critical Race Theory to AnalyzeRachelle Winkle-Wagner, Thandi Sulè, and Dina C. Maramba Chapter 13 Habermas and Data Analysis in For-Profit Higher Education InstitutionsAshley GaskewChapter 14 Sistahs’ are Doin’ it for themselves: Using Social Media to (Re)Center Black Women’s Voices in Educational ResearchJamila Lee-Johnson and Lora HendersonAfterwordJamila Lee-Johnson, Ashley Gaskew, and Rachelle Winkle-WagnerContributor Bios

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Book SynopsisThis book presents an accessible introduction to data-driven storytelling. Resulting from unique discussions between data visualization researchers and data journalists, it offers an integrated definition of the topic, presents vivid examples and patterns for data storytelling, and calls out key challenges and new opportunities for researchers and practitioners.  Trade ReviewTrumpets please! This lucidly written report on data-driven storytelling lays out the compelling benefits and substantial challenges of this potent journalistic innovation. The strong team of authors offer fresh thinking and thoughtful guidance on exploration, explanation, engagement, ethics, and evaluation. Packed with examples for practitioners and references for researchers, this book opens up fresh possibilities that extend information visualization into decision-making, policy-shifting, and mind-changing applications.--Ben Shneiderman, University of MarylandData-Driven Storytelling presents an accessible and informative introduction to data-driven storytelling. Resulting from unique discussions between data visualization researchers and data journalists, Data-Driven Storytelling offers an integrated definition of the topic, presents vivid examples and patterns for data storytelling, and calls out key challenges and new opportunities for researchers and practitioners. The newest addition to the simply outstanding CRC Press 'A K Peters Visualization Series', Data-Driven Storytelling is unreservedly recommended for professional, corporate, college, and university library Computer Science & Engineering collections and supplemental studies reading lists.--Midwest Book Review, May 2018 Data-Driven Storytelling promotes an efficient way of data-driven storytelling in professionalism and its profound implications. This approach involves visualizations, explorations, explanations, curated visuals, narrative design patterns, process from analysis to communication, impressing the audience, ethical guidelines and evaluation methods on successful the data driven storytelling.--Ramalingam Shanmugam, Journal of Statistical Computation and Simulation“This bible of data-driven storytelling covers everything you need to know about the topic, from storytelling techniques (including scrollytelling) and narrative design patterns to evaluation and ethics in storytelling, providing readers with explanations and examples of the concepts described as well as extensive reference material. All of this is expressed in an accurate, concise, and clear way.”--Lorenzo Amabili, University of Groningen, Book Review in NightingaleTrumpets please! This lucidly written report on data-driven storytelling lays out the compelling benefits and substantial challenges of this potent journalistic innovation. The strong team of authors offer fresh thinking and thoughtful guidance on exploration, explanation, engagement, ethics, and evaluation. Packed with examples for practitioners and references for researchers, this book opens up fresh possibilities that extend information visualization into decision-making, policy-shifting, and mind-changing applications.--Ben Shneiderman, University of MarylandData-Driven Storytelling presents an accessible and informative introduction to data-driven storytelling. Resulting from unique discussions between data visualization researchers and data journalists, Data-Driven Storytelling offers an integrated definition of the topic, presents vivid examples and patterns for data storytelling, and calls out key challenges and new opportunities for researchers and practitioners. The newest addition to the simply outstanding CRC Press 'A K Peters Visualization Series', Data-Driven Storytelling is unreservedly recommended for professional, corporate, college, and university library Computer Science & Engineering collections and supplemental studies reading lists.--Midwest Book Review, May 2018 Data-Driven Storytelling promotes an efficient way of data-driven storytelling in professionalism and its profound implications. This approach involves visualizations, explorations, explanations, curated visuals, narrative design patterns, process from analysis to communication, impressing the audience, ethical guidelines and evaluation methods on successful the data driven storytelling.--Ramalingam Shanmugam, Journal of Statistical Computation and Simulation“This bible of data-driven storytelling covers everything you need to know about the topic, from storytelling techniques (including scrollytelling) and narrative design patterns to evaluation and ethics in storytelling, providing readers with explanations and examples of the concepts described as well as extensive reference material. All of this is expressed in an accurate, concise, and clear way.”--Lorenzo Amabili, University of Groningen, Book Review in NightingaleTable of ContentsIntroduction. From Cognitive to Design Principles. Exploration and Explanation in Data-Driven Storytelling. Data-Driven Storytelling Techniques: Analysis of a Curated Collection of Visual Stories. Narrative Design Patterns for Data-Driven Storytelling. Watches to Augmented Reality Devices and Gadgets for Data-Driven Storytelling. From Analysis to Communication Supporting the Lifecycle of a Story. Organizing the Work of Data-Driven Visual Storytelling. Communicating Data to an Audience.Ethics in Data-Driven Visual Storytelling. Evaluating Data-Driven Stories & Storytelling Tools.

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Book SynopsisWhat happens to risk as the economic horizon goes to zero and risk is seen as an exposure to a change in state that may occur instantaneously at any time? All activities that have been undertaken statically at a fixed finite horizon can now be reconsidered dynamically at a zero time horizon, with arrival rates at the core of the modeling. This book, aimed at practitioners and researchers in financial risk, delivers the theoretical framework and various applications of the newly established dynamic conic finance theory. The result is a nonlinear non-Gaussian valuation framework for risk management in finance. Risk-free assets disappear and low risk portfolios must pay for their risk reduction with negative expected returns. Hedges may be constructed to enhance value by exploiting risk interactions. Dynamic trading mechanisms are synthesized by machine learning algorithms. Optimal exposures are designed for option positioning simultaneously across all strikes and maturities.Table of Contents1. Introduction; 2. Univariate risk representation using arrival rates; 3. Estimation of univariate arrival rates from time series data; 4. Estimation of univariate arrival rates from option surface data; 5. Multivariate arrival rates associated with prespecified univariate arrival rates; 6. The measure-distorted valuation as a financial objective; 7. Representing market realities; 8. Measure-distorted value-maximizing hedges in practice; 9. Conic hedging contributions and comparisons; 10. Designing optimal univariate exposures; 11. Multivariate static hedge designs using measure-distorted valuations; 12. Static portfolio allocation theory for measure-distorted valuations; 13. Dynamic valuation via nonlinear martingales and associated backward stochastic partial integro-differential equations; 14. Dynamic portfolio theory; 15. Enterprise valuation using infinite and finite horizon valuation of terminal liquidation; 16. Economic acceptability; 17. Trading Markovian models; 18. Market implied measure-distortion parameters; References; Index.

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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 SynopsisJordan Morrow is known as the "Godfather of Data Literacy", having helped pioneer the field by building one of the world's first data literacy programs. He is the founder and CEO of Bodhi Data, served as the Chair of the Advisory Board for The Data Literacy Project and has helped companies and organizations around the world, including the United Nations, build and understand data literacy. Morrow is the author of three books: Be Data Literate, Be Data Driven, and Be Data Analytical, all published by Kogan Page. He is based near Salt Lake City, Utah.Trade Review"A must-read for anyone looking to harness the power of data. Be Data Analytical stands out as a comprehensive guide that empowers readers to unlock the hidden potential within their data, driving innovation and growth in any field." * Bernard Marr, Founder & CEO, Bernard Marr & Co *"If you're looking for a practical guide to learn about the four levels of analytics, look no further than Be Data Analytical. Jordan Morrow's hands-on approach to teaching data analytics makes the book an invaluable resource for anyone who wants to learn the skills needed to succeed in the field. The clear explanations, practical examples and content breakdown make this book an excellent choice for both beginners and experienced professionals." * Chandra Donelson, Washington D.C. Chapter Lead, Women in Data *"Jordan's passion and enthusiasm for data shines through. Breaking down analytics into four accessible levels means this book is for everyone. Its real-life examples and analogies bring to life the importance of understanding and implementing good analytics." * Susan Walsh, Founder & Managing Director, The Classification Guru Ltd *"This book provides an excellent framework for data-driven decision-making in organizations. By framing the analytics implementations progressively through the four levels of analytics, Be Data Analytical is easy to follow as an analytics guidebook. At each stage, the book covers key definitions, roles of the different enterprise players, numerous business examples and strategy suggestions to get the analytics job done." * Kirk Borne, Founder, Data Leadership Group *"Data Analytics is a crucial aspect of decision-making in the modern business landscape, and this book provides a comprehensive guide to understanding its nuances. The author's expertise and passion for the subject is present in every chapter, making this book a must-read for anyone seeking to improve their data literacy and enhance their decision-making skills. I highly recommend this book to anyone looking to unlock the power of data analytics in their organization." * Esther Munyi, Chief Data and Analytics Officer, Sasfin *"Be Data Analytical is a book about leadership, decision making, staying ahead and having your own built-in systems. A consummate storyteller, Jordan speaks to those who know this space and those who perhaps need to. The data environment has changed forever and the complexity and challenge for leaders means the rule book we used to follow, and our previous frames of reference, are redundant. New ways of thinking and improving decision making are therefore vital." * Mike Roe, CEO, Tensense.ai *"Ingenious! Jordan's engaging work propels the reader from data literacy to data analysis." * Major General Dustin Shultz *Table of Contents Chapter - 00: Introduction; Section - ONE: Data and analytics; Chapter - 01: Defining data and analytics; Chapter - 02: Defining the four levels of analytics; Chapter - 03: The power of analytics in decision making; Section - TWO: The four levels of analytics - define, empower, understand and learn; Chapter - 04: Descriptive analytics; Chapter - 05: How are descriptive analytics used today?; Chapter - 06: How individuals and organizations can improve in descriptive analytics; Chapter - 07: Diagnostic analytics; Chapter - 08: How are diagnostic analytics used today?; Chapter - 09: How individuals and organizations can improve in diagnostic analytics; Chapter - 10: Predictive analytics; Chapter - 11: How are predictive analytics used today?; Chapter - 12: How individuals and organizations can improve in predictive analytics; Chapter - 13: Prescriptive analytics; Chapter - 14: How are prescriptive analytics used today?; Chapter - 15: How individuals and organizations can improve in prescriptive analytics; Section - Three: Bringing it all together; Chapter - 16: Using all four levels of analytics to empower decision making; Chapter - 17: Conclusion;

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Book SynopsisThomas C Redman is known as the 'Data Doc' and is the founder and President of Data Quality Solutions. Through this company he helps people and organizations think about data and data quality in new and exciting ways. Based in Rumson, New Jersey, he previously worked at AT&T where he formed their data quality lab.Trade Review"Based on my own decades-long experience working with organizations on removing data-related barriers and building data-driven strategies, I could not agree more with Tom's guidance to put people at the center. He's on to something massively important here - if you internalize and act on the people-focused principles he's suggesting, you will no doubt accelerate and amplify your impact on the business." * Ted Friedman, Former Gartner Analyst and Industry Thought-Leader *"For any organisation to succeed in the 21st Century, it needs people and data. Moreover, as Tom Redman explains in People and Data, when these two elements unite and when the benefits of data are extended to everyone in the organisation you can transform your business. Whether you are a leader, manager or worker or whether you work in HR, Finance, Operations or Marketing, I heartily recommend reading People and Data." * David Green, Co-author of Excellence in People Analytics, Managing Partner at Insight222, and host of the Digital HR Leaders podcast. *"People and Data offers great insights and advice on how organizations can unleash "real people" , working together, to solve the data quality problem, once and for all. We could all benefit from Tom's optimism and experience." * Maria Villar, Head of Enterprise Data Strategy & Transformation, SAP North America *"People and Data is the provocation that many data managers and executives need to spur them into action. Today's world is driven by data, but as Tom reminds us, if the data is poor (garbage in), then the outcomes are as well (garbage out). Tom's solution for maintaining quality data lies not in technology but in people - how they are organized, what tasks they are given, what culture they create, how they are motivated, promoted and trained. Tom explores all aspects of building a world-class data organization." * Theresa Kushner, former Head of Innovation Center, NTT Data, North America *"Tom Redman has a knack for taking the complex world of data and making it simple to understand and improve. What businesses have struggled with for decades is delivered in this next generational approach: data is predominantly a people issue and must be considered a team sport. In this new book, Redman brings solutions that highlight how regular people working together within and across organizations, under the direction of senior leaders, can finally solve this very expensive and seemingly endless enigma and no longer need to stand on the data sidelines." * Bob Palermo, former Vice President, Performance Excellence, Shell *"The book, People and Data: Uniting to transform your business is an exceptional resource by one of the world's leading thinkers and practitioners on 'data' - Dr. Thomas Redman. Every professional, with "data" in their title or not, should read Tom's book and learn from his experiences developed over 25+ years as the 'Data Doc'." * Anne Marie Smith, Ph.D., Alabama Yankee Systems, LLC *"Nothing of quality happens without quality people, fueled with quality data, making quality decisions. Here Redman establishes the case, roadmap and tools for dramatic business growth through quality data. Lead People, Manage Assets goes the adage. Tom shows us sensible ways to Lead regular people in uniting to successfully manage the unique assets of data, and information technology. Fortune Favors The Brave: so read People and Data - then lead bravely for sustained business growth." * Robert Pautke, Founder & Leadership Coach, SOAR with Purpose, LLC *Table of Contents Chapter - 00: Introduction; Section - ONE: The big picture; Chapter - 01: Ann’s data Tuesday; Chapter - 02: The opportunity and the problem; Chapter - 03: Building a better organization for data; Section - TWO: People; Chapter - 04: The data generation and provocateurs; Chapter - 05: All roads lead through quality; Chapter - 06: Putting data to work; Section - THREE: Data is a team sport; Chapter - 07: Fat organizational pipes; Chapter - 08: Don’t confuse apples and oranges; Chapter - 09: Dream big, but change the culture one project at a time; Section - 10: The data teams companies need now; Section - 11: Conclusion - Courage required; Section - 12: Resource Centre 1 - Toolkit; Section - 13: Resource Centre 2 - Curriculum for training regular people;

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Book SynopsisJordan Morrow is known as the "Godfather of Data Literacy", having helped pioneer the field by building one of the world's first data literacy programs. He is the founder and CEO of Bodhi Data and the Senior Vice President of Data and AI Transformation for AgileOne. He served as the Chair of the Advisory Board for The Data Literacy Project and has helped companies and organizations around the world, including the United Nations, build and understand data literacy. Morrow is the author of three other books: Be Data Literate, Be Data Driven and Be Data Analytical all published by Kogan Page.

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Book SynopsisThis book is a practical introduction to digital history. It offers advice on the scoping of a project, evaluation of existing digital history resources, a detailed introduction to how to work with large text resources, how to manage digital data and how to approach data visualisation.Doing digital history covers the entire life-cycle of a digital project, from conception to digital outputs. It assumes no prior knowledge of digital techniques and shows you how much you can do without writing any code. It will give you the skills to use common formats such as XML. A key message of the book is that data preparation is a central part of most digital history projects, but that work becomes much easier and faster with a few essential tools.Table of ContentsAcknowledgementsGlossaryIntroduction1 The context of digital history2 Formulating your research questions3 How a digital project begins4 Working with text 1: unstructured text5 Working with text 2: structured text6 Caring for your digital history project7 Visualising your data8 What next for digital history?Test yourself answersAppendix 1: Getting the dataAppendix 2: Some command line recipesAppendix 3: Regular expressionsReferencesIndex

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Book SynopsisThe third edition of A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) guides readers through learning and mastering the techniques of this approach in clear language. Authors Joseph H. Hair, Jr., G. Tomas M. Hult, Christian Ringle, and Marko Sarstedt use their years of conducting and teaching research to communicate the fundamentals of PLS-SEM in straightforward language to explain the details of this method, with limited emphasis on equations and symbols. A running case study on corporate reputation follows the different steps in this technique so readers can better understand the research applications. Learning objectives, review and critical thinking questions, and key terms help readers cement their knowledge. This edition has been thoroughly updated, featuring the latest version of the popular software package SmartPLS 3. New topics have been added throughout the text, including a thoroughly revised and extended chapter on mediation, recent research on the foundations of PLS-SEM, detailed descriptions of research summarizing the advantages as well as limitations of PLS-SEM, and extended coverage of advanced concepts and methods, such as out-of-sample versus in-sample prediction metrics, higher-order constructs, multigroup analysis, necessary condition analysis, and endogeneity. Table of ContentsPreface About the Authors Chapter 1. An Introduction to Structural Equation Modeling Chapter Preview What Is Structural Equation Modeling? Considerations in Using Structural Equation Modeling Principles of Structural Equation Modeling PLS-SEM, CB-SEM, and Regressions Based on Sum Scores Considerations When Applying PLS-SEM Guidelines for Choosing Between PLS-SEM and CB-SEM Organization of Remaining Chapters Summary Review Questions Critical Thinking Questions Key Terms Suggested Readings Chapter 2. Specifying the Path Model and Examining Data Chapter Preview Stage 1: Specifying the Structural Model Stage 2: Specifying the Measurement Models Stage 3: Data Collection and Examination Case Study Illustration—Specifying the PLS-SEM Model Summary Review Questions Critical Thinking Questions Key Terms Suggested Readings Chapter 3. Path Model Estimation Chapter Preview Stage 4: Model Estimation and the PLS-SEM Algorithm Case Study Illustration—PLS Path Model Estimation (Stage 4) Summary Review Questions Critical Thinking Questions Key Terms Suggested Readings Chapter 4. Assessing PLS-SEM Results—Part I: Evaluation of the Reflective Measurement Models Chapter Preview Overview of Stage 5: Evaluation of Measurement Models Stage 5a: Assessing Results of Reflective Measurement Models Case Study Illustration—Evaluation of the Reflective Measurement Models (Stage 5a) Summary Review Questions Critical Thinking Questions Key Terms Suggested Readings Chapter 5. Assessing PLS-SEM Results—Part II: Evaluation of the Formative Measurement Models Chapter Preview Stage 5b: Assessing Results of Formative Measurement Models Case Study Illustration—Evaluation of the Formative Measurement Models (Stage 5b) Summary Review Questions Critical Thinking Questions Key Terms Suggested Readings Chapter 6. Assessing PLS-SEM Results—Part III: Evaluation of the Structural Model Chapter Preview Stage 6: Structural Model Results Evaluation Case Study Illustration—Evaluation of the Structural Model (Stage 6) Summary Review Questions Critical Thinking Questions Key Terms Suggested Readings Chapter 7. Mediator and Moderator Analysis Chapter Preview Mediation Moderation Case Study Illustration—Moderation Summary Review Questions Critical Thinking Questions Key Terms Suggested Readings Chapter 8. Outlook on Advanced Methods Chapter Preview Importance-Performance Map Analysis Necessary Condition Analysis Higher-Order Constructs Confirmatory Tetrad Analysis Examining Endogeneity Treating Observed and Unobserved Heterogeneity Measurement Model Invariance Consistent PLS-SEM Summary Review Questions Critical Thinking Questions Key Terms Suggested Readings Glossary References Index

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Book SynopsisA revolutionary approach to bringing data and business togetherData is lazy. It sits in files or databases, minding its own business but not accomplishing very much. Data is like someone in their mid-twenties, living with their parents, who won''t get off the couch and make something of their life. Data is also the closest thing we have to truth in our organizationsbut most business leaders today struggle using data to make an impact on what really matters: the success of their businesses.Data Leadership for Everyone is a game-changing book for anyone who believes in the power of data and is ready to create revolutionary change in their organization. Whether you''re a C-suite executive, a manager, or an individual contributor, this book will propel your career by unlocking the mystery of how raw data transforms into real outcomes. In this book, data leadership advocate and transformation coach Anthony J. Algmin reveals his five-step Data Leadership Fram

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Book SynopsisThe modern world is awash with data. The R Project is a statistical environment and programming language that can help to make sense of it all. A huge open-source project, R has become enormously popular because of its power and flexibility. With R you can organise, analyse and visualise data. This clear and methodical book will help you learn how to use R from the ground up, giving you a start in the world of data science. Learning about data is important in many academic and business settings, and R offers a potent and adaptable programming toolbox. The book covers a range of topics, including: importing/exporting data, summarising data, visualising data, managing and manipulating data objects, data analysis (regression, ANOVA and association among others) and programming functions. Regardless of your background or specialty, you'll find this book the perfect primer on data analysis, data visualisation and data management, and a springboard for further exploration.Table of Contents1. A brief introduction to R 2. Basic math 3. Introduction to R objects 4. Making and importing data objects 5. Managing and exporting data objects 6. R object types and their properties 7. Working with data objects 8. Manipulating data objects 9. Summarizing data 10. Tabulation 11. Graphics: basic charts 12. Graphics: adding to plots 13. Graphics: advanced methods 14. Analyze data: statistical analyses 15. Programming tools Appendix Index

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Book Synopsis''Concise, breezy and pragmatic'' Wall Street Journal''Remarkably practical techniques for comprehending and communicating the maths that really matters' Adam GrantUntil very recently, most languages had no words for numbers greater than five anything from six to infinity was known as ''lots''. Understanding numbers is essential in the modern world, but we simply aren't built to understand them.What does 5GB of storage actually mean? (Two months of commutes, without repeating a song.)What's the size of a nucleus compared to a cell? (Imagine a bee in a cathedral.)How much bigger is a billion than a million? (Well, a million seconds is twelve days. A billion seconds isthirty-two years.)Drawing on years of research into making ideas stick, Chip Heath and Karla Starr outline six critical principles that will give anyone the tools to understand and communicate numbers with more transparency and meaning. Offering

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Book SynopsisIn this provocative yet practical guidebook Steve Morlidge demonstrates why the approach and methods of performance reporting that all information professionals have been taught fails, and what we need to do differently to help us make sense of the dynamic, complex and data rich world in which we now live and work. Reporting on performance should not be treated as worthy but dull, requiring no more than routine comparisons of actual against targets. This traditional approach is based on the false premise organisations can be managed as if they were a simple mechanical system operating in a predictable environment. And the methods associated with it, such as variance analyses and data tables that are used to measure and communicate performance, are completely inadequate. Instead, Morlidge argues performance reporting should be reconceived as an act of perception conducted on behalf of the organisation, helping to make sense of the sensory inputs (data) that it has at its disposal. And to do so effectively performance reporters need to learn from and exploit the strengths of our own brains, compensate for its weaknesses and communicate in a way that makes it easy for their audience’s brains to assimilate. Drawing on the latest insights from cognitive science in this book you will learn: • how to bring a dynamic perspective into performance reporting • how to deploy a set of simple tools to help speared the signal from the noise inherent in large data sets and to make sound inferences • how to set goals intelligently • about the grammar of data visualization and how use it to design powerful and simple reports In this way information professionals are uniquely charged with the responsibility for creating the shared consciousness that is a prerequisite for organisations to effectively respond and adapt to their environments.

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Book Synopsis'Terrifying yet funny, surprising yet predictable, simple yet poignant' Chris PackhamA shocking but informative, eye-catching and witty book of maps that illustrate the perilous state of our planet.The maps in this book are often shocking, sometimes amusing, and packed with essential information:· Did you know that just 67 companies worldwide are responsible for 67 per cent of global greenhouse emissions? · Or that keeping a horse has the same carbon footprint as a 23,500-kilometre road trip? · Did you know how many countries use less energy than is consumed globally by downloading porn from the internet?· Do you know how much of the earth's surface has been concreted over?· Or how many trees would we have to plant to make our planet carbon-neutral?Presenting a wealth of innovative scientific research and data in stunning, beautiful infographics, 99 Maps to Save the Planet provides us with instant snapshots of the destruction of our environment. At one glance, we can see the precarious state of our planet - but also realise how easy it would be to improve it Enlightening, a bit frightening, but definitely inspiring, 99 Maps to Save the Planet doesn't provide practical tips on how to save our planet: it just presents the facts. And the facts speak for themselves. Once we know them, what excuse do we have for failing to act?Trade ReviewYou'll never look at the fight for our common home in the same way again after seeing the images in this remarkable book * Big Issue *Impressively imaginative and effectively alarming * Wanderlust *Terrifying yet funny, surprising yet predictable, simple yet poignant -- Chris Packham

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Book SynopsisThis concise introduction covers inverse problems and data assimilation, before exploring their inter-relations. Suitable for both classroom teaching and self-guided study, it is aimed at advanced undergraduates and beginning graduate students in mathematical sciences, together with researchers in science and engineering.Table of ContentsIntroduction; Part I. Inverse Problems: 1. Bayesian inverse problems and well-posedness; 2. The linear-Gaussian setting; 3. Optimization perspective; 4. Gaussian approximation; 5. Monte Carlo sampling and importance sampling; 6. Markov chain Monte Carlo; Exercises for Part I; Part II. Data Assimilation: 7. Filtering and smoothing problems and well-posedness; 8. The Kalman filter and smoother; 9. Optimization for filtering and smoothing: 3DVAR and 4DVAR; 10. The extended and ensemble Kalman filters; 11. Particle filter; 12. Optimal particle filter; Exercises for Part II; Part III. Kalman Inversion: 13. Blending inverse problems and data assimilation; References; Index.

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Book SynopsisThe Spark distributed data processing platform provides an easy-to-implement tool for ingesting, streaming, and processing data from any source. In Spark in Action, Second Edition, you’ll learn to take advantage of Spark’s core features and incredible processing speed, with applications including real-time computation, delayed evaluation, and machine learning. Unlike many Spark books written for data scientists, Spark in Action, Second Edition is designed for data engineers and software engineers who want to master data processing using Spark without having to learn a complex new ecosystem of languages and tools. You’ll instead learn to apply your existing Java and SQL skills to take on practical, real-world challenges. Key Features · Lots of examples based in the Spark Java APIs using real-life dataset and scenarios · Examples based on Spark v2.3 Ingestion through files, databases, and streaming · Building custom ingestion process · Querying distributed datasets with Spark SQL For beginning to intermediate developers and data engineers comfortable programming in Java. No experience with functional programming, Scala, Spark, Hadoop, or big data is required. About the technology Spark is a powerful general-purpose analytics engine that can handle massive amounts of data distributed across clusters with thousands of servers. Optimized to run in memory, this impressive framework can process data up to 100x faster than most Hadoop-based systems. Author BioAn experienced consultant and entrepreneur passionate about all things data, Jean-Georges Perrin was the first IBM Champion in France, an honor he’s now held for ten consecutive years. Jean-Georges has managed many teams of software and data engineers.

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Book SynopsisJan-Willem Middelburg is a Dutch entrepreneur and author with a passion for technology and innovation. He is the CEO and co-founder of Cybiant, a global technology that company that helps to create a more sustainable world through analytics, big data and automation. He is also President and Chief Examiner of the Enterprise Big Data Framework, an independent organization dedicated to upskilling individuals with expertise in Big Data. In partnership with APMG-International, the Enterprise Big Data Framework offers vendor-neutral certifications for individuals.Trade Review"The Enterprise Big Data Framework is relevant for everybody within an organisation engaged in driving maximum benefits from data. There is something for everybody; from the board considering governance and ethical behaviour to individuals within the organisation knowing where they fit and the value they can get from better use of their organisation's data. If you are considering a transformation project, this is an excellent guide for your project team." * Richard Pharro, CEO, The APM Group Limited *"If you are looking for a good guide to empower your knowledge on big data and to find a framework to help you on your big data journey, then this book is for you. From learning what big data is to defining a big data strategy, Jan-Willem has built a book to empower the learner on the topic of big data." * Jordan Morrow, Chief Strategy & Transformation Officer, DataPrime and Author of Be Data Literate *"This book is a master piece for those who are familiar and those who discover the world of data. It provides an "a la carte framework" starting with a (big) data strategy and the supporting aspects such as big data functions, architecture and algorithms. It covers in depth data platforms architectures, its management as well as data governance, data catalogue and all the required security considerations associated to the various data classifications. You will find details of data life cycle management, of various machine learning algorithms and an important chapter covering AI ethics when building and deploying sophisticated algorithms using data. The concepts covered in this book apply to on-premises and in the (public) cloud environments, making this book a must read." * Jean-Michel Coeur, APAC Technology Practice Lead, Data Analytics, Google Cloud *Table of Contents Section - ONE: Introduction to Big Data; Chapter - 01: Introduction to Big Data; Chapter - 02: The Big Data framework; Chapter - 03: Big Data strategy; Chapter - 04: Big Data architecture; Chapter - 05: Big Data algorithms; Chapter - 06: Big Data processes; Chapter - 07: Big Data functions; Chapter - 08: Artificial intelligence; Section - TWO: Enterprise Big Data analysis; Chapter - 09: Introduction to Big Data analysis; Chapter - 10: Defining the business objective; Chapter - 11: Data ingestion – importing and reading data sets; Chapter - 12: Data preparation – cleaning and wrangling data; Chapter - 13: Data analysis – model building; Chapter - 14: Data presentation; Section - THREE: Enterprise Big Data engineering; Chapter - 15: Introduction to Big Data engineering; Chapter - 16: Data modelling; Chapter - 17: Constructing the data lake; Chapter - 18: Building an enterprise Big Data warehouse; Chapter - 19: Design and structure of Big Data pipelines; Chapter - 20: Managing data pipelines; Chapter - 21: Cluster technology; Section - FOUR: enterprise Big Data algorithm design; Chapter - 22: Introduction to Big Data algorithm design; Chapter - 23: Algorithm design – fundamental concepts; Chapter - 24: Statistical machine learning algorithms; Chapter - 25: The data science roadmap; Chapter - 26: Programming languages 26 visualization and simple metrics; Chapter - 27: Advanced machine learning algorithms; Chapter - 28: Advanced machine learning classification algorithms; Chapter - 29: Technical communication and documentation; Section - FIVE: Enterprise Big Data architecture; Chapter - 30: Introduction to the Big Data architecture; Chapter - 31: Strength and resilience – the Big Data platform; Chapter - 32: Design principles for Big Data architecture; Chapter - 33: Big Data infrastructure; Chapter - 34: Big Data platforms; Chapter - 35: The Big Data application provider; Chapter - 36: System orchestration in Big Data

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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 SynopsisThis is the first comprehensive overview of the exciting field of the 'science of science'. With anecdotes and detailed, easy-to-follow explanations of the research, this book is accessible to all scientists, policy makers, and administrators with an interest in the wider scientific enterprise.Trade Review'Wang and Barabási book is a manifesto for the science of science domain. Graduate students (as well as their mentors) owe the authors a debt of gratitude for this impressive synthesis of what is a fast-evolving field of research.' Pierre Azoulay, Massachusetts Institute of Technology'Analyzing quantitative aspects of science with state-of-art tools, Wang and Barabási have written an insightful and comprehensive book that will become a must-read for all scholars interested in science.' Yu Xie, Princeton University'In their engaging book, Wang and Barabási take a fresh look at the science of science. They convincingly argue that in the age of big data and AI applying the scientific method to science itself not only helps understand how science works but may even enhance it. We are compelled to consider the determinants of individual careers and what this means in the age of large-scale scientific collaborations. These and other questions around the meaning of scientific impact, in academia and beyond, make the book highly relevant to scientists, academic administrators and funders alike. By the time the final, forward-looking chapter ends we are hooked on all the correlations and predictions, and so it is only fitting that we are invited to join in, to help shape the field which is likely to be driven by a human-machine collaboration.' Magdalena Skipper, Nature'Overall, I found this book very stimulating. It made me wonder whether in-depth metrics analyses of 'only' the subjective narratives of authors, such as the references list they select, actually creates a foundation on which to form judgement rather than opinion? Namely, what fraction of these publications analysed for their metrics were actually underpinned by their data? As well as provoking thought, this book offers a feast of references, 424 in all. There are such further enticing reads as reference 396, Life3.0: Being Human in the Age of Artificial Intelligence. To conclude, I recommend this book for your library, and maybe even take it for your summer beach reading.' John R. Helliwell, Journal of Applied Crystallography'… a text that should appeal to practicing scientists curious about the structure of the whole scientific enterprise, academic administrators and policy makers interested in evidence-based decision-making, and researchers interested in contributing further to the "science of science." There is no better, handier, and more readable work to appeal to such audiences … Highly recommended.' M. Oromaner, Choice ConnectTable of ContentsIntroduction; Part I. The Science of Career: 1. Productivity of a scientist; 2. The H Index; 3. The Matthew Effect; 4. Age and Scientific Achievement; 5. Random Impact Rule; 6. The Q Factor; 7. Hot Streaks; Part II. The Science of Collaboration: 8. The increasing dominance of teams in science; 9. The Invisible College; 10. Coauthorship Networks; 11. Team Assembly; 12. Small and large teams; 13. Scientific Credit; 14. Credit Allocation; Part III. The Science of Impact: 15. Big Science; 16. Citation Disparity; 17. High Impact Papers; 18. Scientific Impact; 19. The Time Dimension of Science; 20. Ultimate Impact; Part IV. Outlook: 21. Can Science be Accelerated?; 22. Artificial Intelligence; 23. Bias and Causality in Science; Part V. Last thought; All the Science of Science: Appendix A1 Modeling team assembly; Appendix A2 Modeling Citations; References; Index.

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Book SynopsisThis book offers a hands-on introduction and teaching resource for students, users, and teachers of Qualitative Comparative Analysis (QCA; Ragin, 1987, 2000, 2008b). Given its superior ability to model certain aspects of complexity, QCA has made inroadsinto virtually every social science discipline and beyond. Software solutions for QCA have also been developing at a fast pace. This book seeks to reduce the time and effort required when we first encounter the logic of not just a new method but also newsoftware. It offers a genuinely simple, intuitive, and hands-on resource for implementing the state-of-the-art protocol of QCA using R, the most advanced software environment for QCA. Our book has an applied and practical focus--Trade ReviewIn a relatively short, clear, and well-written textbook, the authors cover all the essentials of QCA. It includes all the current practices and developments that one needs to do a complete QCA analysis. I am using it in my QCA class and I think the students will like it. Gary Goertz, Kroc Institute for International Peace Studies, University of Notre DameThis book is simply a must for anyone aiming at exploiting 'hands on' the distinctive analytic leverage of QCA, via the vast possibilities of the R environment. Benoît Rihoux, University of Louvain and COMPASSS global network (compasss.org)Every now and then, a book comes along of which you think 'why wasn't this around when I was learning this stuff?'. Because sometimes, you just want to learn from the best. The authors have written an excellent guide for both first-time users and experienced QCA scholars: robust, powerful, and a must-read. Bart Cambré, Antwerp Management SchoolEasily accessible and loaded with a wealth of examples, this important book by Oana, Schneider, and Thomann provides an expert guide to the set-analytic perspective. The integration of concepts and research strategies with the R software package makes their approach particularly successful. I expect it will quickly become the standard introduction to QCA. Peer C. Fiss, University of Southern CaliforniaSocial scientists interested in QCA should not miss this book. Oana, Schneider, and Thomann offer an introduction to QCA that is friendly, up to date, and technically advanced. The volume covers set relations, calibration of “crisp” and “fuzzy” sets, necessary conditions, sufficient configurations, temporality, advanced diagnostics, and post-QCA tools. Each chapter contains intuitive examples, advanced tips, and detailed implementation instructions in R. Aníbal Pérez-Liñán , University of Notre DameTable of ContentsPart I. Getting started: Introduction: QCA in a nutshell; Part II. Before the analytic moment: 2. Calibrating and combining sets; Part III. During the analytic moment: 3. Necessary conditions; 4. Sufficient conditions; Part IV. After the analytic moment: 5. Rounding up solid a QCA; 6. Post-QCA tools; 7. Summary and outlook.

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Book SynopsisMike Grigsby, based in Orlando, Florida, has more than 30 years' experience in the field of marketing analytics. He was formerly vice president of customer insights and advanced analytics at Brierley and Partners and of strategic business analysis and advanced analytics at Targetbase and has also held leadership positions at Hewlett-Packard and Gap. Previously an adjunct professor at the University of Texas at Dallas, he taught analytics at both graduate and undergraduate levels. He is the author of Advanced Customer Analytics, also published by Kogan Page.Trade Review"In Marketing Analytics, Mike Grigsby takes passionate marketing strategists on a practical, real-life journey for solving common marketing challenges. By combining the concepts and knowledge areas of statistics, marketing strategy and consumer behaviour, Grigsby recommends scientific and innovative solutions to common marketing problems in the current business environment. I highly recommend reading this book as it adds a completely new dimension to marketing science." * Kristina Domazetoska, Project Manager and Implementation Consultant at Insala – Talent Development and Mentoring Solutions *"Grigsby's book is the right blend of theory applied to the real-world large-scale data problems of marketing. It's exactly the book I wish I'd had when I started out in this field." * Jeff Weiner, Senior Director, Analytics, One10 *Table of Contents Section - 00: Introduction; Section - PART ONE: How can marketing analytics help you?; Chapter - 01: Overview of statistics; Chapter - 02: Consumer behaviour and marketing strategy; Chapter - 03: What is an insight?; Section - PART TWO: Dependent variable techniques; Chapter - 04: Modelling demand and elasticity; Chapter - 05: Polynomial distributed lags; Chapter - 06: Using Poisson regression; Chapter - 07: Logistic regression and market basket analysis; Chapter - 08: Survival modelling and lifetime value; Chapter - 09: Panel regression and same store sales; Chapter - 10: Introduction to forecasting; Section - PART THREE: Interrelationship techniques; Chapter - 11: Simultaneous equations; Chapter - 12: Principal components and factor analysis; Chapter - 13: Segmentation overview; Chapter - 14: Tools of segmentation; Section - PART FOUR: Focus on media and loyalty; Chapter - 15: Modelling marcom value; Chapter - 16: Media mix modelling; Chapter - 17: Overview of loyalty; Chapter - 18: Loyalty with SEM; Chapter - 19: The customer loyalty journey; Section - PART FIVE: More important topics for everyday marketing; Chapter - 20: Statistical testing; Chapter - 21: Introduction to Big Data; Chapter - 22: Conclusion - The finale; Chapter - 23: References; Chapter - 24: Further reading;

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Book SynopsisTHE INSTANT NEW YORK TIMES BESTSELLER 'Chart a child's path with less stress and more optimization for healthy habits and future success' Time From age 5 to 12, parenting decisions get more complicated and have lasting consequences. What's the right kind of school? Should they play a sport? When's the right time for a phone? Making these decisions is less about finding the specific answer and more about taking the right approach. Along with these bigger questions, Oster investigates how to navigate the complexity of day-to-day family logistics. The Family Firm is a smart and winning guide to how to think more clearly - and with less ambient stress - about the key decisions of these early years.Trade ReviewOster is a self-described data nerd, a delightful contrarian who dared question the status quo, shush the shamers and tell parents what made sense. * The New York Times Book Review *A targeted mini-MBA program designed to help moms and dads establish best practices for day-to-day operations ... Because this is an Oster book, there's data scattered everywhere - on the development of reading skills by age ... It's all presented in the breezy, skeptical style that's made Oster's work a must-read for parents who don't have the time to investigate Finnish studies about integrating extracurriculars into the school day. * Washington Post *A guide ... to chart a child's path with less stress and more optimization for healthy habits and future success. * TIME Magazine *Oster's prose flows well (as usual) lightly sprinkled with the dry wit that suffuses her other books. * Salon *Oster offers a plethora of rational guidance for parents of kids between pre-K and middle school in this eminently practical guide. * Publishers Weekly *Merging a business approach with her trademark empowering voice, Emily dispenses the stress-less advice you actually want. -- Audrey Goodson Kingo * Working Mother *With Oster's help, rather than fear this next stage of parenting, readers can embrace (and even enjoy) the challenge. * Booklist *Emily Oster dives into the data on parenting issues, cuts through the clutter, and gives families the bottom line to help them make better decisions. Her books on pregnancy and toddlers skyrocketed her to parenting-world fame, and now she's back, crunching the numbers on topics that keep parents with school-age kids up at night. * Good Morning America *Oster draws on her experience as a business school professor to suggest that economic reasoning - the art of making decision-making given constraints - can tell us a lot about how to make some of these hard decisions a little better ... Some careful, economics-inspired thinking can help reduce the anxiety, tension, and stress ... For that alone, The Family Firm is worth picking up -- Charles Fain Lehman * The Washington Free Beacon *Praise for Cribsheet: * - *She has crunched all the statistics on breastfeeding, potty training, working mothers and playgroups and discovered there is no optimal set of choices that will produce the perfect child. Most parents say they want happy, well-adjusted, robust kids and there are myriad ways to achieve those results. She's right -- Alice Thomson * The Times *It couldn't be more relevant ... steers clear of recommendations and cast-iron guarantees, instead promising to arm parents with information to make the decisions that are right for them * Daily Telegraph *Parenting can be fraught. Cribsheet aims to help parents do better. * Economist *A huge relief from the scare stories ... Cribsheet is not another call for the end of helicopter parenting or snowplow parenting or whatever kind of parenting is lighting up social media today, and it's not a call to overthrow medical wisdom; it's a call for parenting with context, and it's freeing * Washington Post *Both refreshing and useful. With so many parenting theories driving us all a bit batty, this is the type of book that we need to help calm things down. * LA Times *The Guilt-Free, Data-Driven Guide to Parenting ... uses science and stats to cut through the confusion of raising a family ... Smart, relatable, and funny * Bloomberg *Praise for Emily Oster: * - *A revelation -- Pandora SykesI am so grateful for her work -- Amy SchumerIn my household, [Emily Oster] is the all-knowing Aunt we have never met. Parenting would be a lot more stressful without these books. -- Adam Ozimek * Forbes *

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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 SynopsisUsing diverse real-world examples, this book explores the use of R for data analysis, with extensive use of graphical presentation. It assists scientists in the analysis of their own data, demonstrating how to check the underlying assumptions, and gives students in statistical theory exposure to practical data analysis.

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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 SynopsisFoundations of Data Science with Python introduces readers to the fundamentals of data science, including data manipulation and visualization, probability, statistics, and dimensionality reduction. This book is targeted toward engineers and scientists, but it should be readily understandable to anyone who knows basic calculus and the essentials of computer programming. It uses a computational-first approach to data science: the reader will learn how to use Python and the associated data-science libraries to visualize, transform, and model data, as well as how to conduct statistical tests using real data sets. Rather than relying on obscure formulas that only apply to very specific statistical tests, this book teaches readers how to perform statistical tests via resampling; this is a simple and general approach to conducting statistical tests using simulations that draw samples from the data being analyzed. The statistical techniques and tools are explained and demonstrated

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