Data capture and analysis Books

Book Synopsis

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Book Synopsis

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

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

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Book SynopsisStatistical methods are a key tool for all scientists working with data, but learning the basics continues to challenge successive generations of students. This accessible textbook provides an up-to-date introduction to the classical techniques and modern extensions of linear model analysis-one of the most useful approaches for investigating scientific data in the life and environmental sciences. While some of the foundational analyses (e.g. t tests, regression, ANOVA) are as useful now as ever, best practice moves on and there are many new general developments that offer great potential. The book emphasizes an estimation-based approach that takes account of recent criticisms of over-use of probability values and introduces the alternative approach that uses information criteria.This new edition includes the latest advances in R and related software and has been thoroughly road-tested over the last decade to create a proven textbook that teaches linear and generalized linear model analTrade ReviewReview from previous edition The book is suitable for undergraduate and graduate students, researchers and practitioners in biological sciences. I found it refreshing and worthy of wide use. * Basil Jarvis, The Biologist *[T]his book is of great interest ... it is important to evaluate its value as a teaching tool for R for biologists. ... [T]he book's strength is that it takes an applied scientist through the necessary basic statistics, and shows step by step how to work with real data. The New Statistics with R is, furthermore, a great textbook for computer exercise sessions in any introductory statistical class (especially for the life sciences). With its help, one should be able to design a very attractive course for both applied and more theoretical students. * Krzysztof Bartoszek, Systematic Biology *... overall the book gives useful, ecumenical, and reliable statistical advice. I would recommend it for courses that are trying to equip students who already know elementary statistics with the basic tools they need to understand and perform analyses of real, messy data. * Ben Bolker, Quarterly Review of Biology *Table of Contents1: Introduction 2: Motivation 3: Description 4: Reproducible Research 5: Estimation 6: Linear Models 7: Regression 8: Prediction 9: Testing 10: Intervals 11: Analysis of Variance 12: Factorial Designs 13: Analysis of Covariance 14: Linear Model Complexities 15: Generalized Linear Models 16: GLMs for Count Data 17: Binomial GLMs 18: GLMs for Binary Data 19: Conclusions 20: A Very Short Introduction to R

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Book SynopsisIdeal for developers and data scientists, this book gets you creating packages ASAP, then shows you how to get progressively better over time. You'll learn to focus on what you want your package to do, rather than thinking about package structure.

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Book SynopsisThe R language is widely used by statisticians for data analysis, and the popularity of R programming has therefore increased substantially in recent years. The emerging Internet of Things (IoT) gathers increasing amounts of data that can be analyzed to gain useful insights into trends.R for Data Analysis in easy steps, 2nd edition has an easy-to-follow style that will appeal to anyone who wants to produce graphic visualizations to gain insights from gathered data. The book begins by explaining core programming principles of the R programming language, which stores data in vectors from which simple graphs can be plotted. Next, it describes how to create matrices to store and manipulate data from which graphs can be plotted to provide better insights. This book then demonstrates how to create data frames from imported data sets, and how to employ the Grammar of Graphics to produce advanced visualizations that can best illustrate useful insights from your data.R for Data Analysis in easy steps, 2nd edition contains separate chapters on the major features of the R programming language. There are complete example programs that demonstrate how to create Line graphs, Bar charts, Histograms, Scatter graphs, Box plots, and more. The code for each R script is listed, together with screenshots that illustrate the actual output when that script has been executed. The free, downloadable example R code is provided for clearer understanding. By the end of this book you will have gained a sound understanding of R programming, and be able to write your own scripts that can be executed to produce graphic visualizations for data analysis. You need have no previous knowledge of any programming language, so it''s ideal for the newcomer to computer programming.Updated for the latest version of R.

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Book SynopsisTable of Contents1. Introduction2. Providing Structure to Unstructured Data3. Identification, Deidentification, and Reidentification4. Metadata, Semantics, and Triples5. Classifications and Ontologies6. Introspection7. Data Integration and Software Interoperability8. Immutability and Immortality9. Assessing the Adequacy of a Big Data Resource10. Measurement11. Indispensable Tips for Fast and Simple Big Data Analysis12. Finding the Clues in Large Collections of Data13. Using Random Numbers to Bring Your Big Data Analytic Problems Down to Size14. Special Considerations in Big Data Analysis15. Big Data Failures and How to Avoid (Some of) Them16. Legalities17. Data Sharing18. Data Reanalysis: Much More Important than Analysis19. Repurposing Big Data

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Book SynopsisTable of Contents1. Introduction to Text and Sentiment Data Analysis 2. Natural Language Processing and Sentiment Analysis: Perspectives from Computational Intelligence 3. Applications and Challenges of Sentiment Analysis in Real Life Scenarios 4. Emotions Recognition of Students from Online and Offline Texts 5. Online Social Network Sensing Models 6. Identifying Sentiments of Hate Speech using Deep Learning 7. An Annotation System to Summarize Medical Corpus using Sentiment based Models 8. Deep learning-based Dataset Recommendation System by employing Emotions 9. Hybrid Deep Learning Architecture Performance on Large English Sentiment Text Data: Merits and Challenges 10. Human-centered Sentiment Analysis 11. An Interactive Tutoring System for Older Adults - Learning with New Apps 12. Irony and Sarcasm Detection 13. Concluding Remarks

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Book SynopsisThis accessible guide to data modeling introduces basic probabilistic concepts, gradually building toward state-of-the art data modeling and analysis techniques. Aimed at students and researchers in the sciences, the text is self-contained and pedagogical, including practical examples and end of chapter problems.Table of ContentsPart I. Concepts from Modeling, Inference, and Computing: 1. Probabilistic modeling and inference; 2. Dynamical systems and Markov processes; 3. Likelihoods and latent variables; 4. Bayesian inference; 5. Computational inference; Part II. Statistical Models: 6. Regression models; 7. Mixture models; 8. Hidden Markov models; 9. State-space models; 10. Continuous time models*; Part III. Appendix: Appendix A: Notation and other conventions; Appendix B: Numerical random variables; Appendix C: The Kronecker and Dirac deltas; Appendix D: Memoryless distributions; Appendix E: Foundational aspects of probabilistic modeling; Appendix F: Derivation of key relations; References; Index.

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Book SynopsisA hands-on textbook teaching how to carry out large-scale data analytics and implement machine learning solutions for big data. Including copious real-world examples, it offers a coherent teaching package with lab assignments, exercises, solutions for instructors, and lecture slides.Trade Review'With the growing ubiquity of large and complex datasets, MapReduce and Spark's dataflow programming models have become mission-critical skills for data scientists, data engineers, and ML engineers. Triguero and Galar leverage their extensive teaching experience on this topic to deliver this tour de force deep dive into both the technical concepts and programming knowhow needed for such modern large-scale data analytics. They interleave intuitive exposition of the concepts and examples from data engineering and classical ML pipelines with well-thought-out hands-on code and outputs. This book not only shows how all this knowledge is useful in practice today but also sets up the reader to be able to successfully 'generalize' to future workloads.' Arun Kumar, University of California, San DiegoTable of ContentsPart I. Understanding and Dealing with Big Data: 1. Introduction; 2. MapReduce; Part II. Big Data Frameworks: 3. Hadoop; 4. Spark; 5. Spark SQL and DataFrames; Part III. Machine Learning for Big Data: 6. Machine Learning with Spark; 7. Machine Learning for Big Data; 8. Implementing Classical Methods: k-means and Linear Regression; 9. Advanced Examples: Semi-supervised, Ensembles, Deep Learning Model Deployment.

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Book SynopsisData Analytics in Football provides students, researchers, and coaches with a firm grounding in the principles of modern performance analysis. It offers an insight into the use of positional data, exploring how they can be collected, modeled, analyzed, and interpreted. Introducing cutting-edge methods, the book challenges long-held assumptions and encourages a new way of thinking about football analysis. The book seeks to define the role of positional data in football match analysis by exploring topics such as the following: What is positional data analysis, and how did it emerge from conventional match analysis? How can positional data be collected, and which technologies can be used? What key performance indicators based on positional data should be used? How can traditional match analysis be complemented by using positional data and advanced KPIs? How can these new methods evolve in the future? Based on data collectTable of Contents1. Where Is the Revolution? 2. A Historical Perspective on Positional Data 3. Technological Background 4. Collecting Data in the Bundesliga 5. In Search of the Holy Grail 6. Betting and Sports Analytics 7. Match Intensity 8. From Media to Storytelling 9. Key Properties of Long-Term Success in Football 10. The Key to Success 11. Reasons For Dominance 12. FCB Versus FCB 13. Home Advantage 14. Managerial Influence 15. All on Attack 16. Laws of a Derby 17. Women Vs. Men – Draw in Tactical Performance 18. Experimental Tactics Research 19. Positional Data Meet Sport Psychology 20. Summary

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

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

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

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Book SynopsisResearch on the problem of clustering tends to be fragmented across the pattern recognition, database, data mining, and machine learning communities. Addressing this problem in a unified way, Data Clustering: Algorithms and Applications provides complete coverage of the entire area of clustering, from basic methods to more refined and complex data clustering approaches. It pays special attention to recent issues in graphs, social networks, and other domains.The book focuses on three primary aspects of data clustering: Methods, describing key techniques commonly used for clustering, such as feature selection, agglomerative clustering, partitional clustering, density-based clustering, probabilistic clustering, grid-based clustering, spectral clustering, and nonnegative matrix factorization Domains, covering methods used for different domains of data, such as categorical data, text data, multimedia data, graph data, biologicTable of ContentsAn Introduction to Cluster Analysis. Feature Selection for Clustering: A Review. Probabilistic Models for Clustering. A Survey of Partitional and Hierarchical Clustering Algorithms. Density-Based Clustering. Grid-Based Clustering. Non-Negative Matrix Factorizations for Clustering: A Survey. Spectral Clustering. Clustering High-Dimensional Data. A Survey of Stream Clustering Algorithms. Big Data Clustering. Clustering Categorical Data. Document Clustering: The Next Frontier. Clustering Multimedia Data. Time Series Data Clustering. Clustering Biological Data. Network Clustering. A Survey of Uncertain Data Clustering Algorithms. Concepts of Visual and Interactive Clustering. Semi-Supervised Clustering. Alternative Clustering Analysis: A Review. Cluster Ensembles: Theory and Applications. Clustering Validation Measures. Educational and Software Resources for Data Clustering. Index.

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Book SynopsisA beginner's guide to the corpus analysis of style in texts

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Book SynopsisTell your story and show it with data, using free and easy-to-learn tools on the web. This introductory book teaches you how to design interactive charts and customized maps for your website, beginning with simple drag-and-drop tools such as Google Sheets, Datawrapper, and Tableau Public.

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

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Book SynopsisA flood of data means that many of the challenges in biology are now challenges in computing. Bioinformatics, the application of computational techniques to analyse the information associated with biomolecules on a large-scale, has now firmly established itself as a discipline in molecular biology, and encompasses a wide range of subject areas from structural biology, genomics to gene expression studies. In this text we provide an introduction and overview of the current state of the field. We discuss the main principles that underpin bioinformatics analyses, look at the types of biological information and databases that are commonly used, and finally examine some of the studies that are being conducted, particularly with reference to transcription regulatory systems. The aims of bioinformatics are threefold. First, at its simplest bioinformatics organises data in a way that allows researchers to access existing information and to submit new entries as they are produced, e.g. the Protein Data Bank for 3D macromolecular structures . While data-curation is an essential task, the information stored in these databases is essentially useless until analysed. Thus the purpose of bioinformatics extends much further. The second aim is to develop tools and resources that aid in the analysis of data. For example, having sequenced a particular protein, it is of interest to compare it with previously characterised sequences. This needs more than just a simple text-based search and programs such as FASTA and PSI-BLAST must consider what comprises a biologically significant match. Development of such resources dictates expertise in computational theory as well as a thorough understanding of biology. The third aim is to use these tools to analyse the data and interpret the results in a biologically meaningful manner. Traditionally, biological studies examined individual systems in detail, and frequently compared them with a few that are related. In bioinformatics, we can now conduct global analyses of all the available data with the aim of uncovering common principles that apply across many systems and highlight novel feature.

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Book SynopsisGain practical insights by exploiting data in your business to build advanced predictive modeling applications Key Features A step-by-step guide to predictive modeling including lots of tips, tricks, and best practices Learn how to use popular predictive modeling algorithms such as Linear Regression, Decision Trees, Logistic Regression, and Clustering Master open source Python tools to build sophisticated predictive models Book Description Social Media and the Internet of Things have resulted in an avalanche of data. Data is powerful but not in its raw form; it needs to be processed and modeled, and Python is one of the most robust tools out there to do so. It has an array of packages for predictive modeling and a suite of IDEs to choose from. Using the Python programming language, analysts can use these sophisticated methods to build scalable analytic applications. This book is your guide to getting started with predictive analytics using Python. You'll balance both statistical and mathematical concepts, and implement them in Python using libraries such as pandas, scikit-learn, and NumPy. Through case studies and code examples using popular open-source Python libraries, this book illustrates the complete development process for analytic applications. Covering a wide range of algorithms for classification, regression, clustering, as well as cutting-edge techniques such as deep learning, this book illustrates explains how these methods work. You will learn to choose the right approach for your problem and how to develop engaging visualizations to bring to life the insights of predictive modeling. Finally, you will learn best practices in predictive modeling, as well as the different applications of predictive modeling in the modern world. The course provides you with highly practical content from the following Packt books: 1. Learning Predictive Analytics with Python 2. Mastering Predictive Analytics with Python What you will learn Understand the statistical and mathematical concepts behind predictive analytics algorithms and implement them using Python libraries Get to know various methods for importing, cleaning, sub-setting, merging, joining, concatenating, exploring, grouping, and plotting data with pandas and NumPy Master the use of Python notebooks for exploratory data analysis and rapid prototyping Get to grips with applying regression, classification, clustering, and deep learning algorithms Discover advanced methods to analyze structured and unstructured data Visualize the performance of models and the insights they produce Ensure the robustness of your analytic applications by mastering the best practices of predictive analysis Who this book is for This book is designed for business analysts, BI analysts, data scientists, or junior level data analysts who are ready to move on from a conceptual understanding of advanced analytics and become an expert in designing and building advanced analytics solutions using Python. If you are familiar with coding in Python (or some other programming/statistical/scripting language) but have never used or read about predictive analytics algorithms, this book will also help you.Table of ContentsTable of Contents Module 1 Module 2

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Book SynopsisData Science and Analytics explores the solutions to problems in society, environment and in industry. With the increase in the availability of data, analytics has now become a major element in both the top line and the bottom line of any organization. This book explores perspectives on how big data and business analytics are increasingly essential in better decision making. This edited work explores the application of big data and business analytics by academics, researchers, industrial experts, policy makers and practitioners, helping the reader to understand how big data can be efficiently utilized in better managerial applications. Data Science and Analytics brings together researchers, engineers and practitioners to encompass a wide and diverse range of topics in a wide range of fields. The book will provide unique insights to researchers, academics and data scientists from a variety of disciplines interested in analyzing and application of big data analytics, as well as data analysts, students and scholars pursuing advanced study in big data.Table of ContentsChapter 1. Data Visualization Aarti Mehta Sharma Chapter 2. Analytical aspects of Multimedia Big Data Computing and Future Scope Hiral R. Patel, Ajay M Patel Satyen M Parikh Chapter 3. Predictive Analysis: Comprehensive study of popular open source tools Gauri Rajendra Virkar, Supriya Sunil Shinde Chapter 4. Market Opportunities through Effective Market Analytics Shakti Ranjan Panigrahy Chapter 5. Stochastic point process techniques for modelling problems in IoT and Marketing: Technique of “Random Point Process” (RPP) & “Product density” (PD)techniques in Stochastic Modeling KSS Iyer, Madhavi Damle Chapter 6. Real-Time Data Analytics - A Contemporary Approach towards Customer Relationship Management Samir Yerpude Chapter 7. Application of Big Data for Sustainable Rural Development with Special Reference to MNREGA K. K. Tripathy, Sneha Kumari Chapter 8. Challenges of Digital Technologies in The Development of Supply Chains: A Guide for Their Selection Jorge Tarifa-Fernandez, Almudena Martínez Aguilera, José Felipe Jiménez-Guerrero

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Book SynopsisOffering a thorough exploration of the symbiotic relationship between data engineering and modern marketing strategies, Data Engineering for Data-Driven Marketing uses a strategic lens to delve into methodologies of collecting, transforming, and storing diverse data sources.

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Book SynopsisThis book constitutes the proceedings of the 10th International Conference on Big Data Analytics, BDA 2022, which took place in a hybrid mode during December 2022 in Aizu, Japan.The 14 full papers included in this volume were carefully reviewed and selected from 70 submissions. They were organized in topical sections as follows: big data analytics, networking, social media, search, information extraction, image processing and analysis, spatial, text, mobile and graph data analysis, machine learning, and healthcare.Table of ContentsData Science: Systems.- Architectures.- Big Data Analytics in Healthcare Support Systems.- Information Interchange of Web Data Resources.- Business Analytics.

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

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Book SynopsisThe LNCS journal Transactions on Large-Scale Data- and Knowledge-Centered Systems focuses on data management, knowledge discovery, and knowledge processing, which are core and hot topics in computer science. Since the 1990s, the Internet has become the main driving force behind application development in all domains. An increase in the demand for resource sharing (e.g., computing resources, services, metadata, data sources) across different sites connected through networks has led to an evolution of data- and knowledge management systems from centralized systems to decentralized systems enabling large-scale distributed applications providing high scalability. This, the 48th issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, contains 8 invited papers dedicated to the memory of Prof. Dr. Roland Wagner. The topics covered include distributed database systems, NewSQL, scalable transaction management, strong consistency, caches, data warehouse, ETL, reinforcement learning, stochastic approximation, multi-agent systems, ontology, model-driven development, organisational modelling, digital government, new institutional economics and data governance.Table of ContentsDistributed Database Systems: The Case for NewSQL.- Boosting OLTP Performance using Write-back Client-side Caches.- pygrametl: A Powerful Programming Framework for Easy Creation and Testing of ETL Flows.- A Data Warehouse of Wi-Fi Sessions for Contact Tracing and Outbreak Investigation.- Convergence Proof for Actor-Critic Methods Applied to PPO and RUDDER.- Revival of MAS Technologies in Industry.- From Strategy to Code: Achieving Strategical Alignment in Software Development Projects through Conceptual Modelling.- On State-Level Architecture of Digital Government Ecosystems: From ICT-Driven to Data-Centric.

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Book SynopsisEvery day, more and more kinds of historical data become available, opening exciting new avenues of inquiry but also new challenges. This updated and expanded book describes and demonstrates the ways these data can be explored to construct cultural heritage knowledge, for research and in teaching and learning. It helps humanities scholars to grasp Big Data in order to do their work, whether that means understanding the underlying algorithms at work in search engines or designing and using their own tools to process large amounts of information.Demonstrating what digital tools have to offer and also what 'digital' does to how we understand the past, the authors introduce the many different tools and developing approaches in Big Data for historical and humanistic scholarship, show how to use them, what to be wary of, and discuss the kinds of questions and new perspectives this new macroscopic perspective opens up. Originally authored 'live' online with ongoing feedback from the wider digital history community, Exploring Big Historical Data breaks new ground and sets the direction for the conversation into the future.Exploring Big Historical Data should be the go-to resource for undergraduate and graduate students confronted by a vast corpus of data, and researchers encountering these methods for the first time. It will also offer a helping hand to the interested individual seeking to make sense of genealogical data or digitized newspapers, and even the local historical society who are trying to see the value in digitizing their holdings.

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Learn statistics by analysing professional basketball data! Statistics Slam Dunk is an action-packed book that will help you build your skills in exploratory data analysis by digging into the fascinating world of NBA games and player stats using the R language. This textbook will upgrade your R data science skills by taking on practical analysis challenges based on NBA game and player data. You will take on the challenge of wrangling messy data to drill on the skills that will make you the star player on any data team. And just like in the real world, you will get no clean pre-packaged datasets in this book. You will develop a toolbox of R data skills including: Reading and writing data Installing and loading packages Transforming, tidying, and wrangling data Applying best-in-class exploratory data analysis techniques Creating compelling visualizations Developing supervised and unsupervised machine learning algorithms Execute hypothesis tests, including t-tests and chi-square tests for independence Compute expected values, Gini coefficients, and z-scores Is losing games on purpose a rational strategy? Which hustle statistics have an impact on wins and losses? Each chapter in this one-of-a-kind guide uses new data science techniques to reveal interesting insights like these. About the technology Amazing insights are hiding in raw data, and statistical analysis with R can help reveal them! R was built for data, and it supports modelling and statistical techniques including regression and classification models, time series forecasts, and clustering algorithms. And when you want to see your results, R's visualisations are stunning, with best-in-class plots and charts.

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Book SynopsisStatistical graphing was born in the seventeenth century as a scientific tool, but it quickly escaped all disciplinary bounds. Today graphics are ubiquitous in daily life. Michael Friendly and Howard Wainer detail the history of data visualization and argue that it has not only helped us solve problems, but it has also changed the way we think.Trade ReviewThe invention of graphs and charts was a much quieter affair than that of the telescope, but these tools have done just as much to change how and what we see. -- Hannah Fry * New Yorker *An indispensable account of how important practitioners of data visualizations write the history of their field. -- Crystal Lee * Information & Culture *We live in an era of data dependence—never before have graphic representations of data been as essential and sought after as at this moment…There has not been a publication of this scope on the evolution of graphic representation of qualitative and quantitative data since Funkhouser’s work…Scholars, practitioners, lovers of statistics and data visualization, and anyone interested in understanding the methods and techniques of today will benefit from understanding the innovations that brought us to where we are. -- María del Mar Navarro * Journal of Design, Economics, and Innovation *A thoughtful and well-written introduction to the world of data visualization and its history. -- Bill Satzer * MAA Reviews *An intellectually fascinating book…The audience for this book is wide. It would be useful to professionals and to professors in many departments such as psychology, sociology, economics, biology, physics, and any department that uses graphs to display quantitative information. It is a book to broaden your knowledge and offer interesting asides for lectures and meetings…Consult it frequently to learn of the stories of the developers of the many graphic methods we use today. -- Malcolm James Ree * Personnel Psychology *A marvel of research scholarship…This is the sort of book that one can browse and sample in bite-size chunks as the mood seizes, encountering curious delights while doing so. -- Bert Gunter * Significance *A masterly study of graphic innovations, their context, and their scientific use. This brilliant book, without equivalent, is an indispensable read. -- Gilles Palsky, coauthor of An Atlas of Geographical WondersFriendly and Wainer are the Watson and Crick of statistical graphics, showing us the history of the DNA structure that is the code of life for innovative visualizations. -- Ben Shneiderman, founder of the Human-Computer Interaction Lab, University of MarylandData expertise is a fundamental prerequisite for success in our digital age. But exactly how, and when, have we learned to draw conclusions from data? For decades, Michael Friendly and Howard Wainer have been studying how data has informed decision-making, through visualization and statistical analysis. Replete with mesmerizing visual examples, this book is an eye-opening distillation of their research. -- Sandra Rendgen, author of History of Information GraphicsMichael Friendly and Howard Wainer have given us a wonderful history of the dazzling field of data visualization. They bring new life to ancient death statistics and describe the artistic poetry used to display numbers. An intriguing story of how we have learned to communicate data of all types. -- Stephen M. Stigler, author of The Seven Pillars of Statistical WisdomTwo of the most distinguished scholars of data visualization give us a glimpse of ancient attempts to quantify the world, before revealing the century-long revolution that led to the invention of modern statistics and many of the graphical methods we use today. I learned a lot from this book, and I think you will too. -- Alberto Cairo, author of How Charts Lie: Getting Smarter about Visual InformationFriendly and Wainer demonstrate the amazing progress that has been made in data graphics over the past two hundred years. Understanding this history—where graphs came from and how they developed—will be valuable as we move forward. -- Andrew Gelman, coauthor of Regression and Other Stories

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Book SynopsisThis comprehensive and authoritative guide will teach you the DAX language for business intelligence, data modeling, and analytics. Leading Microsoft BI consultants Marco Russo and Alberto Ferrari help you master everything from table functions through advanced code and model optimization. You’ll learn exactly what happens under the hood when you run a DAX expression, how DAX behaves differently from other languages, and how to use this knowledge to write fast, robust code. If you want to leverage all of DAX’s remarkable power and flexibility, this no-compromise “deep dive” is exactly what you need. Perform powerful data analysis with DAX for Microsoft SQL Server Analysis Services, Excel, and Power BI Master core DAX concepts, including calculated columns, measures, and error handling Understand evaluation contexts and the CALCULATE and CALCULATETABLE functions Perform time-based calculations: YTD, MTD, previous year, working days, and more Work with expanded tables, complex functions, and elaborate DAX expressions Perform calculations over hierarchies, including parent/child hierarchies Use DAX to express diverse and unusual relationships Measure DAX query performance with SQL Server Profiler and DAX Studio Table of Contents Introduction Chapter 1: What is DAX? Chapter 2: Introducing DAX Chapter 3: Using basic table functions Chapter 4: Understanding evaluation contexts Chapter 5: Understanding CALCULATE and CALCULATETABLE Chapter 6: DAX examples Chapter 7: Time intelligence calculations Chapter 8: Statistical functions Chapter 9: Advanced table functions Chapter 10: Advanced evaluation context Chapter 11: Handling hierarchies Chapter 12: Advanced relationships Chapter 13: The VertiPaq engine Chapter 14: Optimizing data models Chapter 15: Analyzing DAX query plans Chapter 16: Optimizing DAX Index

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Book SynopsisEmphasizes the strategy of experimentation, data analysis, and the interpretation of experimental results. * Features numerous examples using actual engineering and scientific studies. * Presents statistics as an integral component of experimentation from the planning stage to the presentation of the conclusions.Trade Review"With an excellent presentation, this is suitable as a textbook in a graduate level course in design of experiments." (Journal of Statistical Computation and Simulation, April 2005) "...can really provide useful information for the intended audience..." (Zentralblatt Math, Vol. 1029, 2004) “...a practitioner’s guide to statistical methods for designing and analyzing experiments...” (Quarterly of Applied Mathematics, Vol. LXI, No. 3, September 2003) "...a perfect desktop reference..." (Technometrics, Vol. 45, No. 3, August 2003)Table of ContentsPreface. PART I: FUNDAMENTAL STATISTICAL CONCEPTS. Statistics in Engineering and Science. Fundamentals of Statistical Inference. Inferences on Means and Standard Deviations. PART II: DESIGN AND ANALYSIS WITH FACTORIAL STRUCTURE. Statistical Principles in Experimental Design. Factorial Experiments in Completely Randomized Designs. Analysis of Completely Randomized Designs. Fractional Factorial Experiments. Analysis of Fractional Factorial Experiments. PART III: DESIGN AND ANALYSIS WITH RANDOM EFFECTS. Experiments in Randomized Block Designs. Analysis of Designs with Random Factor Levels. Nested Designs. Special Designs for Process Improvement. Analysis of Nested Designs and Designs for Process Improvement. PART IV: DESIGN AND ANALYSIS WITH QUANTITATIVE PREDICTORS AND FACTORS. Linear Regression with One Predicator Variables. Linear Regression with Several Predicator Variables. Linear Regression with Factors and Covariates as Predictors. Designs and Analyses for Fitting Re sponse Surfaces. Model Assessment. Variable Selection Techniques. Appendix: Statistical Tables. Index.

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Book SynopsisLearn how to model and train advanced neural networks to implement a variety of Computer Vision tasksKey Features Train different kinds of deep learning model from scratch to solve specific problems in Computer Vision Combine the power of Python, Keras, and TensorFlow to build deep learning models for object detection, image classification, similarity learning, image captioning, and more Includes tips on optimizing and improving the performance of your models under various constraints Book DescriptionDeep learning has shown its power in several application areas of Artificial Intelligence, especially in Computer Vision. Computer Vision is the science of understanding and manipulating images, and finds enormous applications in the areas of robotics, automation, and so on. This book will also show you, with practical examples, how to develop Computer Vision applications by leveraging the power of deep learning. In this book, you will learn different techniques related to object classification, object detection, image segmentation, captioning, image generation, face analysis, and more. You will also explore their applications using popular Python libraries such as TensorFlow and Keras. This book will help you master state-of-the-art, deep learning algorithms and their implementation.What you will learn Set up an environment for deep learning with Python, TensorFlow, and Keras Define and train a model for image and video classification Use features from a pre-trained Convolutional Neural Network model for image retrieval Understand and implement object detection using the real-world Pedestrian Detection scenario Learn about various problems in image captioning and how to overcome them by training images and text together Implement similarity matching and train a model for face recognition Understand the concept of generative models and use them for image generation Deploy your deep learning models and optimize them for high performance Who this book is forThis book is targeted at data scientists and Computer Vision practitioners who wish to apply the concepts of Deep Learning to overcome any problem related to Computer Vision. A basic knowledge of programming in Python—and some understanding of machine learning concepts—is required to get the best out of this book.Table of ContentsTable of Contents Getting Started Image Classification Image Retrieval Object Detection Semantic Segmentation Similarity Learning Image Captioning Generative models Video Classification Deployment

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Book SynopsisGain hands-on experience of Python programming with industry-standard machine learning techniques using pandas, scikit-learn, and XGBoostKey Features Think critically about data and use it to form and test a hypothesis Choose an appropriate machine learning model and train it on your data Communicate data-driven insights with confidence and clarity Book DescriptionIf data is the new oil, then machine learning is the drill. As companies gain access to ever-increasing quantities of raw data, the ability to deliver state-of-the-art predictive models that support business decision-making becomes more and more valuable.In this book, you’ll work on an end-to-end project based around a realistic data set and split up into bite-sized practical exercises. This creates a case-study approach that simulates the working conditions you’ll experience in real-world data science projects.You’ll learn how to use key Python packages, including pandas, Matplotlib, and scikit-learn, and master the process of data exploration and data processing, before moving on to fitting, evaluating, and tuning algorithms such as regularized logistic regression and random forest.Now in its second edition, this book will take you through the end-to-end process of exploring data and delivering machine learning models. Updated for 2021, this edition includes brand new content on XGBoost, SHAP values, algorithmic fairness, and the ethical concerns of deploying a model in the real world.By the end of this data science book, you’ll have the skills, understanding, and confidence to build your own machine learning models and gain insights from real data.What you will learn Load, explore, and process data using the pandas Python package Use Matplotlib to create compelling data visualizations Implement predictive machine learning models with scikit-learn Use lasso and ridge regression to reduce model overfitting Evaluate random forest and logistic regression model performance Deliver business insights by presenting clear, convincing conclusions Who this book is forData Science Projects with Python – Second Edition is for anyone who wants to get started with data science and machine learning. If you’re keen to advance your career by using data analysis and predictive modeling to generate business insights, then this book is the perfect place to begin. To quickly grasp the concepts covered, it is recommended that you have basic experience of programming with Python or another similar language, and a general interest in statistics.Table of ContentsTable of Contents Data Exploration and Cleaning Introduction to Scikit-Learn and Model Evaluation Details of Logistic Regression and Feature Exploration The Bias-Variance Trade-off Decision Trees and Random Forests Gradient Boosting, XGBoost, and SHAP (SHapley Additive exPlanations) Values Test Set Analysis, Financial Insights, and Delivery to the Client

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Book SynopsisCommon data science algorithms and predictive analytics tools treat customer behavioral data, such as clicks on a website or purchases in a supermarket, the same as any other data. Instead, this practical guide introduces powerful methods specifically tailored for behavioral data analysis.

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Book SynopsisRupert Morrison is an industry leader and entrepreneur in data-driven business. He is the founder and CEO of Arahi and was previously the CEO and co-founder of Concentra Analytics. Based in London, UK, he is also an international conference speaker and industry writer, whose contributions have featured in Forbes, HR Director and Personnel Today. He is the author of Data-Driven Organization Design, also published by Kogan Page.Trade Review"Building a forward-looking OP&A unit is crucial at these times of increasing economic disruption. This book is so much more than an optional read." * Ian Kantor, Founder, Investec *"In his new book, Rupert astutely illustrates applying systems theory to organizational planning for driving outcomes in a complex environment. He gives us courage to rethink planning as a dynamic and insightful process to build long term organizational capabilities." * Shradha Prakash, VP, Future of Work, Org Design and Talent Enablement at Prudential Financial *"Rupert masterfully offers ideas, specific analytical tools and cases on planning, transformation, workforce, and technology to improve organizations. Anyone interested in creating more effective organizations will find this an exceptional guide." * Dave Ulrich, Rensis Likert Professor, Ross School of Business, University of Michigan Partner The RBL Group *"Leadership is about taking an organization from one place to another knowing that the movement can only come from the people. This book shows how data can power alignment, speed and purpose on the journey." * Pär Åström, President Gardena Division, Husqvarna Group *"In his new book Organizational Planning and Analysis, Rupert offers a compelling guide for executive and practitioner alike. If you truly believe that people are your organization's greatest asset and you have been frustrated with previous efforts to make a workforce transformation real, then look no further. Blending strategy, finance, workforce planning and analytics, this book offers a logical, evidence-based process for introducing OP&A as a new capability in your organization. Doing so will ensure that you can achieve sustained and measurable workforce success. Highly recommended." * David Stroud, Director, Workforce Insight Pty Ltd *"If you enjoyed Data-Driven Organization Design, the prequel to this book, then you'll love Organizational Planning and Analysis. Rupert is one of the genuine thought leaders in our field. He first outlined the key concepts outlined in this book to me as a guest on the Digital HR Leaders podcast. I was captivated. If you want to understand how to use data and analysis to build organizational capabilities through workforce planning and drive business success, then this is the book for you." * David Green, co-author of Excellence in People Analytics, Managing Partner at Insight222 and host of the Digital HR Leaders podcast. *"We all know, in theory, that planning and analysis founded on good data is an organizational must. Getting the theory into day-to-day practice is more problematic. Rupert Morrison clearly and carefully lays out why and how to do this. What's not to follow in his guidance? The benefits are huge." * Naomi Stanford, organization design author and consultant *"A practical guide to using data to continuously optimize organizational performance, aligning for innovation, agility and productivity. A must build muscle for all organizations today." * Kent McMillan, Managing Director, Global Organization Strategy & Design Lead, Accenture *"In a world where business leaders are told to reflect properly and plan effectively, this book equips them with the right questions to ask and a framework for generating continuous performance. Ultimately, it ushers in a leadership approach that is confident and clear in the face of an increasingly volatile and opaque business landscape. Essential reading." * John Brown, Founder and CEO, Don’t Cry Wolf *"Simplicity is something hard to achieve. OP&A is a simple yet very effective concept to solve one of the most complex challenges that organisations face today. This is an essential read to anyone in charge of teams." * Thiago R. Kiwi, Head of Marketing & Communications at Headspring Executive Development *Table of Contents Section - ONE: Introduction; Chapter - 1.1: Organizational planning and analytics; Chapter - 1.2: Data-driven organization design; Chapter - 1.3: Financial planning and analytics; Section - TWO: Making it real - Transformation and optimization; Chapter - 2.1: Introduction; Chapter - 2.2: Preparing to execute; Chapter - 2.3: Implementation; Chapter - 2.4: Ongoing optimization; Section - THREE: Workforce Planning; Chapter - 3.1: Introduction; Chapter - 3.2: Supply forecasting and top-down demand planning; Chapter - 3.3: Bottom-up position planning, finalizing the workforce plan and monitoring progress; Section - FOUR: OP&A analysis; Chapter - 4.1: Introduction; Chapter - 4.2: Laying the foundations for effective OP&A analysis; Chapter - 4.3: Ignoring statistical traps and leveraging data science; Section - FIVE: The OP&A function; Chapter - 5.1: Introduction; Chapter - 5.2: Stage 1: Scoping the macro operating design and writing the business case; Chapter - 5.3: Stage 2: Detailing the OP&A function; Chapter - 5.4: Stage 3: Making the OP&A function real; Chapter - 18: Glossary

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Book SynopsisThis book lays out the roles everyone, up and down the organisation chart, can and must play to ensure that data is up to the demands of its use, in day-in, day-out work, decision-making, planning, and analytics. By now, everyone knows that bad data extorts an enormous toll, adding huge (though often hidden) costs, and making it more difficult to make good decisions and leverage advanced analyses. While the problems are pervasive and insidious, they are also solvable! As Tom Redman, the Data Doc explains, the secret lies in getting the right people in the right roles to get in front of the management and social issues that lead to bad data in the first place. Everyone should see himself or herself in this book. We are all both data customers and data creators -- after all, we use data created by others and create data used by others. And all of us must step up to these roles. As data customers, we must clarify our most important needs and communicate them to data creators. As data creators, we must strive to meet those needs by finding and eliminating the root causes of error. This book proposes new roles for data professionals as: embedded data managers, in helping data customers and creators complete their work, DQ team leads, in connecting customers and creators, pulling the entire program together, and training people on their new roles, data maestros, in providing deep expertise on the really tough problems, chief data architects, in establishing common data definitions, and technologists, in increasing scale and decreasing unit cost. The book introduces a new role, the data provocateur, the motive force in attacking data quality properly! This book urges everyone to unleash their inner provocateur. Finally, it crystallises what senior leaders must do if their entire organisations are to enjoy the benefits of high-quality data!

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

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Book SynopsisWith constantly expanding options such as Azure Data Lake Server (ADLS) and Azure SQL Data Warehouse (ADW), how can developers learn the process and components required to successfully move this data? Quick Start Guide to Azure Data Factory, Azure Data Lake Server, and Azure Data Warehouse teaches you the basics of moving data between Azure SQL solutions using Azure Data Factory. Discover how to build and deploy each of the components needed to integrate data in the cloud with local SQL databases. Mark Beckner's step by step instructions on how to build each component, how to test processes and debug, and how to track and audit the movement of data, will help you to build your own solutions instantly and efficiently. This book includes information on configuration, development, and administration of a fully functional solution and outlines all of the components required for moving data from a local SQL instance through to a fully functional data warehouse with facts and dimensions.

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Book SynopsisBig Data Analytics Methods unveils secrets to advanced analytics techniques ranging from machine learning, random forest classifiers, predictive modeling, cluster analysis, natural language processing (NLP), Kalman filtering and ensembles of models for optimal accuracy of analysis and prediction. More than 100 analytics techniques and methods provide big data professionals, business intelligence professionals and citizen data scientists insight on how to overcome challenges and avoid common pitfalls and traps in data analytics. The book offers solutions and tips on handling missing data, noisy and dirty data, error reduction and boosting signal to reduce noise. It discusses data visualization, prediction, optimization, artificial intelligence, regression analysis, the Cox hazard model and many analytics using case examples with applications in the healthcare, transportation, retail, telecommunication, consulting, manufacturing, energy and financial services industries. This book's state of the art treatment of advanced data analytics methods and important best practices will help readers succeed in data analytics.Table of ContentsIntroduction PART I: Big Data Analytics Chapter 1. Data Analytics Overview Chapter 2. Basic Data Analysis Chapter 3. Data Visualization Tools PART II: Advanced Analytics Methods Chapter 4. Natural Language Processing Chapter 5. Quantitative Analysis - Prediction and Prognostics Chapter 6. Advanced Analytics & Predictive Modeling Chapter 7. Ensemble of Models Chapter 8. Machine Learning, Deep Learning – Artificial Neural Networks Chapter 9. Model Accuracy & Optimization PART III: Case Study – Prediction & Advanced Analytics in Practice Chapter 10: Ensemble of Models – Medical Prediction Case Study Appendix A: Prognostics Methods Appendix B: A Neural Network Example Appendix C: Back Propagation Algorithm Derivation Appendix D: NeuroSolutions Software Description Appendix E: The Oracle Program References

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Book SynopsisThese days, businesses are collecting massive amounts of data. But this data isnt valuable until someone analyzes it to gain insights that can be used to make decisions. Thats why the US Bureau of Labor Statistics (BLS) predicts that the demand for data analysts will continue to grow for the rest of the decade. Now, with Murachs R for Data Analysis as a guide, you can learn the R skills you need to become a data analyst, and you can learn them faster and better than ever before. Thanks to its unique paired-pages format this book works equally well if youre new to programming or if youre an experienced programmer. Youll get started fast by learning only the parts of the R language that you need for data analysis. Then, youll learn how to use R with the tidyverse package to get, clean, prepare, analyze, and visualize data at a professional level. By the end of this book, youll be creating linear regression models and classification models and using them to make predictions. This book contains three realistic analyses that use real-world data. Thats because we believe that studying analyses like these is critical to the learning process. Mike Murach & Associates has been publishing high-quality books about computer programming since 1972. Download a sample chapter from the Murach website and see for yourself.

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