Data science and analysis Books
Oxford University Press DATADRIVEN MODELING SCIENTIFIC COMP C Methods for Complex Systems Big Data
a huge range and FREE tracked UK delivery on ALL orders.
£117.50
Oxford University Press Analysis of Longitudinal Data Oxford Statistical Science NCS P 25 Oxford Statistical Science Series
Book SynopsisThis second edition has been completely revised and expanded to become the most up-to-date and thorough professional reference text in this fast-moving area of biostatistics. It contains an additional two chapters on fully parametric models for discrete repeated measures data and statistical models for time-dependent predictors.Trade ReviewThe book is readable, well-written, and amply illustrated * Technometrics, August 1995 (previous edition) *It belongs in the possession of every statistician who encouters longitudinal data. * Journal of the American Statistical Association *. . . provides an excellent bridge between novel concepts in theoretical statistics and their potential use in applied research. * Statistics in Medicine *The topics covered are too numerous to dwell on here ... If your work involves longitudinal data and you wish to update, this book will serve you very well. As a quick look-up, it is very useful. * Pharmaceutical Statistics *The authors conclude each chapter with a helpful summary or conclusion, often indicating further reading. Helpfully, they also mention the topics that they have chosen not to present, together with other recommended books for you to follow up ... They have also chosen a good selection of examples, many of them medical, with which the various methods are clearly illustrated. * Pharmaceutical Statistics *Readers with interests across a wide spectrum of application areas will find the ideas relevant and interesting ... The book is readable and well written ... It belongs to the possession of every statistician who encounters longitudinal data. * Zentralblatt MATH *Table of Contents1. Introduction ; 2. Design considerations ; 3. Exploring longitudinal data ; 4. General linear models ; 5. Parametric models for covariance structure ; 6. Analysis of variance methods ; 7. Generalized linear models for longitudinal data ; 8. Marginal models ; 9. Random effects models ; 10. Transition models ; 11. Likelihood-based methods for categorical data ; 12. Time-dependent covariates ; 13. Missing values in longitudinal data ; 14. Additional topics ; Appendix ; Bibliography ; Index
£53.20
Oxford University Press Questioning the Politics of Numbers
Book SynopsisQuestioning Numbers: How to Read and Critique Research is a critical companion for students in research methods courses in any of the social sciences. This book helps teach students how to read and critique research that employs numbers in the course of empirical argument. Author Karin Gwinn Wilkins provides a list of guidelines for reading research and also presents a critical approach to judging and using numbers in navigating and changing social worlds. Illuminating the agendas and politics that can inform how research is conducted and interpreted, this text shows readers how to read and critique research contexts, research design, sampling strategies, definitions, research implementation, data analysis, and interpretation. It also provides strong pedagogical support, including key terms, review exercises, and end-of-chapter reflection questions.A flexible supplement to more comprehensive research texts, Questioning Numbers helps students to become more critical consumers and producTrade Review"A handy-dandy methods supplement that really does a great job of helping students understand why scholars make the choices they do in research design and implementation."--David M. Rhea, Governors State University "This book offers students a different lens to think about all scholarship, including questions about research design and power, who funds scholarship, who is privileged (and who is left out) of research, and who gets to approve and to deny the practice of scholarship. The book gives students the language necessary to ask fundamental questions about scholarly methods that are often assumed to be true or that are typically unquestioned. Students, like Americans in general, have grown up with the notion that science is objective and provides a superior form of understanding the world, when in fact the scientific method does create an argument, often around numbers, that should be examined with a critical eye."--Brant Short, Northern Arizona University "The book does a good job of showing students how to read and critique research that uses numbers. After reading this book, students will understand how research context, selection, definition, implementation, analysis, and interpretation can impact data. And understanding this context is very useful to students."--Gonzalo R. Soruco, University of Miami "A wonderful resource that will encourage students to critically examine the impact of numbers in their lives. Karin Gwinn Wilkins's examination of the social, political, and psychological ramifications of empirical conclusions will ensure that students never take research for granted again."--Andrea Lambert, Northern Kentucky UniversityTable of ContentsUNDERSTANDING NUMBERS; DEDUCTIVE AND INDUCTIVE PROCESSES; INTRINSIC VALUE; THE POLITICAL CONTEXT OF RESEARCH; RESEARCH ILLUSTRATIONS; NUMERICAL LITERACY; KEY TERMS; REFLECTION QUESTIONS; EXERCISE; WHO PRODUCED THE RESEARCH?; RESEARCHERS' HOME INSTITUTIONS; FUNDING INSTITUTIONS; PERMISSION GRANTING INSTITUTIONS; WHAT IS THE PURPOSE OF THE RESEARCH?; WHAT ARE THE RESEARCH QUESTIONS?; HOW IS THE RESEARCH JUSTIFIED?; WHY THE RESEARCH CONTEXT MATTERS; KEY TERMS; REVIEW QUESTIONS; REFLECTION QUESTIONS; EXERCISE; WHAT IS THE RESEARCH DESIGN?; WHAT ARE THE INTERNAL VALIDITY CONCERNS?; EXPERIMENTAL DESIGN; QUASI-EXPERIMENTAL CROSS-SECTIONAL DESIGN; QUASI-EXPERIMENTAL LONGITUDINAL DESIGN; CASE STUDY DESIGN; QUESTIONING RESEARCH DESIGNS; WHY RESEARCH DESIGN MATTERS; KEY TERMS; REVIEW QUESTIONS; REFLECTION QUESTIONS; EXERCISE; WHO OR WHAT IS THE SUBJECT OF THE RESEARCH?; VULNERABLE POPULATIONS; HOW ARE SUBJECTS SELECTED?; PROBABILITY SAMPLING; NON-PROBABILITY SAMPLING; WHAT ARE THE LIMITATIONS OF THE SAMPLE?; WHY SELECTION STRATEGIES MATTER; KEY TERMS; REVIEW QUESTIONS; REFLECTION QUESTIONS; EXERCISE; HOW ARE KEY CONCEPTS DEFINED?; HOW ARE VARIABLES OPERATIONALIZED FROM CONCEPTS?; WHAT DO WE KNOW ABOUT KEY VARIABLES?; MEASUREMENT; ASSESSMENT; WHY DEFINITIONS MATTER; KEY TERMS; REVIEW QUESTIONS; REFLECTION QUESTIONS; EXERCISE; HOW ARE DATA GATHERED?; HOW DO RESEARCHERS GAIN ACCESS TO DATA?; DECEPTION; VOLUNTARY PARTICIPATION; INFORMED CONSENT; PUBLIC SPACE; HOW DO RESEARCHERS DOCUMENT THEIR OBSERVATIONS?; RECORDING OBSERVATIONS; CONFIDENTIALITY AND ANONYMITY; WHY RESEARCH IMPLEMENTATION MATTERS; KEY TERMS; REVIEW QUESTIONS; REFLECTION QUESTIONS; EXERCISE; WHAT DO WE KNOW ABOUT PATTERNS ACROSS VARIABLES?; SIGNIFICANCE; STRENGTH; CAUSALITY; WHAT DO WE STILL NEED TO KNOW?; WHY ANALYSIS AND INTERPRETATION MATTER; KEY TERMS; REVIEW QUESTIONS; REFLECTION QUESTIONS; EXERCISE; GUIDELINE OF QUESTIONS; QUESTIONING THE RESEARCH CONTEXT; QUESTIONING THE RESEARCH DESIGN; QUESTIONING THE SELECTION STRATEGY; QUESTIONING KEY TERMS; QUESTIONING THE RESEARCH IMPLEMENTATION; QUESTIONING ANALYSIS & INTERPRETATION; WHY THE POLITICAL CONTEXT MATTERS
£25.42
Cambridge University Press Working with Network Data
Book SynopsisDrawing examples from real-world networks, this essential book traces the methods behind network analysis and equips you with a toolbox of diverse methods and data modelling approaches. Suitable for both graduate students and researchers across a range of disciplines, this novel text provides a fast-track to network data expertise.
£49.99
Cambridge University Press A HandsOn Introduction to Data Science with R
a huge range and FREE tracked UK delivery on ALL orders.
£99.99
Medical Group Management Association/Center for Research in Ambulatory Health Care Administration Data Sanity: A Quantum Leap to Unprecedented Results
£97.85
Statistics by Jim Publishing Introduction to Statistics: An Intuitive Guide for Analyzing Data and Unlocking Discoveries
£19.56
Statistics by Jim Publishing Hypothesis Testing: An Intuitive Guide for Making Data Driven Decisions
£23.51
Statistics by Jim Publishing Regression Analysis: An Intuitive Guide for Using and Interpreting Linear Models
£23.51
Sad Press and Friends Visualising Uncertainty: A short introduction
£13.62
£30.00
Ladoo Publishing LLC Python: This book includes: Python basics for Beginners + Python Automation Techniques And Web Scraping + Python For Data Science And Machine Learning
£24.99
Bloomsbury Publishing (UK) The Trouble With Big Data
Book SynopsisJennifer Edmond is Associate Professor of Trinity College Dublin and the co-director of the Trinity Center for Digital Humanities, Ireland. Jennifer also serves as President of the Board of Directors of the pan-European research infrastructure for the arts and humanities, DARIAH-EU and represents this body on the Open Science Policy Platform (OSPP), which supports the European Commission in developing and promoting Open Science policies. Nicola Horsley's qualitative research critiques the marginalisation of the social in various discourses and explores the dominance of scientific and technical knowledge as bases for policy and practice. Her co-authored book, Challenging the Politics of Early Intervention explores the scientific evidence base for early intervention policies.Jörg Lehmann is a post-doctoral research fellow at the Romanistic Seminar at Eberhard Karls University Tübingen, Germany. He has published two monographs on war literaTrade ReviewBy examining the much-hyped phenomenon of ‘big data’ through a humanist lens, the authors provide a rich account of the possibilities and limits. They focus on the importance of culture and context for understanding how data are imagined, collected, analysed and understood. * Sally Wyatt, Professor of Digital Cultures, Maastricht University, the Netherlands *Table of ContentsIntroduction Chapter 1: Data and Language Chapter 2: Data and Sensemaking Chapter 3: Data and Invisibility Chapter 4: Big Data and the Abyss of Aggregation Chapter 5: Data and Power Conclusion
£85.50
Bloomsbury Publishing PLC Big Data
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
£12.34
Pearson Education Effective Data Analysis
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.
£41.39
World Scientific Europe Ltd Statistical Data Science
Book SynopsisAs an emerging discipline, data science broadly means different things across different areas. Exploring the relationship of data science with statistics, a well-established and principled data-analytic discipline, this book provides insights about commonalities in approach, and differences in emphasis.Featuring chapters from established authors in both disciplines, the book also presents a number of applications and accompanying papers.
£80.00
LID Publishing The Fifth Phase: An insight-driven approach to
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.
£12.74
John Wiley & Sons Inc Data Analysis and Visualization in Genomics and
Book SynopsisData Analysis and Visualization in Genomics and Proteomics is the first book addressing integrative data analysis and visualization in this field. It addresses important techniques for the interpretation of data originating from multiple sources, encoded in different formats or protocols, and processed by multiple systems.Table of ContentsPreface. List of Contributors. SECTION I: INTRODUCTION - DATA DIVERSITY AND INTEGRATION. 1. Integrative Data Analysis and Visualization: Introduction to Critical Problems, Goals and Challenges (Francisco Azuaje and Joaquín Dopazo). 1.1 Data Analysis and Visualization: An Integrative Approach. 1.2 Critical Design and Implementation Factors. 1.3 Overview of Contributions. References. 2. Biological Databases: Infrastructure, Content and Integration (Allyson L. Williams, Paul J. Kersey, Manuela Pruess and Rolf Apweiler). 2.1 Introduction. 2.2 Data Integration. 2.3 Review of Molecular Biology Databases. 2.4 Conclusion. References. 3. Data and Predictive Model Integration: an Overview of Key Concepts, Problems and Solutions (Francisco Azuaje, Joaquín Dopazo and Haiying Wang). 3.1 Integrative Data Analysis and Visualization: Motivation and Approaches. 3.2 Integrating Informational Views and Complexity for Understanding Function. 3.3 Integrating Data Analysis Techniques for Supporting Functional Analysis. 3.4 Final Remarks. References. SECTION II: INTEGRATIVE DATA MINING AND VISUALIZATION -EMPHASIS ON COMBINATION OF MULTIPLE DATA TYPES. 4. Applications of Text Mining in Molecular Biology, from Name Recognition to Protein Interaction Maps (Martin Krallinger and Alfonso Valencia). 4.1 Introduction. 4.2 Introduction to Text Mining and NLP. 4.3 Databases and Resources for Biomedical Text Mining. 4.4 Text Mining and Protein-Protein Interactions. 4.5 Other Text-Mining Applications in Genomics. 4.6 The Future of NLP in Biomedicine. Acknowledgements. References. 5. Protein Interaction Prediction by Integrating Genomic Features and Protein Interaction Network Analysis (Long J. Lu, Yu Xia, Haiyuan Yu, Alexander Rives, Haoxin Lu, Falk Schubert and Mark Gerstein). 5.1 Introduction. 5.2 Genomic Features in Protein Interaction Predictions. 5.3 Machine Learning on Protein-Protein Interactions. 5.4 The Missing Value Problem. 5.5 Network Analysis of Protein Interactions. 5.6 Discussion. References. 6. Integration of Genomic and Phenotypic Data (Amanda Clare). 6.1 Phenotype. 6.2 Forward Genetics and QTL Analysis. 6.3 Reverse Genetics. 6.4 Prediction of Phenotype from Other Sources of Data. 6.5 Integrating Phenotype Data with Systems Biology. 6.6 Integration of Phenotype Data in Databases. 6.7 Conclusions. References. 7. Ontologies and Functional Genomics (Fátima Al-Shahrour and Joaquín Dopazo). 7.1 Information Mining in Genome-Wide Functional Analysis. 7.2 Sources of Information: Free Text Versus Curated Repositories. 7.3 Bio-Ontologies and the Gene Ontology in Functional Genomics. 7.4 Using GO to Translate the Results of Functional Genomic Experiments into Biological Knowledge. 7.5 Statistical Approaches to Test Significant Biological Differences. 7.6 Using FatiGO to Find Significant Functional Associations in Clusters of Genes. 7.7 Other Tools. 7.8 Examples of Functional Analysis of Clusters of Genes. 7.9 Future Prospects. References. 8. The C. elegans Interactome: its Generation and Visualization (Alban Chesnau and Claude Sardet). 8.1 Introduction. 8.2 The ORFeome: the first step toward the interactome of C. elegans. 8.3 Large-Scale High-Throughput Yeast Two-Hybrid Screens to Map the C. elegans Protein-Protein Interaction (Interactome) Network: Technical Aspects. 8.4 Visualization and Topology of Protein-Protein Interaction Networks. 8.5 Cross-Talk Between the C. elegans Interactome and other Large-Scale Genomics and Post-Genomics Data Sets. 8.6 Conclusion: From Interactions to Therapies. References. SECTION III: INTEGRATIVE DATA MINING AND VISUALIZATION - EMPHASIS ON COMBINATION OF MULTIPLE PREDICTION MODELS AND METHODS. 9. Integrated Approaches for Bioinformatic Data Analysis and Visualization - Challenges, Opportunities and New Solutions (Steve R. Pettifer, James R. Sinnott and Teresa K. Attwood). 9.1 Introduction. 9.2 Sequence Analysis Methods and Databases. 9.3 A View Through a Portal. 9.4 Problems with Monolithic Approaches: One Size Does Not Fit All. 9.5 A Toolkit View. 9.6 Challenges and Opportunities. 9.7 Extending the Desktop Metaphor. 9.8 Conclusions. Acknowledgements. References. 10. Advances in Cluster Analysis of Microarray Data (Qizheng Sheng, Yves Moreau, Frank De Smet, Kathleen Marchal and Bart De Moor). 10.1 Introduction. 10.2 Some Preliminaries. 10.3 Hierarchical Clustering. 10.4 k-Means Clustering. 10.5 Self-Organizing Maps. 10.6 A Wish List for Clustering Algorithms. 10.7 The Self-Organizing Tree Algorithm. 10.8 Quality-Based Clustering Algorithms. 10.9 Mixture Models. 10.10 Biclustering Algorithms. 10.11 Assessing Cluster Quality. 10.12 Open Horizons. References. 11. Unsupervised Machine Learning to Support Functional Characterization of Genes: Emphasis on Cluster Description and Class Discovery (Olga G. Troyanskaya). 11.1 Functional Genomics: Goals and Data Sources. 11.2 Functional Annotation by Unsupervised Analysis of Gene Expression Microarray Data. 11.3 Integration of Diverse Functional Data For Accurate Gene Function Prediction. 11.4 MAGIC - General Probabilistic Integration of Diverse Genomic Data. 11.5 Conclusion. References. 12. Supervised Methods with Genomic Data: a Review and Cautionary View (Ramón Díaz-Uriarte). 12.1 Chapter Objectives. 12.2 Class Prediction and Class Comparison. 12.3 Class Comparison: Finding/Ranking Differentially Expressed Genes. 12.4 Class Prediction and Prognostic Prediction. 12.5 ROC Curves for Evaluating Predictors and Differential Expression. 12.6 Caveats and Admonitions. 12.7 Final Note: Source Code Should be Available. Acknowledgements. References. 13. A Guide to the Literature on Inferring Genetic Networks by Probabilistic Graphical Models (Pedro Larrañaga, Iñaki Inza and Jose L. Flores). 13.1 Introduction. 13.2 Genetic Networks. 13.3 Probabilistic Graphical Models. 13.4 Inferring Genetic Networks by Means of Probabilistic Graphical Models. 13.5 Conclusions. Acknowledgements. References. 14. Integrative Models for the Prediction and Understanding of Protein Structure Patterns (Inge Jonassen). 14.1 Introduction. 14.2 Structure Prediction. 14.3 Classifications of Structures. 14.4 Comparing Protein Structures 14.5 Methods for the Discovery of Structure Motifs. 14.6 Discussion and Conclusions. References. Index.
£132.26
John Wiley & Sons Inc Practical Methods for Design and Analysis of
Book SynopsisStatistical complex survey analysis is a means to analyse the results, and gain information about a large population based on a complex survey of a sample of that population. A complex survey is a sample survey that divides the population into subgroups and collecting information from clusters within each subgroup and combining the results.Trade Review"As in the previous edition, this book is a good resource for practitioners and cross-disciplinary researchers who use data from complex survey designs." (Journal of the American Statistical Association, March 2006) "The first edition of the book was one of the first books in the excellent Wiley U.K. series on Statistics in Practice." (Technometrics, May 2005)Table of ContentsPreface. 1. Introduction. 2. Basic Sampling Techniques. 2.1 Basic definitions. 2.2 The Province’91 population. 2.3 Simple random sampling and design effect. 2.4 Systematic sampling and intra-class correlation. 2.5 Selection with probability proportional to size. 3. Further Use of Auxiliary Information. 3.1 Stratified sampling. 3.2 Cluster sampling. 3.3 Model-assisted estimation. 3.4 Efficiency comparison using design effects. 4. Handling Nonsampling Errors. 4.1 Reweighting. 4.2 Imputation. 4.3 Chapter summary and further reading. 5. Linearization and Sample Reuse in Variance Estimation. 5.1 The Mini-Finland Health Survey. 5.2 Ratio estimators. 5.3 Linearization method. 5.4 Sample reuse methods. 5.5 Comparison of variance estimators. 5.6 The Occupational Health C are Survey. 5.7 Linearization method for covariance-matrix estimation. 5.8 Chapter summary and further reading. 6. Model-assisted Estimation for Domains. 6.1 Framework for domain estimation. 6.2 Estimator type and model choice. 6.3 Construction of estimators and model specification. 6.4 Further comparison of estimators. 6.5 Chapter summary and further reading. 7. Analysis of One-way and Two-way Tables. 7.1 Introductory example. 7.2 Simple goodness-of-fit test. 7.3 Preliminaries for tests for two-way tables. 7.4 Test of homogeneity. 7.5 Test of independence. 7.6 Chapter summary and further reading. 8. Multivariate Survey Analysis. 8.1 Range of methods. 8.2 Types of models and options for analysis. 8.3 Analysis of categorical data. 8.4 Logistic and linear regression. 8.5 Chapter summary and further reading. 9. More Detailed Case Studies. 9.1 Monitoring quality in a long-term transport survey. 9.2 Estimation of mean salary in a business survey. 9.3 Model selection in a socioeconomic survey. 9.4 Multi-level modelling in an educational survey. References. Author Index. Subject Index. Web Extension. In addition to the printed book, electronic materials supporting the use of the book can be found in the web extension.
£100.76
John Wiley & Sons Inc How to Conduct Your Own Survey
Book SynopsisA nuts-and-bolts guide to conducting your own professional-quality surveys without paying professional fees. How can you gauge public support for a cause or test the market for a product or service? What are the best methods for validating opinions for use in a paper or dissertation? A well-documented survey is the answer.Table of ContentsPractical Surveys. Cornerstones of a Quality Survey. Deciding What Information You Need. Choosing a Survey Method. When and How to Select a Sample. Writing Good Questions. Questionnaire Design. Setting Your Survey in Motion and Getting It Done. From Questionnaires to Survey Results. Reporting Survey Results. Advice, Resources, and Maintaining Perspective. References. Index.
£34.00
John Wiley & Sons Inc How to Conduct Your Own Survey
Book SynopsisA nuts-and-bolts guide to conducting your own professional-quality surveys without paying professional fees. How can you gauge public support for a cause or test the market for a product or service? What are the best methods for validating opinions for use in a paper or dissertation? A well-documented survey is the answer.Table of ContentsPractical Surveys. Cornerstones of a Quality Survey. Deciding What Information You Need. Choosing a Survey Method. When and How to Select a Sample. Writing Good Questions. Questionnaire Design. Setting Your Survey in Motion and Getting It Done. From Questionnaires to Survey Results. Reporting Survey Results. Advice, Resources, and Maintaining Perspective. References. Index.
£19.54
John Wiley & Sons Inc Analysis of Survey Data
Book SynopsisThis book is concerned with statistical methods for the analysis of data collected from a survey. A survey could consist of data collected from a questionnaire or from measurements, such as those taken as part of a quality control process.Table of ContentsPreface. List of Contributors. Introduction (R. L. Chambers & C. J. Skinner). PART A: APPROACHES TO INFERENCE. Introduction to Part A (R. L.Chambers). Design-based and Model-based Methods for Estimating Model Parameters(David A. Binder and Georgia R. Roberts). The Bayesian Approach to Sample Survey Inference (Roderick J. Little). Interpreting a Sample as Evidence about a Finite Population (Richard Royall). PART B: CATEGORICAL RESPONSE DATA. Introduction to Part B (C. J.Skinner). Analysis of Categorical Response Data from Complex Surveys: an Appraisal and Update (J. N. K. Rao and D. R. Thomas). Fitting Logistic Regression Models in Case-Control Studies with Complex Sampling (Alastair Scott and Chris Wild). PART C: CONTINUOUS AND GENERAL RESPONSE DATA. Introduction to Part C (R. L.Chambers). Graphical Displays of Complex Survey Data through Kernel Smoothing (D. R. Bellhouse, C. M. Goia, and J. E. Stafford) Nonparametric Regression with Complex Survey Data (R. L. Chambers, A. H. Dorfman and M. Yu. Sverchkov). Fitting Generalized Linear Models under Informative Sampling (Danny Pfeffermann and M. Yu. Sverchkov). PART D: LONGITUDINAL DATA. Introduction to Part D (C. J.Skinner). Random Effects Models for Longitudinal Survey Data (C. J.Skinner and D. J.Holmes). Event History Analysis and Longitudinal Surveys (J. F. Lawless). Applying Heterogeneous Transition Models in Labour Economics: the Role of Youth Training in Labour Market Transitions (Fabrizia Mealli and Stephen Pudney). PART E: INCOMPLETE DATA. Introduction to Part E (R. L.Chambers). Bayesian Methods for Unit and Item Nonresponse (Roderick J. Little). Estimation for Multiple Phase Samples (Wayne A. Fuller). Analysis Combining Survey and Geographically Aggregated Data (D. G. Steel, M. Tranmer and D. Holt). References. T. M. F.Smith: Publications up to 2002. Author Index. Subject Index.
£109.76
Princeton University Press Data Analysis for Scientists and Engineers
Book SynopsisTrade Review"Robinson's text is an excellent overview of modern statistical techniques and is sure to become a definitive reference. He ably and concisely presents all of the necessary foundational mathematics while also providing a thorough description of sophisticated methods used by practicing engineers and scientists. I particularly enjoyed the division of the book into frequentist and Bayesian approaches and Robinson's clear discussion of the relative merits of each method."—Jeremy Kasdin, Princeton University"With an accessible and consistent style, Data Analysis for Scientists and Engineers stands out for its depth of materials and pedagogical presentation. Building from simple concepts, the book's mathematical rigor and accuracy are solid and logical. This book is appropriate for senior undergraduates, graduate students at all levels, and practicing scientists."—Wade Fisher, Michigan State University
£68.00
Princeton University Press Thinking Clearly with Data A Guide to
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
£74.80
Princeton University Press Data Science for Neuroimaging
Book Synopsis
£32.30
Pluto Press Data Power
Book SynopsisAn introduction to learning how to protect ourselves and organise against Big DataTrade Review'A call to arms [...] sets out a clear, persuasive argument for the need to challenge the power of platforms and systems, and details the tools to do so. A thought-provoking read' -- Prof. Rob Kitchin, Maynooth University‘The first non-technical guidebook on how to live with location data and it is a truly radical response for our times. Spatial data for us, not about us’ -- Jeremy W. Crampton, Professor of Urban Data Analysis, Newcastle University‘Brilliantly traces the closed loops of spatial data and suggests new escape routes, reminding us that our data can be remade to tell different stories’ -- Professor Kate Crawford, author of ‘Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence’'The book that I’ve long been waiting for, one that takes a material approach to the data geographies informing and being informed by technologies of everyday life’ -- Erin McElroy, Assistant Professor of American and Digital Studies at the University of Texas at Austin and cofounder of the Anti-Eviction Mapping Project'Data Power is an activist handbook wrapped in a theoretical treatise inside a media manifesto. The authors have a lively set of suggestions that provide a welcome antidote to the temptations of resignation and complacency' -- Mark Andrejevic, Professor in the School of Media, Film, and Journalism at Monash UniversityTable of ContentsList of Figures and Tables Series Preface Acknowledgments List of Abbreviations Introduction: Technology and the Axes of Hope and Fear 1. Life in the Age of Big Data 2. What Are Our Data, and What Are They Worth? 3. Existing Everyday Resistances 4. Contesting the Data Spectacle 5. Our Data Are Us, So Make Them Ours Epilogue Notes Bibliography Index
£18.99
Kogan Page Ltd Practical Text Analytics
Book SynopsisSteven Struhl PhD, MBA, MA has more than 25 years' experience in consulting and research, specializing in practical solutions based on statistical models of decision-making and behaviour. In addition to text analytics and data mining, his work addresses how buying decisions are made, optimizing service delivery and product configurations and finding the meaningful differences among products and services. Steven also has taught graduate courses on statistical methods and data analysis. He speaks at conferences and has given numerous seminars on pricing, choice modelling, market segmentation and presenting data.Trade Review"Textual analysis has recently become a useful research methodology, of great interest to both academics and practitioners. Dr. Steven Struhl provides relevant and lucid discussion of the topic, highlighting the fundamental issues involved in preparing, analyzing, and presenting textual data for meaningful interpretations. A very interesting and timely contribution that should be of interest to a wide range of audiences." * Dr. Jehoshua Eliashberg, Sebastian S. Kresge Professor of Marketing, Professor of Operations and Information Management, Wharton University *"Steven provides a broad and fair context in which to understand textual analysis in a very readable and informative way. I'm confident this would provide great value to anyone with an interest in the Internet and textual analysis, researcher and non-researcher alike." * Darrin Helsel, Co-Founder and Principal of Distill Research LLC, and Research Chair, American Marketing Association, Portland Chapter *"Steven Struhl has an incredible knack for demystifying complex analyses and analytic software, and making it accessible to those who are interested in what it does without delving too deeply into the incomprehensible elements of how it works. In his new book, Dr. Struhl takes on text analytics. I found the chapter on Bayes Nets particularly useful. In it he shows quite convincingly that, in some cases, they do a much better job with text than other predictive methods. He provides a story through crystal-clear examples that are immediately interesting and easy to follow." * Larry Durkin, Principal, MSP Analytics *"As I've been evaluating text analytics materials lately for my data science education engagements, much of what I've found published on this subject is written from a very academic and technical perspective that is not very approachable for someone that doesn't have a fairly deep expertise in statistics, math and programming. This book solves that disconnect. A welcome addition to any data scientist's library. In addition, the timely nature of the subject should provide much food-for-thought as the rise in interest in unstructured data processing techniques continues to be of interest. Highly recommended." * Daniel D. Gutierrez, Inside Big Data *"A fascinating, if not rather specialist book, which aims to be an accessible guide to the world of text analytics and data analysis for marketing folk." * Darren Ingram, Darren Ingram Media *Table of Contents Chapter - 01: Who should read this book? And what do you want to do today?; Chapter - 02: Getting ready: capturing, sorting, sifting, stemming and matching; Chapter - 03: In pictures: word clouds, wordles and beyond; Chapter - 04: Putting text together: clustering documents using words; Chapter - 05: In the mood for sentiment (and counting) ; Chapter - 06: Predictive models 1: having words with regressions; Chapter - 07: Predictive models 2: classifications that grow on trees; Chapter - 08: Predictive models 3: all in the family with Bayes Nets; Chapter - 09: Looking forward and back
£33.24
Kogan Page Ltd Practical Text Analytics
Book SynopsisSteven Struhl PhD, MBA, MA has more than 25 years' experience in consulting and research, specializing in practical solutions based on statistical models of decision-making and behaviour. In addition to text analytics and data mining, his work addresses how buying decisions are made, optimizing service delivery and product configurations and finding the meaningful differences among products and services. Steven also has taught graduate courses on statistical methods and data analysis. He speaks at conferences and has given numerous seminars on pricing, choice modelling, market segmentation and presenting data.Trade Review"Textual analysis has recently become a useful research methodology, of great interest to both academics and practitioners. Dr. Steven Struhl provides relevant and lucid discussion of the topic, highlighting the fundamental issues involved in preparing, analyzing, and presenting textual data for meaningful interpretations. A very interesting and timely contribution that should be of interest to a wide range of audiences." * Dr. Jehoshua Eliashberg, Sebastian S. Kresge Professor of Marketing, Professor of Operations and Information Management, Wharton University *"Steven provides a broad and fair context in which to understand textual analysis in a very readable and informative way. I'm confident this would provide great value to anyone with an interest in the Internet and textual analysis, researcher and non-researcher alike." * Darrin Helsel, Co-Founder and Principal of Distill Research LLC, and Research Chair, American Marketing Association, Portland Chapter *"Steven Struhl has an incredible knack for demystifying complex analyses and analytic software, and making it accessible to those who are interested in what it does without delving too deeply into the incomprehensible elements of how it works. In his new book, Dr. Struhl takes on text analytics. I found the chapter on Bayes Nets particularly useful. In it he shows quite convincingly that, in some cases, they do a much better job with text than other predictive methods. He provides a story through crystal-clear examples that are immediately interesting and easy to follow." * Larry Durkin, Principal, MSP Analytics *"As I've been evaluating text analytics materials lately for my data science education engagements, much of what I've found published on this subject is written from a very academic and technical perspective that is not very approachable for someone that doesn't have a fairly deep expertise in statistics, math and programming. This book solves that disconnect. A welcome addition to any data scientist's library. In addition, the timely nature of the subject should provide much food-for-thought as the rise in interest in unstructured data processing techniques continues to be of interest. Highly recommended." * Daniel D. Gutierrez, Inside Big Data *"A fascinating, if not rather specialist book, which aims to be an accessible guide to the world of text analytics and data analysis for marketing folk." * Darren Ingram, Darren Ingram Media *Table of Contents Chapter - 01: Who should read this book? And what do you want to do today?; Chapter - 02: Getting ready: capturing, sorting, sifting, stemming and matching; Chapter - 03: In pictures: word clouds, wordles and beyond; Chapter - 04: Putting text together: clustering documents using words; Chapter - 05: In the mood for sentiment (and counting) ; Chapter - 06: Predictive models 1: having words with regressions; Chapter - 07: Predictive models 2: classifications that grow on trees; Chapter - 08: Predictive models 3: all in the family with Bayes Nets; Chapter - 09: Looking forward and back
£95.00
Kogan Page Ltd Predictive Analytics for Marketers
Book SynopsisDr Barry Leventhal is a leading UK authority on geodemographics and a marketing analytics expert. He is Emeritus Chair of the Census and Geodemographics Group (CGG), which is an advisory board of The Market Research Society (MRS) and a leading voice in the UK information industry. He was recently awarded the MRS Gold Medal Award - the association's rarest accolade, presented for the first time since 2008 - in recognition of his lifetime of exceptional achievement and contribution to the research profession.Trade Review"This book is an invaluable aid in the journey from big data to smart data usage, which is where competitive advantage rests. Leventhal delivers lashings of common sense based on erudition and experience, making this a very pragmatic and useful work." * Jane Frost CBE, Chief Executive Office, Market Research Society *"A comprehensive, engaging and accessible introduction to the increasingly important field of predictive analytics and marketing from one of the leading practitioners. Leventhal takes each of the main application areas in turn and focuses on how to generate value from data for your organization." * Tom Smith, Managing Director, Office for National Statistics (ONS) Data Science Campus. *"Leventhal masterfully presents a complex subject in a highly accessible way, liberally illustrating the material with real-life examples from his own experience." * Professor David J. Hand, Emeritus Professor of Mathematics, Imperial College London and Chief Scientific Advisor, Winton Group *"Leventhal has distilled his wealth of rich practical experience into a clear and comprehensive text, sharing best practice in methods for collecting data, building models, and operationalizing and leveraging the power of data to maximize economic value. A mandatory book for anyone working with customer data or predictive analytics." * Paul Cushion, Customer and Digital Associate, KPMG Management Consultancy *"I highly recommend this book both to those starting out in a career in marketing and to those seasoned marketers in need of some new tricks if they are to stay relevant." * Giles Pavey, Head of Data Strategy at the Department for Work & Employment and Former Chief Data Scientist at dunnhumby Ltd *"In a world teeming with data, competitive advantage now firmly lies in how effectively data is analysed. This book provides a comprehensive guide on how to approach, execute, evaluate and get the most out of predictive analytics. It is very easy to read - even for the non-statistically minded." * Lynne Robinson, Research Director, Institute of Practitioners in Advertising (IPA) *"If you think predictive analytics is not for you, think again. It is vital for anyone in any management capacity. Leventhal's Predictive Analytics for Marketers is required reading for anyone who needs to understand the latest practical methods to segment and analyse data, whether for the public or the private sector, or to predict future success or understand reasons for failure." * Roger Holland, Executive Chairman, JICPOPS (the Joint Industry Committee for Population Standards) *"Throughout, this is a very practical guide, with a number of marketing-focused case studies bringing the power of the analytical techniques discussed to life. A book that's very definitely not just for the shelf!" * Paul Cresswell, Head of Data Governance, Experian Marketing Services - Targeting *"Predictive Analytics for Marketers clearly explains the analytics process and its commercial context in language understandable to managers, marketers, IT specialists and analysts. It addresses the essential areas of communication between these specialisms, giving lucid accounts of the process of planning an analytics project, the importance of framing the business problem, and the need for its alignment with appropriate methods. Leventhal's book is a welcome addition, covering current topics in analytics clearly and insightfully." * David Harris, Product Development Partner, CACI Ltd. *"This is much more than a lucid and comprehensive textbook on predictive analytics. Leventhal's profound expertise shines through as he shares his thoughts from a practical as well as technical point of view. For businesses who wish to be data driven, this unambiguous and wise advice will provide an accelerated path to success." * Gordon Farquharson, Director of Analytics, more2 ltd *"Leventhal helpfully clarifies key concepts and gives sound and practical advice, drawing on his extensive experience in marketing. No matter how much you think you know about analytics, I suggest you read this book, apply it, and benefit from it!" * Paul Allin, Visiting Professor in Statistics, Imperial College London *Table of Contents Section - 00: Introduction to predictive analytics; Section - 01: How can predictive analytics help your business?; Section - 02: Using data mining to build predictive models; Section - 03: Managing the data for predictive analytics; Section - 04: The analytical modelling toolkit; Section - 05: Software solutions for predictive analytics; Section - 06: Predicting customer behaviour using analytical models; Section - 07: Predicting lifetimes – from customers to machines; Section - 08: How to build a customer segmentation; Section - 09: Accounts, baskets, citizens or businesses – applying predictive analytics in various sectors; Section - 10: From people to products – using predictive analytics in retail; Section - 11: How to benefit from social network analysis; Section - 12: Testing the benefits of predictive models and other marketing effects; Section - 13: Top tips for gaining business value from predictive analytics;
£25.64
John Wiley and Sons Ltd Animal Disease Surveillance and Survey Systems
Book SynopsisThis valuable text presents methods and techniques for conducting an animal disease surveillance program, and developing an animal health moitoring system. The text is a ''recipe book'' for these techniques as it explains modern techniques, while emphasizing the fundamentals and principles of using these techniques.The book is targeted to epidemiologists and other animal health authorities who are working in national, regional, and international programs. The book can be used as a text for professional and postgraduate training curricula. This text will be of value in veterinary epidemiology and regulatory medicine, where there is need for a concise collection of material on animal disease monitoring, surveillance, and reporting strategies. This need arises from a new era of international trade regulations based on animal diseases, new demands for accountability in utilization of research funds, and calls for prioritizing and economically justifying animal health regulatory aTable of ContentsPreface. Contributors. Chapter 1. Surveillance and Monitoring Systems for Animal Health Program and Disease Suveys (M.D. Salman). Chapter 2. Application of Surveillance and Monitoring Systems in Disease Control Programs (J. Christensen). Chapter 3. Planning Survey, Surveillance, and Monitoring Systems-Roles and Requirements (C. Zepeda and M.D. Salman). Chapter 4. Sampling Considerations in Surveys and Monitoring and Surveillance Systems (A. Cameron, I. Gardner, M.G. Doherr, and B. Wagner). Chapter 5. Statistical Analysis of Data from Surveys, Monitoring, and Surveillance Systems (B. Wagner, I. Gardner, A. Cameron, and M.G. Doherr). Chapter 6. Methods for Determining Temporal Clusters in Surveillance and Survey Programs (T.E. Carpenter and M.P. Ward). Chapter 7. Methods for Determining Spartial Clusters in Surveillance and Survey Programs (T.E. Carpenter and M.P. Ward). Chapter 8. Use of Sentinel Herds in Monitoring and Surveillance Systems (B.J. McCluskey). Chapter 9. Use of Animal Monitoring and Surveillance Systems When the Frequency of Health-Related Events is Near Zero (M.G. Doherr, L. Audigé, M.D. Salman, and I.A. Gardner). Chapter 10. Use of Simulation Models in Surveillance and Monitoring Systems (L. Audigé, M.G. Doherr, and B. Wagner). Chapter 11. Quality Assessment of Animal Disease Surveillance and Survey Systems (K.D.C. Stärk). Chapter 12. Dissemination of Surveillance Findings (N.E. Wineland and D.A. Dargatz). Chapter 13. Danish Swine Salmonellosis Control Program: 1993 to 2001 (J. Christensen). Index.
£75.56
MP-ALA American Library Assoc 3D Data Creation to Curation Community Standards
Book SynopsisCovers best practices for 3D data preservation, management, metadata, legal issues, and access. Beginning with surveys of current practices, the authors provide recommendations for implementing standards and identify areas in which further development is required. A glossary of key terms and acronyms is included for easy reference.Table of Contents Acknowledgments Chapter 1. Introduction Jennifer Moore, Adam Rountrey, and Hannah Scates Kettler Context for This Work The Democratization of 3D Data Production The Audience The Creators Values of CS3DP From Creation to Preservation Modalities Represented in the Chapters What to Expect Notes Bibliography Chapter 2. Best Practices for 3D Data Preservation Kristina Golubiewski-Davis, Jessica Maisano, Marcia McIntosh, Jennifer Moore, Kieron Niven, Will Rourk, and Rebecca Snyder Introduction Existing Standards Preservation Intervention Points Documentation Good/Better/Best Recommendations for Implementation Conclusion Notes Bibliography Chapter 3. Management and Storage of 3D Data Doug Boyer, Rachel Fernandez, Monique Lassere, Marcia McIntosh, Jennifer Moore, Francis P. McManamon, Albert Rozo, Todd P. Swanson, and Kate Webbink Introduction Survey Overview Management Technology Sustainability Conclusion Notes Bibliography Chapter 4. Metadata Requirements for 3D Data Jon Blundell, Jasmine L. Clark, Katherine E. DeVet, and Juliet L. Hardesty Introduction Methods Considerations, Decisions, and Scope Digital Asset Life Cycle and 3D Metadata Gap Analysis/Future Work Conclusion: Summary Recommendations Acknowledgments Notes Bibliography Chapter 5. Copyright and Legal Issues Surrounding 3D Data Andrea D’Andrea, Michael Conyers, Kyle K. Courtney, Emily Finch, Melissa Levine, Nicole Meyer, Adam Rountrey, Hannah Scates Kettler, Kate Webbink, and Ann Whiteside Introduction Foundations: Copyright and the “Bundle of Rights” Case Studies Conclusion Notes Bibliography Chapter 6. Accessing 3D Data Francesca Albrezzi, John Bonnett, Tassie Gniady, Heather Richards-Rissetto, and Lisa M. Snyder Introduction Modes of 3D Data Audiences for 3D Data Discovering 3D Assets and Decision-Making Issues Technology Requirements and Limitations Impacting Access Use Case Challenges and Outstanding Questions Recommendations for Next Steps Conclusion Notes Bibliography Chapter 7. Conclusion Jennifer Moore, Adam Rountrey, and Hannah Scates Kettler How Are 3D Data Different? Ideas from the Community Assessing Our Approach (CoP) Going Forward Notes Bibliography Glossary Biographies
£77.25
John Wiley & Sons Inc Presenting Data How to Communicate Your Message
Book SynopsisA clear easy-to-read guide to presenting your message using statistical data Poor presentation of data is everywhere; basic principles are forgotten or ignored. As a result, audiences are presented with confusing tables and charts that do not make immediate sense. This book is intended to be read by all who present data in any form. The author, a chartered statistician who has run many courses on the subject of data presentation, presents numerous examples alongside an explanation of how improvements can be made and basic principles to adopt. He advocates following four key C' words in all messages: Clear, Concise, Correct and Consistent. Following the principles in the book will lead to clearer, simpler and easier to understand messages which can then be assimilated faster. Anyone from student to researcher, journalist to policy adviser, charity worker to government statistician, will benefit from reading this book. More importantly, it will also benefit the reTrade Review�Highly recommended to a very wide audience.� (Mathematical Association of America, 1 January 2015) �Presenting data is, of course, more than a good table or an informative graphic. The words that describe the data must also be correct, clear, concise and consistent, and the author devotes a chapter to ensuring that the expert�s hard work is not undone by commentary that is too technical or too laboured to be understood by non-expert audiences.� (The Royal Statistical Society, 1 February 2015) �This book must be mandatory to any data analyst and statistician, and indeed anyone who presents numeric data. The word "numeric" is important, as non-numeric data are not discussed in the book.� (ENBIS News, 10 December 2014) �The words that describe the data, and the messages it may have, must also be correct, clear, concise and consistent, and the author devotes a chapter to ensuring that the expert�s hard work is not undone by commentary that is too technical or too laboured to be understood by non-expert audiences.� (Significance, 1 February 2014) "The book is easy to read and can be consumed in a single train journey. It is also easy to look at and the illustrations and graphics occupy as much space as the text. The author has indeed put his money where his mouth is and the graphics both illustrate his points and makes them alone. The highlight for me was in the chapter on tables where the author reduces a table containing seven columns and five rows to a two by two table which beautifully makes the point when previously it had been lost in a forest of figures... This is an excellent book and I would recommend it as essential reading to anyone who is writing a thesis or dissertation, making a presentation or writing a paper. That�s all of us." (Occupational Medicine, 15th February 2016)Table of ContentsList of Tables vii List of Figures ix Introduction xiii Preface xvii Acknowledgements xix 1 Understanding number 1 1.1 Thousands separator 2 1.2 Decimal separator 3 1.3 Level of detail in comparisons 4 1.4 Justification of data 5 1.5 Basic rounding 7 1.6 Effective rounding 9 Notes 16 2 Tables 17 2.1 Position of totals in tables 17 2.2 What is a table? 19 2.3 Reference tables 19 2.4 Summary tables 22 2.5 How tables are read 24 2.6 Layout of data in tables 25 2.7 Capital letters for table titles and headings in tables 29 2.8 Use of bold typeface 30 2.9 Use of gridlines and other lines in tables 30 Notes 31 3 Charts (bar charts, histograms, pie charts, graphs) 33 3.1 How the user interprets charts 33 3.2 Written aims for charts 35 3.3 Scale definition and display 37 3.4 Difference between bar charts and histograms 49 3.5 Pie chart principles 51 3.6 Issues with pie charts 55 3.7 Graph principles 63 3.8 Issues with graphs 64 3.9 Pictogram principles 79 3.10 Comparative charts: Multiple pies, multiple bar charts, double scale graphs 82 3.11 Graphics 88 3.12 Three-dimensional charts 90 Notes 92 4 Numbers in text 93 4.1 Numbers written as text 94 4.1.1 Correct numbers 94 4.1.2 Clear numbers 94 4.1.3 Concise numbers 95 4.1.4 Consistent numbers 96 4.2 Ordering of data 97 4.3 Technical terms 98 4.4 Plain language 100 4.5 Emotive language 102 4.6 Key messages 103 Notes 105 5 Data presentation on the Internet 107 5.1 The early years 110 5.2 Statistics on CD-ROMs 113 5.3 Data on the Internet 116 5.4 Charts on the Internet 120 5.5 Text on the Internet 128 Notes 130
£20.85
John Wiley & Sons Inc Hands On With Google Data Studio
Book SynopsisLearn how to easily transform your data into engaging, interactive visual reports! Data is no longer the sole domain of tech professionals and scientists. Whether in our personal, business, or community lives, data is rapidly increasing in both importance and sheer volume. The ability to visualize all kinds of data is now within reach for anyone with a computer and an internet connection. Google Data Studio, quickly becoming the most popular free tool in data visualization, offers users a flexible, powerful way to transform private and public data into interactive knowledge that can be easily shared and understood. Hands On With Google Data Studio teaches you how to visualize your data today and produce professional quality results quickly and easily. No previous experience is required to get started right awayall you need is this guide, a Gmail account, and a little curiosity to access and visualize data just like large businesses and organizationTable of ContentsIntroduction xv Part I Data Studio Basics 1 Chapter 1 Data Studio and the Data Citizen 3 Data, Data Everywhere 4 Accessibility of Data 4 Deriving Value from Data 5 The Role of Data Studio 7 A Brief History of Data Studio 9 Fundamentals First 12 Where to Go for Help 12 Time to Get Started! 13 Chapter 2 Cooking with Google Data Studio 15 Our First Example 15 Step 1. Select a Dish to Prepare: Visualizing a Bank Account 16 Before We Start: Things You’ll Need 19 Step 2. Let’s Go Shopping! Getting the Data 19 Step 3. Unpack the Groceries: Setting Up Google Drive 21 Step 4. Preparing the Ingredients: Working with Google Sheets 22 Step 5. Familiarizing Yourself with the Kitchen: Data Studio Home Screen and Starting a New Report 25 Step 6. Assemble the Ingredients: Connecting the Data to the Report 28 Step 7. Set the Table: Adding Your First Chart 35 Step 8. Keep Score with Scorecards 40 Step 9. It’s About Time: Building the Time-Series Chart 45 Step 10. Serving Suggestions: Finishing Touches and Sharing Your Report 50 Summary 55 Chapter 3 Enhancing Basic Graphs 57 Static Report Design 57 Before Getting Started: Things You’ll Need 59 Step 1. Copy the Report 60 Step 2. Modify the Header and Set the Date Range 61 Step 3. Modify Scorecards 63 Step 4. Modify Time-Series Chart 65 Step 5. Modify the Transactions Table 66 Step 6. Save the Report as PDF for Sharing 69 Summary 70 Chapter 4 Data Exploration with Interactive Elements 73 Building Our Workbench Example 73 Step 1. Selecting a Dish to Prepare: Exploration Workbench for a Bank Account 74 Step 2. Let’s Go Shopping! Getting the Data Set 77 Step 3. Assembling the Ingredients: Connecting Data to the Report 78 Adding Chart Components and Graphic Elements 82 Step 4. Reuse, Recycle, and Repurpose: Copying Elements from Other Reports 82 Step 5. Easy as Pie! Adding a Pie Chart 84 Step 6. Step Up to the Bar: Adding the Bar Chart 85 Step 7. Lining Things Up: Adding the Line Chart 90 Step 8. Tables, Again!? Setting Up the Table 91 Adding and Configuring Filter Controls 92 Step 9. Fun with Filters: Setting Up the Standard Filter Controls 92 Tips for Using Interactive Filters 94 Find the Answers: Filter Challenge 95 Summary 97 Part II Business and Marketing Applications 99 Chapter 5 Web Data Visualization with Google Analytics 101 Google Services for Websites and Business 102 A Brief Primer for Google Analytics 104 Using a Template to Create a New Report 106 Building the Google Analytics Report 110 Modifying the Charts 112 Working with the New Report 117 A Real-World Example: The Effects of a Beach Party in Spain on Web Traffic 117 The Limits of Demographic Data from Google Analytics 124 Now for a Side Dish: Adding a Page and Using Filters 126 Summary 132 Chapter 6 Using Google Search Console for Audience Insights 135 Search Console and Search Results Pages 136 Creating a Search Console Report with Data Studio 141 Step 1. Selecting the Dish to Prepare 142 Step 2. Let’s Go Shopping! 144 Step 3. Assemble the Ingredients 147 Step 4. Setting the Table 153 Bon Appetite! Using the Search Console Report 157 An Example for Your Soul! 161 Summary 170 Chapter 7 Viewing Local Organization Data from Google My Business 173 Google Search and the Local Organization 173 GMB: The New Home Page in the Search Results 175 What the User Sees in a GMB Listing 176 What the Owner Sees in a GMB Listing 180 Why Use Data Studio for GMB? 185 Step 1. Selecting the Dish to Prepare 187 The Actions Page 189 The Reviews Page 190 The Search Page 192 Before You Begin: What You Need to Get Started 194 Step 2. Let’s Go Shopping and Assemble the Ingredients 195 Step 3. Setting the Table 202 Report, Page, and Header Setup 203 Page Navigation Button Setup 206 Building the Actions Page 209 Building the Search Page 213 Building the Reviews Page 215 Summary 218 Part III Beyond the Office 221 Chapter 8 Getting Personal 223 Creating Your Own Data 223 Using Google Forms to Collect Your Own Data 225 Curating Data from Other Sources 248 Setting Up IFTTT to Capture Tweets 250 Preparing the Google Sheet for Data Studio 256 Create a Data Source for the Google Sheet 256 Create the New Twitter Report 258 Working with Data Generated for You 262 Music Service Tracking Example 262 Google Fit Community Connector Example 269 Community Connector Setup for Google Fit 270 Bringing It All Together 280 Created Data 280 Curated Data 283 Generated Data 284 Summary 288 Chapter 9 Going Public 291 Shared Data Sets 291 Searching for Data Sets 292 Getting Data from Kaggle 294 Using a Kaggle Account 294 Building the UFO Sightings Workbench 302 Dealing with Performance Issues in Reports 307 Using the Extract Data Connector to Speed Up Reports 307 data.world and Makeover Monday 314 Bringing It Home: Real Estate in Your Neighborhood 330 Selecting the Dish to Prepare 331 Going Shopping: Hunting for Housing Data 332 Unpacking the Groceries: Moving the File to Google Drive 337 Preparing the Ingredients: Fixing Data Formatting Issues 338 Assembling the Ingredients: Connecting the Data to the Report 340 Preparing the Meal: The Plan Meets Reality 340 Sharing Your Dish: Embedding Reports 346 Embedding a Report in a Google Site 351 Embedding a Report in a WordPress Site 353 Dealing with Pivoted Data 356 Summary 361 Chapter 10 Where Do You Go from Here? 363 Helping Your Audience See the Light 363 Annotation Ideas for Static Reports 364 Annotation Ideas for Interactive Reports 367 Providing Detailed Instructions for Users 371 Community Visualizations 375 Data Studio as an Application Development Platform 377 Exotic Ingredients and Your Pantry 383 Data Blending 383 File Upload 388 BigQuery for Big Data 390 Community Connectors 395 The Latest Dishes: New Developments and Releases 396 Summary 396 Index 399
£27.89
John Wiley & Sons Inc Minding the Machines
Book SynopsisOrganize, plan, and build an exceptional data analytics team within your organization In Minding the Machines: Building and Leading Data Science and Analytics Teams, AI and analytics strategy expert Jeremy Adamson delivers an accessible and insightful roadmap to structuring and leading a successful analytics team. The book explores the tasks, strategies, methods, and frameworks necessary for an organization beginning their first foray into the analytics space or one that is rebooting its team for the umpteenth time in search of success. In this book, you'll discover: A focus on the three pillars of strategy, process, and people and their role in the iterative and ongoing effort of building an analytics teamRepeated emphasis on three guiding principles followed by successful analytics teams: start early, go slow, and fully commitThe importance of creating clear goals and objectives when creating a new analytics unit in an organization Perfect for executives, managers, team leads, and other business leaders tasked with structuring and leading a successful analytics team, Minding the Machines is also an indispensable resource for data scientists and analysts who seek to better understand how their individual efforts fit into their team's overall results.Table of ContentsForeword xiii Introduction xvi Chapter 1 Prologue 1 For the Leader from the Business 5 For the Career Transitioner 6 For the Motivated Practitioner 6 For the Student 7 For the Analytics Leader 8 Structure of This Book 8 Why is This Book Needed? 9 Communication Gap 9 Troubles with Taylorism 10 Rinse, Report, Repeat 12 Too Fast, Too Slow 13 More Data, More Problems 14 Summary 15 Chapter 2 Strategy 17 The Role of Analytics in the Organization 20 The Analytics Playbook 20 Data and Analytics as a Culture Change 24 Current State Assessment 26 Readiness Assessment 26 Capability Modeling and Mapping 28 Technology Stack Review 32 Data Quality and Governance 34 Stakeholder Engagement 35 Defining the Future State 37 Defining the Mandate 39 Analytics Governance Model 40 Target Operating Model 42 Define Your Principles 43 Functions, Services, and Capabilities 43 Interaction Models 44 Organizational Design 48 Community of Practice 52 Project Delivery Model 55 Closing the Gap 57 Setting the Horizon 58 Establishing a Talent Roadmap 59 Consultants and Contractors 60 Change Management 62 Implementing Governance Models 64 Summary 65 Chapter 3 Process 69 Project Planning 73 Intake and Prioritization 73 Project Pipelines 77 Portfolio Project Management 80 Project Scoping and Planning 83 Scoping and Requirements Definition 86 Planning 92 Project Execution 96 Governance Structure and Communication Plan 99 Project Kickoff 102 Agile Analytics 103 Change and Stakeholder Management 106 Skeuomorphs 106 AI 101 and Project Brainstorming 107 Iterative Insights 110 Closeout and Delivery 111 Automation 112 Project Debrief 114 Summary 118 Chapter 4 People 121 Building the Team 122 Success Factors 123 Team Composition 128 Hiring and Onboarding 129 Talent Development 131 Retention 136 Departures 137 The Data Scientist Hierarchy of Needs 139 Culture 140 Innovation 145 Communication 147 Succession Planning 149 Potential Pitfalls 151 Dunning-Kruger Effect 152 Diderot Effect 153 Leading the Team 154 Data Scientists as Craftspeople 157 Team Conventions 160 Formal Meetings 162 Coffee Chats 164 Managing Conflict 167 Relationship Management 169 Owning the Narrative 175 Performance Metrics 177 Summary 181 Chapter 5 Future of Business Analytics 187 AutoML and the No‐Code Movement 189 Data Science is Dead 192 The Data Warehouse 195 True Operationalization 196 Exogenous Data 198 Edge AI 199 Analytics for Good 200 Analytics for Evil 201 Ethics and Bias 203 Analytics Talent Shortages 204 Death of the Career Transitioner 206 Chapter 6 Summary 211 Chapter 7 Coda 213 Index 215
£24.79
John Wiley & Sons Inc Data Science Handbook
Book SynopsisTable of ContentsAcknowledgment xi Preface xiii 1 Data Munging Basics 1 Introduction 1 1.1 Filtering and Selecting Data 6 1.2 Treating Missing Values 11 1.3 Removing Duplicates 14 1.4 Concatenating and Transforming Data 16 1.5 Grouping and Data Aggregation 20 References 20 2 Data Visualization 23 2.1 Creating Standard Plots (Line, Bar, Pie) 26 2.2 Defining Elements of a Plot 30 2.3 Plot Formatting 33 2.4 Creating Labels and Annotations 38 2.5 Creating Visualizations from Time Series Data 42 2.6 Constructing Histograms, Box Plots, and Scatter Plots 44 References 54 3 Basic Math and Statistics 57 3.1 Linear Algebra 57 3.2 Calculus 58 3.2.1 Differential Calculus 58 3.2.2 Integral Calculus 58 3.3 Inferential Statistics 60 3.3.1 Central Limit Theorem 60 3.3.2 Hypothesis Testing 60 3.3.3 ANOVA 60 3.3.4 Qualitative Data Analysis 60 3.4 Using NumPy to Perform Arithmetic Operations on Data 61 3.5 Generating Summary Statistics Using Pandas and Scipy 64 3.6 Summarizing Categorical Data Using Pandas 68 3.7 Starting with Parametric Methods in Pandas and Scipy 84 3.8 Delving Into Non-Parametric Methods Using Pandas and Scipy 87 3.9 Transforming Dataset Distributions 91 References 94 4 Introduction to Machine Learning 97 4.1 Introduction to Machine Learning 97 4.2 Types of Machine Learning Algorithms 101 4.3 Explanatory Factor Analysis 114 4.4 Principal Component Analysis (PCA) 115 References 121 5 Outlier Analysis 123 5.1 Extreme Value Analysis Using Univariate Methods 123 5.2 Multivariate Analysis for Outlier Detection 125 5.3 DBSCan Clustering to Identify Outliers 127 References 133 6 Cluster Analysis 135 6.1 K-Means Algorithm 135 6.2 Hierarchial Methods 141 6.3 Instance-Based Learning w/ k-Nearest Neighbor 149 References 156 7 Network Analysis with NetworkX 157 7.1 Working with Graph Objects 159 7.2 Simulating a Social Network (ie; Directed Network Analysis) 163 7.3 Analyzing a Social Network 169 References 171 8 Basic Algorithmic Learning 173 8.1 Linear Regression 173 8.2 Logistic Regression 183 8.3 Naive Bayes Classifiers 189 References 195 9 Web-Based Data Visualizations with Plotly 197 9.1 Collaborative Aanalytics 197 9.2 Basic Charts 208 9.3 Statistical Charts 212 9.4 Plotly Maps 216 References 219 10 Web Scraping with Beautiful Soup 221 10.1 The BeautifulSoup Object 224 10.2 Exploring NavigableString Objects 228 10.3 Data Parsing 230 10.4 Web Scraping 233 10.5 Ensemble Models with Random Forests 235 References 254 Data Science Projects 257 11 Covid19 Detection and Prediction 259 Bibliography 275 12 Leaf Disease Detection 277 Bibliography 283 13 Brain Tumor Detection with Data Science 285 Bibliography 295 14 Color Detection with Python 297 Bibliography 300 15 Detecting Parkinson’s Disease 301 Bibliography 302 16 Sentiment Analysis 303 Bibliography 306 17 Road Lane Line Detection 307 Bibliography 315 18 Fake News Detection 317 Bibliography 318 19 Speech Emotion Recognition 319 Bibliography 322 20 Gender and Age Detection with Data Science 323 Bibliography 339 21 Diabetic Retinopathy 341 Bibliography 350 22 Driver Drowsiness Detection in Python 351 Bibliography 356 23 Chatbot Using Python 357 Bibliography 363 24 Handwritten Digit Recognition Project 365 Bibliography 368 25 Image Caption Generator Project in Python 369 Bibliography 379 26 Credit Card Fraud Detection Project 381 Bibliography 391 27 Movie Recommendation System 393 Bibliography 411 28 Customer Segmentation 413 Bibliography 431 29 Breast Cancer Classification 433 Bibliography 443 30 Traffic Signs Recognition 445 Bibliography 453
£119.70
John Wiley & Sons Inc Sports and Technology Have the Power to Change
Book SynopsisDiscover how the explosions in data analytics, AI, and digital communication are benefiting sports and sports fans around the world In Sports and Technology Have The Power To Change The World: Driving Positive Change Through The Use of Data and AI, the Director of Microsoft Sports, Jon Flynn, delivers an insightful new take on the transformative power of sport and its ability to unite people, break down barriers, and generate positive change. The author explains the critical role that technology has played in growing the impact of sporting events and enabling social change while fostering community improvement. In the book, you'll explore many of the ways in which sports, enabled by new tech, have made significant contributions to society and promoted individual development, health, and wellbeing. You'll also find: Discussions of green technologies and climate and sustainability initiatives linked to sport, with a case study about the 2022 Beijing Winter OlympicsExplorations of the impact of advanced data analytics, with a case study focusing on the 2013 NBA Final matchup between the Miami Heat and the San Antonio SpursHow sport scientists are optimizing player performance Perfect for anyone interested in the intersection of sport, society, and technology, Sports and Technology Have The Power To Change The World is an easy to read and endlessly fascinating look at how the unique combination of athletics and tech makes the world a better place.
£22.94
Kogan Page Ltd The Enterprise Big Data Framework
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
£148.50
Kogan Page Ltd Delivering Data Analytics
Book SynopsisNicholas Kelly is a principal at G&K Consulting, based in Bonney Lake, Washington. He is a leader in analytics adoption having designed and developed dashboards for some of the world's largest companies, from global banks to Formula 1 teams. He is a frequent speaker at international conferences, has trained thousands of professionals in data visualization and analytics adoption and is the inventor of the Dashboard Wireframe KitTrade Review"Over the many years I have worked in data analytics the field has grown significantly. It's no longer enough to deliver accurate numbers and charts: we need to consider business value, governance, adoption, story-telling and even corporate culture. Nick Kelly's book covers all that ground and more." * Donald Farmer, Principle, Tree Hive Strategy *"Achieving real business impact with data goes far beyond technical considerations - you must focus on the human considerations. Through practical examples and real-world stories, Nick has crafted a book that will teach you to capitalize on the human side of data analytics and deliver business-changing results." * David Langer, Founder, Dave on Data *"If you are looking to build an analytic capability or wondering how to improve one, this book covers the why what and how in a down to earth narrative. If you want to fast track from lessons learned and get your program running from the get-go, read this book first." * Akihiko Katayama, Chief Technology Officer, BaronsAI *Table of Contents Chapter - 01: Insight mindset; Chapter - 02: Strategy and planning; Chapter - 03: UX principles; Chapter - 04: Requirements gathering; Chapter - 05: Data assessment; Chapter - 06: The agile process; Chapter - 07: Storytelling; Chapter - 08: Crafting the vision; Chapter - 09: Managing change; Chapter - 10: Adoption and ownership; Chapter - 11: Training and documentation; Chapter - 12: Launch
£87.30
Kogan Page The Practical Guide to Digital Transformation
Book SynopsisDr Antonio Weiss, based in London, UK, is a Senior Partner at The PSC, an award-winning public service consultancy specialising in user-centred design, digital, strategy and delivery. He has advised the Office for Artificial Intelligence, the UK Space Agency and NHSx as well as numerous other pioneering digital organizations and frequently trains leaders to become digital transformation experts. He is also an Affiliated Researcher at the University of Cambridge's Digital State programme and the co-founder of Thomas Clipper, an e-commerce lifestyle brand for men featured in GQ, The Guardian and The Telegraph.Trade Review"Refreshingly free of waffle and ego, this is an incredibly valuable guide - in fact almost a recipe - for launching and landing meaningful digital and business transformation. Every page shares precision insights and immediately actionable suggestions with a simplicity and clarity that only comes from many years of walking the walk. As useful for someone starting their digital transformation journey as one despairing about their progress to date!" * Pete Herlihy, Lead Product Manager, UK Government Digital Service *"This is a great book that will be of huge help to those involved in digitally enabled transformation. It is intensely practical, with many good case studies, but also very readable. The book is aimed squarely at those responsible for initiating and leading the change rather than technical experts. From me it comes strongly recommended. I wish it had been written a long time ago." * Lord Bob Kerslake, former Head of the UK Civil Service *"Digitalisation will have a major impact on the way services are provided and how business is conducted. Digital Transformation can seem like a daunting challenge to any business leader and it is essential that we all build our understanding and knowledge as these major changes take place. This excellent practical guide, written by Antonio Weiss will help you achieve this and de-risk your transformation programme. The guide gives you the tools and advice that you will need to succeed as well as being a valuable resource for those leading any digital change process." * Sir Ian Carruthers, Chancellor of the University of the West of England and former NHS Chief Executive *"Organizations globally are spending billions on digital transformation right now; for many, it is one of their very largest investment areas. But it is easy to waste money on technology, consulting and internal costs because organisations and leaders don't know what they are really trying to do, or how to achieve their goals. The Practical Guide to Digital Transformation is truly practical, with great examples, case studies and tips that will be of value to anyone tasked with delivery. I particularly liked the "what you might say in your next meeting" list at the end of each chapter, and this book is destined to become a well-used friend to many senior managers, consultants and business students." * Peter Smith, Procurement expert and author of Bad Buying *"Organisations are more aware than ever of the need to transform themselves for the digital age. But many, perhaps most, still struggle. The Practical Guide to Digital Transformation is the ideal companion for any company serious about adaptation. Written by a leading digital transformation expert, it provides the ideas and vocabulary to bring about change, illuminated by incisive case studies. It is written in user-friendly language, guiding the reader through the "whys" and "hows" of making change happen. This outstanding guide is essential reading for decision-makers, digital leaders, and practitioners seeking to ensure that their organisations thrive now and in the future." * Dr Tanya Filer, Digital State Project Lead, Bennett Institute for Public Policy, University of Cambridge and founder of StateUp *"The Practical Guide to Digital Transformation is a really excellent, step by step primer to help your organisation get the very best from digital technology. It is clear but not dumbed-down. It will expand the thinking of new and experienced digital leaders alike, and gives concrete, actionable ways of bringing your organisation with you." * Phil Buckley, Prix Jeunesse and BIMA award-winning Digital Product Manager *"This book provides a unique, pragmatic, real-world guide to digital transformation. Each chapter unlocks different aspects, viewpoints and the considerations needed to embed change in a multitude of environments. The book is pitched to all audiences, providing the tools needed for anyone interested or actively involved in digital transformation. Antonio has managed to pack the learning from his many years as a consultant into this wonderful book." * Gary McAllister, Chief Technology Officer for NHS London and author of An Introduction to Digital Healthcare in the NHS *"For anyone trying to implement transformative change in their organisation - this is a complete must have. It's a remarkably waffle-free book that shows how to put theory into practice in the most practical of ways - with real world examples of where it's worked and where it hasn't. It's genuinely a breath of fresh air and an invaluable read for anyone interested in using digital principles and tools to transform their organisation." * Tom Lillywhite, Director of Digital Transformation, UK Labour Party *Table of Contents Chapter - 00: Introduction; Section - ONE: Getting the strategy and the roadmap right; Chapter - 01: What is a digital strategy?; Chapter - 02: Choosing your strategic delivery vehicle; Chapter - 03: How to do a digital roadmap; Section - TWO: Making change happen; Chapter - 04: Ways of working; Chapter - 05: Senior and organisational buy-in; Chapter - 06: Funding digital; Section - THREE: Doing digital; Chapter - 07: Understanding your users; Chapter - 08: Doing the bare minimum; Chapter - 09: Building new services; Chapter - 10: Buying technology; Chapter - 11: The cloud, APIs and open-source; Chapter - 12: Using data science to inform decision-making; Chapter - 13: Stimulating innovation; Section - FOUR: Future-proofing; Chapter - 14: Protecting and defending your organisation; Chapter - 15: The ethical digital organisation; Chapter - 16: Upskilling and bringing in new talent; Chapter - 17: Defining the next horizon
£28.49
Kogan Page Ltd Be Data Driven
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"Jordan Morrow has done it again. This flows on perfectly from his first book, Be Data Literate, and creates a realistic and achievable pathway for organizations to become data driven. And, of course, at the center of it is people-a man after my own data heart! I hope that wasn't a spoiler alert..." * Susan Walsh, The Classification Guru and author of Between the Spreadsheets *"Understanding the basics of data science and AI is key to succeeding today. This book provides key insights into preparing better for a more data-driven future. A fascinating read for anyone looking to stay on top of how data science is revolutionizing just about everything. Jordan Morrow has done an amazing job taking a complex topic and synthesizing it for all to understand. Highly recommended." * Manuj Aggarwal, Founder and Chief Innovation Officer, TetraNoodle *"Jordan Morrow delivers a great follow-up to his first book, Be Data Literate. The phrase 'data driven' has evolved into a meaningless term and hollow buzzword through misuse and misunderstanding. With his new book, Jordan helps us understand what it actually means and why it matters. He then offers a simple, practical approach to help companies get there. For organizations and leaders who know there is value in their data but have struggled to unleash it, this is the book for you." * Brian Ferris, Chief Data, Analytics and Technology Officer, Loyalty NZ *"One of the pioneers in the field of data." * Jimmy Rex, investor, author and podcast host of The Jimmy Rex Show *"Jordan Morrow is a true authority on data literacy and the opportunity for organizations that want to become data driven. In this book, he covers all the critical ingredients organizations need to become truly data driven in a world that's been transformed by the Covid-19 pandemic. His second book is a great guide for leaders and practitioners alike and provides the necessary tools for transforming your business into a data-driven powerhouse." * Eva Murray, Lead Evangelist EMEA, Snowflake *"Jordan Morrow has followed his brilliant first book, Be Data Literate, with this masterpiece. It's a comprehensive and insightful book in which he shares his passion and experience in the data space to give everyone the power to harness the true power of data." * Bernard Marr, best-selling author, futurist, business and technology adviser *"This book aims high, with strong data culture principles and data literacy foundations, while maintaining significant focus on the strategy that guides data initiatives to successful completion and mission success. Failure is not an option in this data-intensive world. Be Data Driven is an excellent launchpad and mission guidebook for your organization's data-driven journey." * Kirk Borne, Chief Science Officer, DataPrime *"Jordan Morrow writes brilliantly, engages with the reader and, most crucially, demystifies the world of data and analytics. It all makes sense, even for those not familiar with the subject or struggling to understand. This book is a terrific start and for those passionate about business and success, essential!" * Mike Roe, CEO, Tensense.ai *Table of Contents Section - ONE: Foundational; Chapter - 01: A data-driven world; Chapter - 02: The impact of Covid-19 on organizations and data; Chapter - 03: Technologies advancing data and analytics, and the need for the human element; Chapter - 04: What is a data-driven organization?; Section - TWO: Gaps; Chapter - 05: Foundational skills gaps; Chapter - 06: Pillars of an organizational data strategy; Chapter - 07: The gap in leadership; Chapter - 08: The biggest hurdle: culture; Section - THREE: Building your data-driven organization; Chapter - 09: Decide your outcome; Chapter - 10: Build your strategy; Chapter - 11: Be data driven—start your journey!; Chapter - 12: References
£72.00
Kogan Page Ltd Talent Intelligence
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
£85.50
Kogan Page Ltd Marketing Analytics
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;
£85.50
Kogan Page Ltd ValueDriven Data
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
£85.50
Kogan Page Ltd Accelerated Digital Transformation
Book SynopsisNeetan Chopra is a C-suite level senior digital leader with a track record of driving change and innovation at established enterprises. In his three-decade career, he has led digital transformation in global enterprises operating diverse business models across the aviation, travel, logistics, retail, food and beverage, real estate and entertainment industry sectors. He is currently Chief Digital and Information Officer at IndiGo (InterGlobe Aviation Ltd) in Gurugram, India; prior to this, he also held roles at Dubai Holding, Emirates Airlines and Accenture. He sits on the boards of multiple tech start-ups, has built multiple global innovation labs including at Oxford University and Carnegie Mellon University and is an Adjunct Professor at Botho University, Botswana. He is recognized by the Constellation Research Business Transformation 150 as one of the world's top global executives leading innovative business transformation efforts in their organizations.Trade Review"An insightful, practitioners' guide full of pragmatic ideas and valuable learnings from decades of experience. A must-read for executives vying for industry leadership in digital." * Joydeep Sengupta, Senior Partner, McKinsey & Company *"More than 80% of digital transformation projects fail inside large organizations not through lack of trying. Every organization looking to crack the code for success must read Neetan Chopra's book. By taking a practitioner's point of view, he draws from three decades of experience to provide a proven methodology known as the Honeycomb Framework. Accelerated Digital Transformation is a must-read book for digital leaders." * R ‘Ray’ Wang, Founder and Chairman, Constellation Research *"We live in a time where the fog of technology is often overwhelming, difficult to dissect and changing so rapidly it becomes difficult to have confidence in our decision making. Neetan Chopra takes the mystery out of creating successful digital transformations for companies and their clients in a way that brings light and clarity to an otherwise challenging process. The insights he has gathered through his years of technology experience provide the keys for those who are looking to unlock value during these extraordinary times." * Tim Kobe, Founder and CEO, Eight Inc. *"Every enterprise must succeed on a digital journey to remain relevant, serve customers and add value. Accelerated Digital Transformation provides you with a pragmatic guide and framework to achieve progress and success. I appreciate Neetan Chopra's storytelling approach and many lessons of everyday role models delivering extraordinary customer service. These stories demonstrate the Uplifting Service ethos that earns customer praise and social media attention." * Ron Kaufman, New York Times Bestselling Author, Uplifting Service: The Proven Path to Delighting Your Customers, Colleagues, and Everyone Else You Meet *"Only if one has lived through or, even better, driven digital transformation of a major company, can one seriously understand what it means to write a book about it that is worth reading. Neetan Chopra is exactly one of these few leaders who has lived through such digital transformations and knows exactly what it means, what works and what doesn't. Chopra's book is probably one of the very few books about digital transformation really worth reading, as it is not yet another consultant textbook, far remote from reality, but written by true experience and painful learnings." * Patrick Naef, Managing Partner, Boyden *"Accelerated Digital Transformation deftly fuses practical examples and actionable insights from Neetan Chopra's vast digital experience, with deep foundational learning, borne out of his global academic pursuits. Chopra's storytelling approach, coupled with innovative interventions, such as co-authoring a chapter with an AI-bot, make the book both engaging and first-of-a-kind read. We highly recommend this book, for anyone looking to navigate the ever-evolving digital landscape and staying ahead of the game in the digital era." * Prof Dr Reinhard Jung, Dean, School of Management, University of St Gallen & Prof Dr Ulrike Baumöl, Executive Director, Executive MBA HSG in Business Engineering, University of St Gallen *"Having seen firsthand how Neetan Chopra executes successful digital transformation where others have struggled, I am grateful he has taken the time to write down his framework. His deliberate 'cell by cell' strategy is what it takes to assess, learn and succeed in this complicated mix of technical, business and cultural transformation. And a bonus, he demonstrates learning with this framework by co-authoring one chapter with an AI bot... brilliant!" * Jana Eggers, CEO, Nara Logics *"All transformations, digital or otherwise, are in essence a human journey of change. I like Chopra's storytelling, a colloquial approach to the intimidating topic of digital transformation for business leaders. This will connect with people across all walks of life, demystifying what it takes to thrive in the digital era and create exponential outcomes. The six global experts, the honeycomb archetypes, provide an additional human touch, sharing their experiences and wisdom, using Chopra's Accelerated Digital Transformation as a platform. A must read." * Raj Jain, Former CEO, Bennett Coleman & Co Limited (Times Group) *"Organizations need simple frameworks by which they can plan their digital transitions. Neetan Chopra's efforts on achieving this through a well thought out 'Honeycomb Framework' is commendable. His building blocks of digital transformation provide a succinct and clear framework through which readers can apply these in the context of their organizations. The future is about organizations which will survive and thrive in a digitally driven business ecosystem. Chopra's book will help organizations survive and thrive in a VUCA world." * Krishnakumar Natarajan, Co-founder & Former Executive Chairperson, LTIMindtree *"Chopra's new book helps you reduce bad friction into a manageable hexagon comb while introducing good friction to sweeten the honey from digital transformation." * Soon Yu, Author, Friction: Adding Value by Making People Work For It *Table of Contents Chapter - 01: Breaking organizational inertia; Chapter - 02: Working the Honeycomb – disrupt phase; Chapter - 03: Working the Honeycomb – digital capabilities; Chapter - 04: Accelerated possibilities; Chapter - 05: Honeycomb as a platform; Chapter - 06: The Honeycomb hacks; Chapter - 07: Reflections;
£87.30
Kogan Page Ltd Be Data Analytical
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;
£72.00
Kogan Page Ltd People and Data
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;
£85.50
Kogan Page Ltd DataDriven HR
Book SynopsisBernard Marr is one of the leading voices in Technology and Innovation. A futurist and strategic performance consultant, he has advised many of the world's best-known organizations on their business and data strategies. A frequent keynote speaker, he also writes on the topic of data and analytics for various publications including Forbes and the Huffington Post. Bernard Marr is also the author of Data Strategy (2021) and The Intelligence Revolution (2020) published by Kogan Page.Trade Review"Without a doubt human capability (talent + leadership + organization + HR) increasingly delivers value to all stakeholders. This excellent book provides business and HR leaders the information required to improve decision making. Bernard's insights on analytics and AI will be the keys for progress." * Dave Ulrich, Rensis Likert Professor, Ross School of Business, University of Michigan Partner, The RBL Group *"If anyone was going to publish a book about the impact of the latest technology developments such as AI on the field of HR and People Analytics my bets were on Bernard Marr. And you won't be disappointed. The book offers a deep dive into the world of data of every kind, every possible use case, honest overview of technology and important considerations. It has never been more critical to educate ourselves about it." * Maja Luckos, VP, Employee Success, Salesforce *"This book propelled me into a world of possibilities for HR leaders in embracing the 'intelligence revolution' to shape people strategies that add value to their organizations and their people. It's enlightened me to the power of AI-enabled HR and how I might use it, and it's made me want to learn more. This is a must read for all HR leaders." * Linda Sleath, Group HR Director, Topps Tiles Plc. *"Data-Driven HR strikes a nice balance between exploring emerging trends in people analytics while primarily serving as a practical guide to HR professionals at any stage of their data journey. The second edition seamlessly weaves AI into a narrative that's easy to engage with and is packed full of examples that bring the theories to life." * Mark Ferrie, People Analytics Director, Meta *"Data-Driven HR is a terrific overview of the enormous world of people analytics and AI. For people trying to understand this important space, this book shows you the way." * Josh Bersin, Global Industry Analyst and CEO of The Josh Bersin Company *"Data, analytics and AI provides to elevate HR from its traditional role as a support function to one of a strategic partner creating value for the enterprise, its customers and its employees. There's a well-thumbed copy of the first edition of Data Driven HR on my bookshelf, and in this timely update Marr, one of the most knowledgeable people on the topic, explains how data and AI can enable HR to drive better decision making about people, deliver an enhanced service to employees; and make HR processes more efficient." * David Green, Managing Partner at Insight222, co-author of Excellence in People Analytics, and host of the Digital HR Leaders podcast. *"Bernard Marr has once again delivered an indispensable guide to harnessing the power of data, analytics and AI in HR. This updated edition thoroughly captures the latest innovations shaping human resources while still being accessible for HR professionals at any level. Through compelling examples and clear frameworks, Marr demonstrates how to drive business value through evidence-based talent practices. This is a must-read playbook for any HR leader looking to build capabilities in data-driven decision-making." * Professor Max Blumberg, PhD, University of Leeds *"This is a great guide for HR professionals who are grappling with the transition to becoming data led. It's easy to read, and with real examples and case studies across the employee lifecycle, it's also a pragmatic resource to have in your HR toolkit." * Ashish Sinha Korn Ferry Head of People Analytics, AI & Strategy EMEA Practice Leader *"AI is transforming the world of work and our personal lives. With a people-centric approach, Bernard Marr demystifies data driven AI enabled HR with context, thought provoking insights and examples of AI at the time this book was written. We all have a role to play when it comes to this rapidly evolving space as the output of AI will be a reflection of our culture and values. Staying on top of leading practices, lessons learned, emerging regulations and standards is critical so we can unlock AI's potential and value add to the business, our customers and employees while minimizing risk. This book sets the foundation so we can do just that!" * Terilyn Juarez Monroe, Terilyn Juarez Monroe, Chief People Officer *"Data-Driven HR is an indispensable resource for Career Services professionals looking to equip their students with cutting-edge strategies in today's competitive job market. This comprehensive book offers invaluable insights into recruitment and candidate selection, employer branding, pinpointing the most effective recruitment channels, and harnessing AI-enhanced automation to identify and assess the best candidates for businesses. It's a game-changer for career advisors committed to empowering their students with the knowledge and skills needed to excel in the evolving world of talent acquisition and HR." * Dr. Amber Wigmore Álvarez, Associate Professor, IE Business School and IE University *Table of Contents Chapter - 00: Preface; Section - ONE: Data, Analytics and AI in HR; Chapter - 01: How data and AI are transforming HR; Chapter - 02: How data and AI have come to revolutionise HR; Chapter - 03: The Data, Analytics and AI tools available to HR; Section - TWO: Data-Driven and AI-enabled HR in Practice; Chapter - 04: Better HR insights and decision-making; Chapter - 05: Recruitment and candidate selection; Chapter - 06: Employee Onboarding; Chapter - 07: Performance Monitoring and Management; Chapter - 08: Employee Training and Development; Chapter - 09: Performance monitoring and management; Chapter - 10: Identify the use cases; Chapter - 11: Building skills and aligning culture; Section - THREE: Making data-driven and AI enabled HR happen; Chapter - 12: Identifying the use cases for your organization; Chapter - 13: The future of HR
£87.30
Kogan Page Business Analytics with Python
Book SynopsisBowei Chen is Associate Professor of Marketing Analytics and Data Science at the Adam Smith Business School, University of Glasgow, UK. He is also the Programme Director of the MSc in Finance and Management and an ESRC IAA Reviewer. Gerhard Kling is Professor in Finance at the University of Aberdeen, UK. He has worked in higher education for over 18 years (SOAS, University of Southampton, UWE, Utrecht University).
£132.30
