Data science and analysis Books

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, helping to utilize data and AI in the total talent management space. 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 four books: Be Data Literate, Be Data Driven, Be Data Analytical, and Business 101 for the Data Professional all published by Kogan Page. He is based near Salt Lake City, Utah.

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Book SynopsisTom Ollerton is the founder of Automated Creative, the creative effectiveness platform who have optimised over 6 billion impressions globally for companies such as MARS, P&G, Diageo and McDonalds. Based in London, UK, he is the host of the Shiny New Object podcast and Advertisers Watching Ads series. He has appeared at MAD//Fest for the last 3 years and was a Juror for the World Federation of Advertisers Marketer of the Year Award 2023. He has spoken at conferences such as Performance Marketing World Unlocked, AdWeek and Social Media Week and is a popular podcast guest.

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Book SynopsisKinsey Li is an accomplished HR leader with 10 years of experience and a proven track record of delivering complex transformation projects both in industry and as a consultant. Based in London, UK, she is Associate Director, HR Analytics and Insights at Ernst and Young (EY). She holds and MBA, a postgraduate certificate in business IT, a postgraduate certificate in business and a BA in commerce.

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Book SynopsisWhat is Big Data, and why should you care?Big data knows where you''ve been and who your friends are. It knows what you like and what makes you angry. It can predict what you''ll buy, where you''ll be the victim of crime and when you''ll have a heart attack. Big data knows you better than you know yourself, or so it claims.But how well do you know big data?You''ve probably seen the phrase in newspaper headlines, at work in a marketing meeting, or on a fitness-tracking gadget. But can you understand it without being a Silicon Valley nerd who writes computer programs for fun?Yes. Yes, you can.Timandra Harkness writes comedy, not computer code. The only programmes she makes are on the radio. If you can read a newspaper you can read this book.Starting with the basics what IS data? And what makes it big? Timandra takes you on a whirlwind tour of how people are using big data today: from science to smart cities, business to politics, self-quantification to the Internet of ThiTrade ReviewA superb explanation of how we got to today. * Evening Standard *Harkness has the perfect combination of wit, charm and statistical insight to crunch big data. It's the book about stats, life and big data we've all been waiting for. -- Matt Parker, Stand-up MathematicianHarkness raises some very big questions indeed, not just about the grandiose claims of the big data evangelists, but also about how in the age of universal surveillance we can defend the concept of privacy. * The Herald *A wonderful collection of fascinating data stories, all told in Timandra's smart and chatty style. But this book also asks the important questions. If big data brings new opportunities, just what are the implications? -- Hannah Fry, author and mathematicianA brilliant guide to our brave new world. -- Brian CleggThis book is a great read – full of interesting stories and fun interviews. But it is not just another credulous tale of technological wonders – Harkness is suitably sceptical of the hype about data analytics, and serious about the challenges is brings. -- David Spiegelhalter, author and mathematicianTable of ContentsIntroduction: What is it? Where did it come from? 1: What Is Data? And what makes it big? 2: Death and Taxes. And Babies. 3: Thinking Machines What Has Big Data Done For Us? 4: Big Business 5: Big Science 6: Big Society 7: Data Driven Democracy Big Ideas? 8: Big Brother 9: Who Do We Think You Are? 10: Are You A Data Point Or A Human Being? Appendix - things you can do to keep your data private Acknowledgements

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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 NEW BOOK FROM THE BESTSELLING AUTHOR OF EVERYBODY LIES 'Don’t Trust Your Gut is a tour de force — an intoxicating blend of analysis, humor, and humanity' DANIEL H. PINK 'Seth Stephens-Davidowitz is an expert on data-driven thinking, and this engaging book is full of surprising, useful insights for using the information at your fingertips to make better decisions' ADAM GRANT Big decisions are hard. We might consult friends and family, read advice online or turn to self-help books for guidance, but in the end we usually just do what feels right. But what if our gut is wrong? As economist and former Google data scientist Seth Stephens-Davidowitz argues, our gut is actually not that reliable – and data can prove this. In Don’t Trust Your Gut, he unearths the startling conclusions that the right data can teach us about who we are and what will make our lives better. Over the past decade, scholars have mined enormous datasets to find remarkable new approaches to life’s biggest self-help puzzles, from the boring careers that produce the most wealth, to old-school, data-backed relationship advice. While we often think we know how to better ourselves, the numbers, it turns out, disagree. Telling fascinating stories through the latest big data research, Stephens-Davidowitz reveals just how wrong we really are when it comes to improving our lives, and offers a new way of tackling our most consequential choices.Trade ReviewSeth Stephens-Davidowitz is more than a data scientist. He is a prophet for how to use the data revolution to reimagine your life. Don’t Trust Your Gut is a tour de force – an intoxicating blend of analysis, humor, and humanity -- Daniel H. Pink, #1 New York Times bestselling authorThis must-read book is packed with helpful discoveries you can use to improve your life, and each is grounded in data. It’s also a page-turner – Seth Stephens-Davidowitz is a smart, witty writer with an extraordinary ability to make charts and statistics engrossing -- Katherine Milkman, author of HOW TO CHANGEThere are two ways to look at big data: as a threat to your intuition or as a resource to test your intuition. Seth Stephens-Davidowitz is an expert on data-driven thinking, and this engaging book is full of surprising, useful insights for using the information at your fingertips to make better decisions -- Adam Grant, #1 New York Times bestselling author of THINK AGAINHow can you look your best? Who should you marry? What makes a good parent? Are you too old to start a business? How can you get rich? What would make you happy? Would you read a book that helps you answer even one of these questions? Seth Stephens-Davidowitz delivers: a cross between Freakonomics and How to Win Friends and Influence People, Don’t Trust Your Gut is your guide for reliable data-driven hacks to get an edge in life -- Ian Bremmer, president and founder of Eurasia GroupSeth Stephens-Davidowitz’s book is a brilliant and clever look into the critical importance of making data-informed decisions for a data-first organization. His truly game-changing approach provided a pivotal moment for me as a leader and his insightful yet humorous writing style is sure to do the same for many others -- Mindy Grossman, CEO of Weight WatchersI love the way Seth Stephens-Davidowitz explains how we can better live our lives by exploiting the small advantages in life. On the basketball court, I made a career out of finding these types of minor advantages, and I’ve found that most successful individuals in life value the accumulation of small advantages. In the end, they add up to significant life benefits -- Shane Battier, two-time NBA Champion basketball player for the Miami HeatStephens-Davidowitz maintains a breezy, conversational style that lends a lighthearted touch to all the wonkery. Whether confirming or debunking conventional wisdom, the smooth presentation and quantitative detail bring a welcome analytical rigor to the self-help genre * Publishers Weekly *

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Book SynopsisCrime research has grown substantially over the past decade, with a rise in evidence-informed approaches to criminal justice. The fuel that has driven this growth is data and one of its most pressing challenges is the lack of research on its use and interpretation. This accessible book closes that gap for researchers, practitioners and students.

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Book SynopsisIn order to assist a hospital in managing its resources and patients, modelling the length of stay is highly important. Recent health scholarship and practice has largely remained empirical, dwelling on primary data. This is critically important, first, because health planners generally rely on data to establish trends and patterns of disease burden at national or regional level. Secondly, epidemiologists depend on data to investigate possible risk factors of the disease. Yet the use of routine or secondary data has, in recent years, proved increasingly significant in such endeavours. Various units within the health systems collected such data primarily as part of the process for surveillance, monitoring and evaluation. Such data is sometimes periodically supplemented by population-based sample survey datasets. Thirdly, coupled with statistical tools, public health professionals are able to analyze health data and breathe life into what may turn out to be meaningless data. The main focus of this book is to present and showcase advanced modelling of routine or secondary survey data. Studies demonstrate that statistical literacy and knowledge are needed to understand health research outputs. The advent of user-friendly statistical packages combined with computing power and widespread availability of public health data resulted in more reported epidemiological studies in literature. However, analysis of secondary data, has some unique challenges. These are most widely reported health literature, so far has failed to recognize resulting in inappropriate analysis, and erroneous conclusions. This book presents the application of advanced statistical techniques to real examples emanating from routine or secondary survey data. These are essentially datasets in which the two editors have been involved, demonstrating how to tackle these challenges. Some of these challenges are: the complex sampling design of the surveys, the hierarchical nature of the data, the dependence of data at the sampled cluster and missing data among many more challenges. Using data from the Health Management Information System (HMIS), and Demographic and Health Survey (DHS), we provide various approaches and techniques of dealing with data complexity, how to handle correlated or clustered data. Each chapter presents an example code, which can be used to analyze similar data in R, Stata or SPSS. To make the book more concise, we have provided the codes on the book's website. The book considers four main topics in the field of health sciences research: (i) structural equation modeling; (ii) spatial and spatio-temporal modeling; (iii) correlated or clustered copula modeling; and (iv) survival analysis. The book has potential to impact methodologists, including students undertaking Master's or Doctoral level programmes as well as other researchers seeking some related reference on quantitative analysis in public health or health sciences or other areas where data of similar nature would be applicable. Further the book can be a resource to public health professionals interested in quantitative approaches to answer questions of epidemiological nature. Each chapter starts with a motivating background, review of statistical methods, analysis and results, ending discussion and possible recommendations.Table of ContentsPrefaceAnalysis and Modelling of Complex Secondary Data: An Overview of Methodological Issues and ChallengesA Mixed Discrete-Time Survival Analysis of Length of Hospitalization: Applications to Malaria Admissions among Peadiatric Children in MalawiBivariate Model of Health Seeking Behaviour among Women for Their Under-Five Children with FeverMover-Stayer Model on Future Contraceptive Use among Married Women in MalawiInvestigating Causal and Mediating Risk Factors for Stunting in under Five Children in Malawi Using Structural Equation Modelling TechniquesLinking Food Insecurity to Quality of Life Using Structural Equation ModelsA Zero-Truncated Negative Binomial Regression Model for Dietary Diversity in Namibian Under-5 ChildrenA Copula Approach to Sample Selection Modelling of Treatment Adherence and Viral Suppression among HIV Patients on Antiretroviral Therapy (ART) in NamibiaCopula-Linked Generalized Joint Regression Model for Water, Sanitation and Hygiene (WASH) Coverage in NamibiaBivariate Copula-Based Regression to Model Timing and Frequency of Antenatal Care UtilizationMultiscale Spatial Modelling of Diabetes and Hypertension in NamibiaModels for Analyzing Spatial Patterns in Risk of Urban Malaria: A Case Study of Blantyre, MalawiSpatio-Temporal Modelling of Malaria Risk in Malawi: An Application to Health Management Information System DataModelling Spatial and Spatial-Temporal Patterns of TB and HIV Mortality in NamibiaAttrition of Women Initiating Antiretroviral Therapy (ART) under Option B+: Cox Proportional Hazards, Competing Risks and Multistate Survival ModelsEpilogueAbout the ContributorsIndex.

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Book SynopsisThe last decade has witnessed various technological advances in life sciences, especially high throughput technologies. These technologies provide a way to perform parallel scientific studies in a very short period of time with low cost. High throughput techniques, mainly, next generation sequencing, microarray and mass spectrometry, have strengthened the omics vision in the last decades (study of complete system) and now resulted in well-developed branches of omics i.e., genomics, transcriptomics, proteomics and metabolomics, which deal with almost every level of central dogma of life. Practice of high throughput techniques throughout the world with different aims and objectives resulted in a voluminous data, which required computational applications, i.e., database, algorithm and software to store, process and get biological interpretation from primary raw data. Researchers from different fields are looking to analyze these raw data for different purposes, but lacking of proper information and knowledge in proper documented form creates different kinds of hurdles and raises the challenges. This book contains thirteen chapters that deal with different computational biology/bioinformatics resources and concepts which are already in practice by the scientific community or can be utilized to handle various aspects of different classes of omics data. It includes different computational concepts, algorithm, resources and recent trends belonging to the four major branches of omics (i.e., genomics, transcriptomics, proteomics and metabolomics), including integrative omics. It will help all scholars who are working in any branch of computational omics and bioinformatics field as well as those who would like to perform research at a systemic biology through computational approaches.Table of ContentsFor more information, please visit our website at:https://novapublishers.com/shop/recent-trends-in-computational-omics-concepts-and-methodology/

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Book SynopsisNOW IN FULL COLOR! Written by sought-after speaker, designer, and researcher Stephanie D. H. Evergreen, Effective Data Visualization shows readers how to create Excel charts and graphs that best communicate their data findings. This comprehensive how-to guide functions as a set of blueprints—supported by both research and the author’s extensive experience with clients in industries all over the world—for conveying data in an impactful way. Delivered in Evergreen’s humorous and approachable style, the book covers the spectrum of graph types available beyond the default options, how to determine which one most appropriately fits specific data stories, and easy steps for building the chosen graph in Excel. Now in full color with new examples throughout, the Second Edition includes a revamped chapter on qualitative data, nine new quantitative graph types, new shortcuts in Excel, and an entirely new chapter on Sharing Your Data With the World, which provides advice on using dashboards. New from Stephanie Evergreen! The Data Visualization Sketchbook provides advice on getting started with sketching and offers tips, guidance, and completed sample sketches for a number of reporting formats. Bundle Effective Data Visualization, 2e, and The Data Visualization Sketchbook, using ISBN 978-1-5443-7178-8! Table of ContentsPREFACE ACKNOWLEDGMENTS ABOUT THE AUTHOR Chapter 1. Our Backbone: Why We Visualize Why We Visualize When Visualization Is Harmful Which Chart Type Is Best? Tell a Story With Data How to Use This Book Exercises Resources References Chapter 2. When a Single Number Is Important: Showing Mean, Frequency, and Measures of Variability What Stories Can Be Told With a Single Number? How Can I Visualize a Single Number? How Can I Show Measures of Variability? Exercises Resources References Chapter 3. How Two or More Numbers Are Alike or Different: Visualizing Comparisons What Stories Can Be Told About How Two or More Numbers Are Alike or Different? How Can I Visualize How Two or More Numbers Are Alike or Different? Exercises Resources References Chapter 4. How We Are Better or Worse Than a Benchmark: Displaying Relative Performance What Stories Can Be Told About How We Are Better or Worse Than a Benchmark? How Can I Visualize How We Are Better or Worse Than a Benchmark? Exercises Resources References Chapter 5. What the Survey Says: Showing Likert, Ranking, Check-All-That-Apply, and More What Stories Can Be Told About What the Survey Says? How Can I Visualize What the Survey Says? Ranking Branching Visualizing Not Applicable or Missing Data Exercises Resources References Chapter 6. When There Are Parts of a Whole: Visualizing Beyond the Pie Chart What Stories Can Be Told When There Are Parts of a Whole? How Can I Visualize the Parts of a Whole? Exercises Resources References Chapter 7. How This Thing Changes When That Thing Does: Communicating Correlation and Regression What Stories Can Be Told About How This Thing Changes When That Thing Does? How Can I Visualize How This Thing Changes When That Thing Does? Exercises Resources References Chapter 8. When the Words Have the Meaning: Visualizing Qualitative Data What Stories Can Be Told When the Words Have the Meaning? How Can I Visualize When the Words Have the Meaning? Pure Qualitative: Highlight a Word Pure Qualitative: Thematic Analysis Some Quantification: Highlight a Word Some Quantification: Thematic Analysis Exercises Resources References Chapter 9. How Things Changed Over Time: Depicting Trends What Stories Can Be Told About How Things Changed Over Time? How Can I Visualize How Things Changed Over Time? Exercises Resources References Chapter 10. Reporting Out: Sharing Your Data With the World Static Visuals Interactive Dashboards Exercises Resources References Chapter 11. It’s About More Than the Buttons Dot Plots Generate Healthcare Pioneers Clearly Labeled Line Graphs Streamline Decisions at a Fortune 500 Diverging Stacked Bars Make for Community Leaders in the Midwest Icons Support Informed Policymaking Building a Culture of Effective Data Visualization Exercises Resources References INDEX

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Book Synopsis"This comprehensive, practical, user-friendly book provides a wealth of data analysis strategies that are essential for any qualitative research. It is a must-have tool book for moving from data analysis to writing for publication!" –Guofang Li, University of British Columbia, Canada Miles, Huberman, and Saldaña’s Qualitative Data Analysis: A Methods Sourcebook is the authoritative text for analyzing and displaying qualitative research data. The Fourth Edition maintains the analytic rigor of previous editions while showcasing a variety of new visual display models for qualitative inquiry. Graphics are added to the now-classic matrix and network illustrations of the original co-authors. Five chapters have been substantially revised, and the appendix’s annotated bibliography includes new titles in research methods. Graduate students and established scholars from all disciplines will find this resource an innovative compendium of ideas for the representation and presentation of qualitative data. As the authors demonstrate, when researchers "think display," their analyses of social life capture the complex and vivid processes of the people and institutions studied.Table of ContentsPreface to the Fourth Edition Part 1: The Substantive Start Chapter 1: Introduction The Purpose of This Book The Nature of This Book Our Orientation An Approach to Qualitative Data Analysis The Nature of Qualitative Data Analysis Our View of Qualitative Data Analysis Suggestions for Readers Closure and Transition Chapter 2: Research Design and Data Management Introduction Loose Versus Tight Research Designs Displaying the Conceptual Framework Methodologies (Genres) of Qualitative Research Formulating Research Questions Defining the Case Sampling: Bounding the Collection of Data Instrumentation Linking Qualitative and Quantitative Data Data Management Issues Bearing on Analysis Closure and Transition Chapter 3: Ethical Issues in Analysis Introduction Agreements with Study Participants Ethical Issues Conflicts, Dilemmas, and Trade-Offs Closure and Transition Chapter 4: Fundamentals of Qualitative Data Analysis Introduction First Cycle Codes and Coding Second Cycle Coding - Pattern Codes Jottings Analytic Memoing Within-Case and Cross-Case Analysis Closure and Transition Part Two: Displaying the Data Chapter 5: Designing Matrix, Network, and Graphic Displays Introduction Display Format Options Matrices Networks Graphics Timing of Display Design Entering Matrix, Network, and Graphic Data Making Inferences and Drawing Conclusions from Matrix, Network, and Graphic Displays The Methods Profiles Closure and Transition Chapter 6: Methods of Exploring Introduction Exploring Fieldwork in Progress Exploring Variables Exploring Reports in Progress Closure and Transition Chapter 7: Methods of Describing Introduction Describing Participants Describing Validity Describing Variability Describing Actions Closure and Transition Chapter 8: Methods of Ordering Introduction Ordering by Time Ordering Processes Ordering by Cases Closure and Transition Chapter 9: Methods of Explaining Introduction Antecedent Conditions, Mediating Variables, and Outcomes On Causation and Explanation Explaining Interrelationship Explaining Change Explaining Causation Closure and Transition Chapter 10: Methods of Predicting Introduction Methods of Predicting Closure and Transition Part 3: Making Good Sense Chapter 11: Drawing and Verifying Conclusions Introduction Tactics for Generating Meaning Tactics for Testing or Confirming Findings Standards for the Quality of Conclusions Analytic Documentation Closure and Transition Chapter 12: Writing About Qualitative Research Introduction Audiences and Effects Voices and Styles Writing Examples and Recommendations Traditional Presentation Modes Progressive Presentation Modes On Theses and Disseratations Chapter 13: Closure Qualitative Analysis at a Glance Reflections Final Advice Appendix: An Annotated Bibliography of Qualitative Research Methods Resources Index

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Book SynopsisMeasurement connects theoretical concepts to what is observable in the empirical world, and is fundamental to all social and behavioral research. In this volume, J. Micah Roos and Shawn Bauldry introduce a popular approach to measurement: Confirmatory Factor Analysis (CFA). As the authors explain, CFA is a theoretically informed statistical framework for linking multiple observed variables to latent variables that are not directly measurable. The authors begin by defining terms, introducing notation, and illustrating a wide variety of measurement models with different relationships between latent and observed variables. They proceed to a thorough treatment of model estimation, followed by a discussion of model fit. Most of the volume focuses on measures that approximate continuous variables, but the authors also devote a chapter to categorical indicators. Each chapter develops a different example (sometimes two) covering topics as diverse as racist attitudes, theological conservatism, leadership qualities, psychological distress, self-efficacy, beliefs about democracy, and Christian nationalism drawn mainly from national surveys. Data to replicate the examples are available on a companion website, along with code for R, Stata, and Mplus.Trade ReviewConfirmatory Factor Analysis is well written and easy to read, The book covers the essentials necessary for understanding and using CFA. It is appropriate for graduate students and professors new to this analysis approach. -- Jerry J. VaskeThe authors provide a masterful and fluid overview of confirmatory factor analysis that will guide readers to the best practices whether conducting their own research or evaluating the research of others. -- John HoffmannThis is a well-written and comprehensive text. -- Michael D. BidermanRoos and Bauldry lucidly set out foundations of confirmatory factor analysis (CFA) as applied in the assessment and construction of scales. Beginning with model specification, they discuss identification, estimation, and assessment of CFA models, before developing extensions to assessing measurement invariance and categorical (rather than quantitative) indicators. -- Peter V. MarsdenTable of ContentsChapter 1: Introduction Chapter 2: Model Specification Chapter 3: Identification and Estimation Chapter 4: Model Evaluation and Respecification Chapter 5: Measurement Invariance Chapter 6: Categorical Indicators Chapter 7: Conclusion Appendix: Reliability of Scales Glossary Bibliography

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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 SynopsisSpatial Data Science will show GIS scientists and practitioners how to add and use new analytical methods from data science in their existing GIS platforms. By explaining how the spatial domain can provide many of the building blocks, it''s critical for transforming data into information, knowledge, and solutions. This book is for those using or studying GIS and the computer scientists, engineers, statisticians, and information and library scientists leading the development and deployment of data science.

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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 SynopsisThis invaluable addition to any data scientist’s library shows you how to apply the R programming language and useful statistical techniques to everyday business situations as well as how to effectively present results to audiences of all levels. To answer the ever-increasing demand for machine learning and analysis, this new edition boasts additional R tools, modeling techniques, and more. Practical Data Science with R, Second Edition takes a practice oriented approach to explaining basic principles in the ever-expanding field of data science. You’ll jump right to real-world use cases as you apply the R programming language and statistical analysis techniques to carefully explained examples based in marketing, business intelligence, and decision support. Key features • Data science and statistical analysis for the business professional • Numerous instantly familiar real-world use cases • Keys to effective data presentations • Modeling and analysis techniques like boosting, regularized regression, and quadratic discriminant analysis Audience While some familiarity with basic statistics and R is assumed, this book is accessible to readers with or without a background in data science. About the technology Business analysts and developers are increasingly collecting, curating, analyzing, and reporting on crucial business data. The R language and its associated tools provide a straightforward way to tackle day-to-day Nina Zumel and John Mount are co-founders of Win-Vector LLC, a San Francisco-based data science consulting firm. Both hold PhDs from Carnegie Mellon and blog on statistics, probability, and computer science at win-vector.com.

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Book SynopsisMona Khalil is a Data Science Manager at Greenhouse Software. Mona holds a degree in psychology from Fordham University and statistics at Baruch College, as well as having a decade of experience working with analytics and data science. Mona has worked with cross-functional teams in a variety of industries, including government, education, and HR technology.

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Book SynopsisA year of HBR's essential thinking on tech—all in one place.Easy-to-use AI tools, contactless commerce, crypto for business, the mature metaverse—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 2023: 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 thinking on fast-moving issues—blockchain, cybersecurity, AI, and more—each book provides the foundational introduction and practical case studies your organization needs to compete today and collects the best research, interviews, and analysis to get it ready for tomorrow.You can't afford to ignore how these issues will transform the landscape of business and society. The Insights You Need series will help you grasp these critical ideas—and prepare you and your company for the future.

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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 SynopsisDigital transformation and data projects are not new and yet, for many, they are a challenge. Driving Data Projects is a compelling guide that empowers data teams and professionals to navigate the complexities of data projects, fostering a more data-informed culture within their organizations.With practical insights and step-by-step methodologies, this guide provides a clear path how to drive data projects effectively in any organization, regardless of its sector or maturity level whilst also demonstrating how to overcome the overwhelming feelings of where to start and how to not lose momentum. This book offers the keys to identifying opportunities for driving data projects and how to overcome challenges to drive successful data initiatives.Driving Data Projects is highly practical and provides reflections, worksheets, checklists, activities, and tools making it accessible to students new to driving data proj

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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 SynopsisData has dramatically changed how our world works. Understanding and using data is now one of the most transferable and desirable skills. Whether you're an entrepreneur wanting to boost your business, a jobseeker looking for that employable edge, or simply hoping to make the most of your current career, Confident Data Skills is here to help. This updated second edition takes you through the basics of data: from data mining and preparing and analysing your data, to visualizing and communicating your insights. It now contains exciting new content on neural networks and deep learning. Featuring in-depth international case studies from companies including Amazon, LinkedIn and Mike's Hard Lemonade Co, as well as easy-to understand language and inspiring advice and guidance, Confident Data Skills will help you use your new-found data skills to give your career that cutting-edge boost. About the Confident series... From coding and web design to data, digital content and cyber security, the Confident books are the perfect beginner's resource for enhancing your professional life, whatever your career path.Trade Review"The most comprehensive book I have seen for those wanting to get into data science - what Harvard Business Review called 'the sexiest job of the 21st century'." * Ben Taylor, Chief AI Evangelist, DataRobot *"Kirill Eremenko's book skilfully unravels the mysteries behind all the popular analytics tools and techniques, as well as many of the algorithms that power intelligent systems. I would recommend it to anyone who wants to pursue a career in data science. " * Dan Shiebler, Senior Machine Learning Engineer, Twitter Cortex *"Kirill Eremenko has come up with an amazing, unique way of making data science simple. From novices to the most experienced, anyone wanting to learn about data science will benefit from this book. Kirill covers everything from what data is and how to wrangle it, to helping you develop your own data analysis process, to effectively communicating with data. This book has it all! " * Andy Kriebel, Head Coach, The Information Lab Data School *"Eremenko is an established voice in the field, and his book is a must-read for anyone with an interest in using data science for business. Crammed with advice, Confident Data Skills provides the means to broaden one's horizons through data." * Michael Segala, CEO and Co-Founder, SFL Scientific *"Terrific. Eremenko has a knack for rendering complex theories in clear, elegant prose. Instructive and spirited, it will help you think - not only about the world around you but also about yourself." * Damian Mingle, Chief Data Scientist, Intermedix *Table of Contents Chapter - 00: Introduction; Section - ONE: "What is it?" key principles; Chapter - 01: Defining data; Chapter - 02: How data fulfils our needs; Chapter - 03: AI and our Future; Section - TWO: "When and where can I get it?" data gathering and analysis; Chapter - 04: Identify the problem; Chapter - 05: Data preparation; Chapter - 06: Data analysis (part I); Chapter - 07: Data analysis (part II); Section - THREE: "How can I present it?" communicating data; Chapter - 08: Data visualization; Chapter - 09: Data presentation; Chapter - 10: Your career in data science

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Book SynopsisElementary Statistics: A Guide to Data Analysis Using R provides students with an introduction to both the field of statistics and R, one of the most widely used languages for statistical computing, analysis, and graphing in a variety of fields, including the sciences, finance, banking, health care, e-commerce, and marketing.Part I provides an overview of both statistics and R. Part II focuses on descriptive statistics and probability. In Part III, students learn about discrete and continuous probability distributions with chapters addressing probability distributions, binominal probability distributions, and normal probability distributions. Part IV speaks to statistical inference with content covering confidence intervals, hypothesis testing, chi-square tests and F-distributions. The final part explores additional statistical inference and assumptions, including correlation, regression, and nonparametric statistics. Helpful appendices provide students with an index of terminology, an index of applications, a glossary of symbols, and a guide to the most common R commands.Elementary Statistics is an ideal resource for introductory courses in undergraduate statistics, graduate statistics, and data analysis across the disciplines.

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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'A superb romcom, with relatable and loveable characters that literally burst from the pages. With laugh-out-loud moments, woven between a heartwarming and hopeful story, it charmed me from the first page'Carmel HarringtonTrue love is only just a swipe away? Right?Iris lives by numbers. The only thing missing from her perfectly calibrated life is a partner - and not for lack of trying. After decades of disappointment, Iris practically has a PhD in online dating. But something still eludes her: that unquantifiable spark.Kim is too busy being the life of the party to look for love. Her terrible dates make great stories for her friends and co-workers, as long as she's not caught by her tyrannical boss, Iris.Connie, Kim's recently widowed mum, is single for the first time since the 1970s. The dating game has changed a lot since her day . . .Sick of being let down, Iris takes matters into her own hands - using her analytical skills to create the first real formula for love. With Kim and Connie on board, they launch Analyzed, a dating app like no other.As Analyzed takes the world by storm, are the three women in over their heads? Is love really just a numbers game?'A laugh on every page, entirely empathetic characters and a warm heart in the middle of the story. I found this book highly entertaining and totally satisfying' Liz NugentPraise for Claudia Carroll:'Brilliantly funny' Sun'An immensely talented writer' Sinéad Moriarty'Brilliant' BellaTrade ReviewA superb romcom, with relatable and loveable characters that literally burst from the pages. With laugh-out-loud moments, woven between a heartwarming and hopeful story, it charmed me from the first page. In short, I couldn't put it down, the perfect summer read! * Carmel Harrington *A laugh on every page, entirely empathetic characters and a warm heart in the middle of the story. I found this book highly entertaining and totally satisfying * Liz Nugent *

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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 SynopsisThe first part of this book is devoted to methods seeking relevant dimensions of data. The variables thus obtained provide a synthetic description which often results in a graphical representation of the data. After a general presentation of the discriminating analysis, the second part is devoted to clustering methods which constitute another method, often complementary to the methods described in the first part, to synthesize and to analyze the data. The book concludes by examining the links existing between data mining and data analysis.Trade Review"The first part of this book is devoted to methods seeking relevantdimensions of data. The variables thus obtained provide a synthetic description which often results in a graphical representation of the data. After a general presentation of the discriminating analysis, the second part is devoted to clustering methods which constitute another method, often complementary to the methods described in the first part, to synthesize and to analyze the data." (Zentralblatt MATH 2016)Table of ContentsPreface xiii Chapter 1. Principal Component Analysis: Application to Statistical Process Control 1 Gilbert SAPORTA, Ndèye NIANG 1.1. Introduction 1 1.2. Data table and related subspaces 2 1.3. Principal component analysis 8 1.4. Interpretation of PCA results 11 1.5. Application to statistical process control 18 1.6. Conclusion 22 1.7. Bibliography 23 Chapter 2. Correspondence Analysis: Extensions and Applications to the Statistical Analysis of Sensory Data 25 Jérôme PAGÈS 2.1. Correspondence analysis 25 2.2. Multiple correspondence analysis 39 2.3. An example of application at the crossroads of CA and MCA 50 2.4. Conclusion: two other extensions 63 2.5. Bibliography 64 Chapter 3. Exploratory Projection Pursuit 67 Henri CAUSSINUS, Anne RUIZ-GAZEN 3.1. Introduction 67 3.2. General principles 68 3.3. Some indexes of interest: presentation and use 71 3.4. Generalized principal component analysis 76 3.5. Example 81 3.6. Further topics 86 3.7. Bibliography 89 Chapter 4. The Analysis of Proximity Data 93 Gerard D’AUBIGNY 4.1. Introduction 93 4.2. Representation of proximity data in a metric space 97 4.3. Isometric embedding and projection 103 4.4. Multidimensional scaling and approximation 108 4.5. Afielded application 122 4.6. Bibliography 139 Chapter 5. Statistical Modeling of Functional Data 149 Philippe BESSE, Hervé CARDOT 5.1. Introduction 149 5.2. Functional framework152 5.3. Principal components analysis 156 5.4. Linear regression models and extensions 161 5.5. Forecasting 169 5.6. Concluding remarks 176 5.7. Bibliography 177 Chapter 6. Discriminant Analysis 181 Gilles CELEUX 6.1. Introduction 181 6.2. Main steps in supervised classification 182 6.3. Standard methods in supervised classification 190 6.4. Recent advances 204 6.5. Conclusion 211 6.6. Bibliography 212 Chapter 7. Cluster Analysis 215 Mohamed NADIF, Gérard GOVAERT 7.1. Introduction 215 7.2. General principles 217 7.3. Hierarchical clustering 224 7.4. Partitional clustering: the k-means algorithm 233 7.5. Miscellaneous clustering methods 239 7.6. Block clustering 245 7.7. Conclusion 251 7.8. Bibliography 251 Chapter 8. Clustering and the Mixture Model 257 Gérard GOVAERT 8.1. Probabilistic approaches in cluster analysis 257 8.2. The mixture model 261 8.3. EM algorithm 263 8.4. Clustering and the mixture model 267 8.5.Gaussian mixture model 271 8.6. Binary variables 275 8.7. Qualitative variables 279 8.8. Implementation 282 8.9. Conclusion 284 8.10. Bibliography 284 Chapter 9. Spatial Data Clustering 289 Christophe AMBROISE, Mo DANG 9.1. Introduction 289 9.2. Non-probabilistic approaches 293 9.3. Markov random fields as models 295 9.4. Estimating the parameters for a Markov field 305 9.5. Application to numerical ecology 313 9.6. Bibliography 316 List of Authors 319 Index 323

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

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Book SynopsisThis book offers an introduction and hands-on examples that demonstrate how Learning Analytics (LA) can be used to enhance digital learning, teaching and training at various levels. While the majority of existing literature on the subject focuses on its application at large corporations, this book develops and showcases approaches that bring LA closer to smaller organizations, and to educational institutions that lack sufficient resources to implement a full-fledged LA infrastructure. In closing, the book introduces a set of software tools for data analytics and visualization, and explains how they can be employed in several LA scenarios.

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Book Synopsis​This book presents both theory of financial data analytics, as well as comprehensive insights into the application of financial data analytics techniques in real financial world situations. It offers solutions on how to logically analyze the enormous amount of structured and unstructured data generated every moment in the finance sector. This data can be used by companies, organizations, and investors to create strategies, as the finance sector rapidly moves towards data-driven optimization.This book provides an efficient resource, addressing all applications of data analytics in the finance sector. International experts from around the globe cover the most important subjects in finance, including data processing, knowledge management, machine learning models, data modeling, visualization, optimization for financial problems, financial econometrics, financial time series analysis, project management, and decision making. The authors provide empirical evidence as examples of specific topics. By combining both applications and theory, the book offers a holistic approach. Therefore, it is a must-read for researchers and scholars of financial economics and finance, as well as practitioners interested in a better understanding of financial data analytics. Table of ContentsPART 1. INTRODUCTION AND ANALYTICS MODELS.- Retraining and Reskilling Financial Participators in the Digital Age.- Basics of Financial Data Analytics.- Predictive Analytics Techniques: Theory and Applications in Finance.- Prescriptive Analytics Techniques: Theory and Applications in Finance.- Forecasting Returns of Crypto Currency - Analyzing Robustness of Auto Regressive and Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANNS).- PART 2. MACHINE LEARNING.- Machine Learning in Financial Markets: Dimension Reduction and Support Vector Machine.- Pruned Random Forests for Effective and Efficient Financial Data Analytics.- Foreign Currency Exchange Rate Prediction Using Long Short Term Memory.- Natural Language Processing (NLP) for Exploring Culture in Finance: Theory and Applications.- PART 3. TECHNOLOGY DRIVEN FINANCE.- Financial Networks: A Review of Models and the Use of Network Similarities.- Optimization of Regulatory Economic-Capital Structured Portfolios: Modeling Algorithms, Financial Data Analytics and Reinforcement Machine Learning in Emerging Markets.- Transforming Insurance Business with Data Science.- A General Cyber Hygiene Approach for Financial Analytical Environment.

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Book SynopsisMaster complex problems and face radical uncertainty by unleashing the power of small data　 Is your business using data to its optimum potential? In complicated well-structured problem situations, executives rely on Big Data. However, when faced with complexity and uncertainty they are challenged to skillfully handle Small Data. Leading by Weak Signals argues that impending dangers, new business opportunities or innovative ideas may be missed when data are classified as simply not "big enough."　　 This insightful book with its new approach initiates a radical shift in perspective from running the business to changing the business. While Big Data are very well suited to run a business efficiently, Small Data lay open phenomena which are connected to transforming a company, like inflection points, scale changes, or critical transitions.　 The authors present practical business examples and an 8-step framework to implement their ideas in teams and on the individual level. This offers reflective practitioners a guideline for leveraging the enormous potential of weak signals for effective strategy development and operational execution in times of uncertainty – and gives them the competitive edge they need to succeed.

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Book SynopsisThis book is a collection of empirical and theoretical research papers on Theory and Practice in Management and Organisation Studies, written by researchers from various universities. The book is aimed at educators, researchers, and students interested in this field.

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Book SynopsisMany people go through life in a rather hit-or-miss fashion, casting about for ideas to explain why their projects improve or decline, why they are successful or why they are not. Guessing and "hunches," however, are not very reliable. And without the knowledge of how to actually investigate situations, good or bad, and get the true facts, a person is set adrift in a sea of unevaluated data. Accurate investigation is, in fact, a rare commodity. Man's tendency in matters he doesn't understand is to accept the first proffered explanation, no matter how faulty. Thus investigatory technology had not actually been practiced or refined. However, L. Ron Hubbard made a breakthrough in the subject of logic and reasoning which led to his development of the first truly effective way to search for and consistently find the actual causes for things. Knowing how to investigate gives one the power to navigate through the random facts and opinions and emerge with the real reasons behind success or failure in any aspect of life. By really finding out why things are the way they are, one is therefore able to remedy and improve a situation-any situation. This is an invaluable technology for people in all walks of life.

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Book SynopsisData Centres in India are political institutions influencing state-capital power dynamics. This book examines their social impact, highlighting power theft and territorial changes in Navi Mumbai. It explores how the Indian state adapts governance norms in the digital era with initiatives like Aadhaar and demonetisation.

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Book SynopsisThe Ultimate Power Query Cookbook for Power BI and Excel serves up easy-to-follow recipes that transform data into meaningful insights. You will learn to clean messy files, combine datasets, and even use AI magic to Power BI and Excel. This book will walk you through the basics of getting connected to data with Power Query. You will understand how to ingest data from files, folders, databases, websites, APIs, and other third party sources. Once connected, you will learn how to transform the data so it is ready for your use. We will clean up columns, filter, replace, extract, and classify data in Power Query to meet your needs. The book offers over 100 practical recipes, ensuring you understand each step with clear explanations and examples.

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Book SynopsisThis book gathers outstanding papers presented at the International Conference on Data Science and Applications (ICDSA 2022), organized by Soft Computing Research Society (SCRS) and Jadavpur University, Kolkata, India, from 26 to 27 March 2022. It covers theoretical and empirical developments in various areas of big data analytics, big data technologies, decision tree learning, wireless communication, wireless sensor networking, bioinformatics and systems, artificial neural networks, deep learning, genetic algorithms, data mining, fuzzy logic, optimization algorithms, image processing, computational intelligence in civil engineering, and creative computing.Table of ContentsImproving River Streamflow Forecasting utilizing Multi-layer Perceptron-based Butterfly Optimization Algorithm.- Covid-19 Contact Tracing Using Low Calibrated Transmission Power from BLE – Approach & Algorithm Experimentation.- Monitoring loud commercials in television broadcast.- Potential Customers Prediction in Bank Telemarketing.- Analysis and implementation of normalization techniques on KDD'99 Dataset for Detect and Prevent Intrusion on Network.- Deep Neural Networks Predicting Student Performance.- An Efficient Group Signature Scheme based on ECDLP.- Sentiment Analysis of COVID-19 tweets using TextBlob and Machine Learning classifiers - An evaluation to show how COVID -19 opinions is influencing psychological reactions of people‘s behaviour in social media.

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Book Synopsis

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Book Synopsis

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