{"title":"Data capture and analysis Books","description":"","products":[{"product_id":"big-data-9780198779575","title":"Big Data","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eAn unimaginably vast amount of data is now generated by our on-line lives and businesses, At the same time, our ability to store, manage, analyse, and exploit this data is becoming ever more sophisticated. This Very Short Introduction maps out the technology, and also the range of possibilities, challenges, and ethical questions it raises.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003eBig data is in the news, and this excellent very short introduction brings the reader up to speed and enables them to understand the various components and implications. * Paradigm Explorer *\u003cbr\u003eThis is a very useful, concise introduction to the topic of big data. * Jonathan Cowie, Science Fact \u0026amp; Science Fiction Concatenation *\u003cbr\u003eA very short introduction to a very big subject ... arguably the most topical of this book series ... This very short introduction is perfect for anyone who is a little bit baffled by the very concept of big data. Holmes introduces the subject in a format that is both concise and manageable. * Jade Taylor-Salazar, E\u0026amp;T Magazine *\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eBYTE SIZE CHART; REFERENCES; FURTHER READING; INDEX","brand":"Oxford University Press","offers":[{"title":"Default Title","offer_id":48732781904215,"sku":"9780198779575","price":9.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780198779575.jpg?v=1719998374"},{"product_id":"the-new-statistics-with-r-9780198798187","title":"The New Statistics with R","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eStatistical 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 anal\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003eReview 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 *\u003cbr\u003e[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 *\u003cbr\u003e... 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 *\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1: 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","brand":"Oxford University Press","offers":[{"title":"Default Title","offer_id":48732787605847,"sku":"9780198798187","price":39.42,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780198798187.jpg?v=1719998400"},{"product_id":"data-grab-9780753560204","title":"Data Grab","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003e\u003ci\u003eYour \u003c\/i\u003elife online is \u003ci\u003etheir \u003c\/i\u003eproduct.\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eIn the past, colonialism was a landgrab of natural resources, exploitative labour and private property from countries around the world. It promised to modernise and civilise, but actually sought to control. It stole from native populations and made them sign contracts they didn't understand. It took resources just because they were there.\u003cbr\u003e\u003cbr\u003eColonialism has not disappeared  it has taken on a new form.\u003cbr\u003e\u003cbr\u003eIn the new world order, data is the new oil. Big Tech companies are grabbing our most basic natural resources  our data  exploiting our labour and connections, and repackaging our information to control our views, track our movements, record our conversations and discriminate against us. Every time we unthinkingly click Accept' on Terms and Conditions, we allow our most personal information to kept indefinitely, repackaged by big Tech companies to control and exploit us for their own profit.\u003cbr\u003e\u003cbr\u003eIn this searin\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eI wish that \u003ci\u003eData Grab\u003c\/i\u003e was required reading when I was a graduate student working in the field of AI. Perspectives like these are crucial if we are to break the colonial paradigm that pervades computing disciplines\u003c\/b\u003e -- Timnit Gebru, founder of the Distributed AI Research Institute\u003cbr\u003e\u003cb\u003eA blistering, vital exposure of the predatory world of data colonialism. In this vivid and passionately written book, Mejias and Couldry urge us to wake up to the invasive and extractive world of today’s Big Tech\u003c\/b\u003e -- Mike Savage, author of 'Social Class in the 21st Century'\u003cbr\u003e\u003cb\u003eRemarkable...\u003c\/b\u003e\u003ci\u003e Data Grab\u003c\/i\u003e helps us understand that \u003cb\u003ethe historical and ongoing relations of power have extended to the realm of data\u003c\/b\u003e, a new raw material of digital capitalism. \u003cb\u003eMejias and Couldry place us on a path to recognise, resist, and challenge\u003c\/b\u003e these forces -- Dr Ramesh Srinivasan, Professor at the UCLA Department of Information Studies and Director of UC Digital Cultures Lab\u003cbr\u003eAs in their previous work, Mejias and Couldry\u003cb\u003e show how important it is to take the perspective of the colonized, not the colonizer, in explaining how the digital world is governed.\u003c\/b\u003e \u003ci\u003eData Grab\u003c\/i\u003e\u003cb\u003e offers important insights \u003c\/b\u003einto how we should analyse power and counter-power in terms of data control. I particularly recommend this book for providing examples of local and vocal initiatives across various continents. \u003cb\u003eA true eye-opener\u003c\/b\u003e -- José van Dijck, Distinguished Professor of Media and Digital Society, Utrecht University\u003cbr\u003eIn this \u003cb\u003eessential and original \u003c\/b\u003ework, Mejias and Couldry lay out a \u003cb\u003epowerful and persuasive\u003c\/b\u003e analysis of the logical continuity between modern colonialism and the extraction of data by Big Tech and its platforms. \u003cb\u003eTheir call to resist data colonialism could not be more urgent or more timely\u003c\/b\u003e -- Jeremy Gilbert, author of 'Hegemony Now: How Big Tech and Wall Street Won the World' and 'Twenty-First Century Socialism'\u003c\/p\u003e","brand":"Ebury Publishing","offers":[{"title":"Default Title","offer_id":48737037517143,"sku":"9780753560204","price":18.7,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780753560204.jpg?v=1723810923"},{"product_id":"largescale-data-analytics-with-python-and-spark-9781009318259","title":"LargeScale Data Analytics with Python and Spark","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eA 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.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e'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 Diego\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePart 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.","brand":"Cambridge University Press","offers":[{"title":"Default Title","offer_id":48738030551383,"sku":"9781009318259","price":28.49,"currency_code":"GBP","in_stock":true}]},{"product_id":"optimization-for-data-analysis-9781316518984","title":"Optimization for Data Analysis","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eOptimization techniques are at the core of data science, including data analysis and machine learning. An understanding of basic optimization techniques and their fundamental properties provides important grounding for students, researchers, and practitioners in these areas. This text covers the fundamentals of optimization algorithms in a compact, self-contained way, focusing on the techniques most relevant to data science. An introductory chapter demonstrates that many standard problems in data science can be formulated as optimization problems. Next, many fundamental methods in optimization are described and analyzed, including: gradient and accelerated gradient methods for unconstrained optimization of smooth (especially convex) functions; the stochastic gradient method, a workhorse algorithm in machine learning; the coordinate descent approach; several key algorithms for constrained optimization problems; algorithms for minimizing nonsmooth functions arising in data science; found\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e'This delightful compact tome gives the reader all the results they should have in their pocket to contribute to optimization and statistical learning. With the clean, elegant derivations of many of the foundational optimization methods underlying modern large-scale data analysis, everyone from students just getting started to researchers knowing this book inside and out will be well-positioned for both using the algorithms and developing new ones for machine learning, optimization, and statistics.' John C. Duchi, Stanford University\u003cbr\u003e'Optimization algorithms play a vital role in the rapidly evolving field of machine learning, as well as in signal processing, statistics and control. Numerical optimization is a vast field, however, and a student wishing to learn the methods required in the world of data science could easily get lost in the literature. This book does a superb job of presenting the most important algorithms, providing both their mathematical foundations and lucid motivations for their development. Written by two of the foremost experts in the field, this book gently guides a reader without prior knowledge of optimization towards the methods and concepts that are central in modern data science applications.' Jorge Nocedal, Northwestern University\u003cbr\u003e'This timely introductory book gives a rigorous view of continuous optimization techniques which are being used in machine learning. It is an excellent resource for those who are interested in understanding the mathematical concepts behind commonly used machine learning techniques.' Shai Shalev-Shwartz, Hebrew University of Jerusalem\u003cbr\u003e'This textbook is a much-needed exposition of optimization techniques, presented with conciseness and precision, with emphasis on topics most relevant for data science and machine learning applications. I imagine that this book will be immensely popular in university courses across the globe, and become a standard reference used by researchers in the area.' Amitabh Basu, Johns Hopkins University\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1. Introduction; 2. Foundations of smooth optimization; 3. Descent methods; 4. Gradient methods using momentum; 5. Stochastic gradient; 6. Coordinate descent; 7. First-order methods for constrained optimization; 8. Nonsmooth functions and subgradients; 9. Nonsmooth optimization methods; 10. Duality and algorithms; 11. Differentiation and adjoints.","brand":"Cambridge University Press","offers":[{"title":"Default Title","offer_id":48738562310487,"sku":"9781316518984","price":37.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781316518984.jpg?v=1720049477"},{"product_id":"the-statistical-physics-of-data-assimilation-and-machine-learning-9781316519639","title":"The Statistical Physics of Data Assimilation and","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eData assimilation is a hugely important mathematical technique, relevant in fields as diverse as geophysics, data science, and neuroscience. This modern book provides an authoritative treatment of the field as it relates to several scientific disciplines, with a particular emphasis on recent developments from machine learning and its role in the optimisation of data assimilation. Underlying theory from statistical physics, such as path integrals and Monte Carlo methods, are developed in the text as a basis for data assimilation, and the author then explores examples from current multidisciplinary research such as the modelling of shallow water systems, ocean dynamics, and neuronal dynamics in the avian brain. The theory of data assimilation and machine learning is introduced in an accessible and unified manner, and the book is suitable for undergraduate and graduate students from science and engineering without specialized experience of statistical physics.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1. Prologue: linking 'The Future' with the present; 2. A data assimilation reminder; 3. Remembrance of things path; 4. SDA variational principles; Euler–Lagrange equations and Hamiltonian formulation; 5. Using waveform information; 6. Annealing in the model precision Rf; 7. Discrete time integration in data assimilation variational principles; Lagrangian and Hamiltonian formulations; 8. Monte Carlo methods; 9. Machine learning and its equivalence to statistical data assimilation; 10. Two examples of the practical use of data assimilation; 11. Unfinished business; Bibliography; Index.","brand":"Cambridge University Press","offers":[{"title":"Default Title","offer_id":48738562834775,"sku":"9781316519639","price":55.09,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781316519639.jpg?v=1720049479"},{"product_id":"data-quality-9781394165230","title":"Data Quality","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003eDiscover how to achieve business goals by relying on high-quality, robust data\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eIn \u003ci\u003eData Quality: Empowering Businesses with Analytics and AI\u003c\/i\u003e, veteran data and analytics professional delivers a practical and hands-on discussion on how to accelerate business results using high-quality data. In the book, you'll learn techniques to define and assess data quality, discover how to ensure that your firm's data collection practices avoid common pitfalls and deficiencies, improve the level of data quality in the business, and guarantee that the resulting data is useful for powering high-level analytics and AI applications. \u003c\/p\u003e\u003cp\u003eThe author shows you how to: \u003c\/p\u003e\u003cul\u003e \u003cli\u003eProfile for data quality, including the appropriate techniques, criteria, and KPIs \u003c\/li\u003e \u003cli\u003eIdentify the root causes of data quality issues in the business apart from discussing the 16 common root causes that degrade data quality in the organization.\u003c\/li\u003e \u003cli\u003eFormulate the reference architecture for data quality, in\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eForeword\u003c\/p\u003e \u003cp\u003eby Bill Inmon\u003c\/p\u003e \u003cp\u003ePreface\u003c\/p\u003e \u003cp\u003eAbout the Book\u003c\/p\u003e \u003cp\u003eQuality Principles Applied in This Book\u003c\/p\u003e \u003cp\u003eOrganization of the Book\u003c\/p\u003e \u003cp\u003eWho Should Read This Book?\u003c\/p\u003e \u003cp\u003eReferences\u003c\/p\u003e \u003cp\u003eAcknowledgments\u003c\/p\u003e \u003cp\u003eDefine Phase\u003c\/p\u003e \u003cp\u003eChapter 1: Introduction\u003c\/p\u003e \u003cp\u003eIntroduction\u003c\/p\u003e \u003cp\u003eData, Analytics, AI, and Business Performance\u003c\/p\u003e \u003cp\u003eData as a Business Asset or Liability\u003c\/p\u003e \u003cp\u003eData Governance, Data Management, and Data Quality\u003c\/p\u003e \u003cp\u003eLeadership Commitment to Data Quality\u003c\/p\u003e \u003cp\u003eKey Takeaways\u003c\/p\u003e \u003cp\u003eConclusion\u003c\/p\u003e \u003cp\u003eReferences\u003c\/p\u003e \u003cp\u003eChapter 2: Business Data\u003c\/p\u003e \u003cp\u003eIntroduction\u003c\/p\u003e \u003cp\u003eData in Business\u003c\/p\u003e \u003cp\u003eTelemetry Data\u003c\/p\u003e \u003cp\u003ePurpose of Data in Business\u003c\/p\u003e \u003cp\u003eBusiness Data Views\u003c\/p\u003e \u003cp\u003eKey Characteristics of Business Data\u003c\/p\u003e \u003cp\u003eCritical Data Elements (CDE)\u003c\/p\u003e \u003cp\u003eKey Takeaways\u003c\/p\u003e \u003cp\u003eConclusion\u003c\/p\u003e \u003cp\u003eReferences\u003c\/p\u003e \u003cp\u003eChapter 3: Data Quality in Business\u003c\/p\u003e \u003cp\u003eIntroduction\u003c\/p\u003e \u003cp\u003eData Quality Dimensions\u003c\/p\u003e \u003cp\u003eContext in Data Quality\u003c\/p\u003e \u003cp\u003eConsequences and Costs of Poor Data Quality\u003c\/p\u003e \u003cp\u003eData Depreciation and Its Factors\u003c\/p\u003e \u003cp\u003eData in IT Systems\u003c\/p\u003e \u003cp\u003eData Quality and Trusted Information\u003c\/p\u003e \u003cp\u003eKey Takeaways\u003c\/p\u003e \u003cp\u003eConclusion\u003c\/p\u003e \u003cp\u003eReferences\u003c\/p\u003e \u003cp\u003eAnalyze Phase\u003c\/p\u003e \u003cp\u003eChapter 4: Causes for Poor Data Quality\u003c\/p\u003e \u003cp\u003eIntroduction\u003c\/p\u003e \u003cp\u003eData Quality RCA Techniques\u003c\/p\u003e \u003cp\u003eTypical Causes of Poor Data Quality\u003c\/p\u003e \u003cp\u003eKey Takeaways\u003c\/p\u003e \u003cp\u003eConclusion\u003c\/p\u003e \u003cp\u003eReferences\u003c\/p\u003e \u003cp\u003eChapter 5: Data Lifecycle and Lineage\u003c\/p\u003e \u003cp\u003eIntroduction\u003c\/p\u003e \u003cp\u003eBusiness-Enabled DLC Stages\u003c\/p\u003e \u003cp\u003eIT Business-Enabled DLC Stages\u003c\/p\u003e \u003cp\u003eData Lineage\u003c\/p\u003e \u003cp\u003eKey Takeaways\u003c\/p\u003e \u003cp\u003eConclusion\u003c\/p\u003e \u003cp\u003eReferences\u003c\/p\u003e \u003cp\u003eChapter 6: Profiling for Data Quality\u003c\/p\u003e \u003cp\u003eIntroduction\u003c\/p\u003e \u003cp\u003eCriteria for Data Profiling\u003c\/p\u003e \u003cp\u003eData Profiling Techniques for Measures of Centrality\u003c\/p\u003e \u003cp\u003eData Profiling Techniques for Measures of Variation\u003c\/p\u003e \u003cp\u003eIntegrating Centrality and Variation KPIs\u003c\/p\u003e \u003cp\u003eKey Takeaways\u003c\/p\u003e \u003cp\u003eConclusion\u003c\/p\u003e \u003cp\u003eReferences\u003c\/p\u003e \u003cp\u003eRealize Phase\u003c\/p\u003e \u003cp\u003eChapter 7: Reference Architecture for Data Quality\u003c\/p\u003e \u003cp\u003eIntroduction\u003c\/p\u003e \u003cp\u003eOptions to Remediate Data Quality\u003c\/p\u003e \u003cp\u003eDataOps\u003c\/p\u003e \u003cp\u003eData Product\u003c\/p\u003e \u003cp\u003eData Fabric and Data Mesh\u003c\/p\u003e \u003cp\u003eData Enrichment\u003c\/p\u003e \u003cp\u003eKey Takeaways\u003c\/p\u003e \u003cp\u003eConclusion\u003c\/p\u003e \u003cp\u003eReferences\u003c\/p\u003e \u003cp\u003eChapter 8: Best Practices to Realize Data Quality\u003c\/p\u003e \u003cp\u003eIntroduction\u003c\/p\u003e \u003cp\u003eOverview of Best Practices\u003c\/p\u003e \u003cp\u003eBP 1: Identify the Business KPIs and the Ownership of These KPIs and the Pertinent Data\u003c\/p\u003e \u003cp\u003eBP 2: Build and Improve the Data Culture and Literacy in the Organization\u003c\/p\u003e \u003cp\u003eBP 3: Define the Current and Desired state of Data Quality\u003c\/p\u003e \u003cp\u003eBP 4: Follow the Minimalistic Approach to Data Capture\u003c\/p\u003e \u003cp\u003eBP 5: Select and Define the Data Attributes for Data Quality\u003c\/p\u003e \u003cp\u003eBP 6: Capture and Manage Critical Data with Data Standards in MDM Systems\u003c\/p\u003e \u003cp\u003eKey Takeaways\u003c\/p\u003e \u003cp\u003eConclusion\u003c\/p\u003e \u003cp\u003eReferences\u003c\/p\u003e \u003cp\u003eChapter 9: Best Practices to Realize Data Quality\u003c\/p\u003e \u003cp\u003eIntroduction\u003c\/p\u003e \u003cp\u003eBP 7: Automate the Integration of Critical Data Elements\u003c\/p\u003e \u003cp\u003eBP 8: Define the SoR and Securely Capture Transactional Data in the SoR\/OLTP System\u003c\/p\u003e \u003cp\u003eBP 9: Build and Manage Robust Data Integration Capabilities\u003c\/p\u003e \u003cp\u003eBP 10: Distribute Data Sourcing and Insight Consumption\u003c\/p\u003e \u003cp\u003eKey Takeaways\u003c\/p\u003e \u003cp\u003eConclusion\u003c\/p\u003e \u003cp\u003eReferences\u003c\/p\u003e \u003cp\u003eSustain Phase\u003c\/p\u003e \u003cp\u003eChapter 10: Data Governance\u003c\/p\u003e \u003cp\u003eIntroduction\u003c\/p\u003e \u003cp\u003eData Governance Principles\u003c\/p\u003e \u003cp\u003eData Governance Design Components\u003c\/p\u003e \u003cp\u003eImplementing the Data Governance Program\u003c\/p\u003e \u003cp\u003eData Observability\u003c\/p\u003e \u003cp\u003eData Compliance – ISO 27001 and SOC2\u003c\/p\u003e \u003cp\u003eKey Takeaways\u003c\/p\u003e \u003cp\u003eConclusion\u003c\/p\u003e \u003cp\u003eReferences\u003c\/p\u003e \u003cp\u003eChapter 11: Protecting Data\u003c\/p\u003e \u003cp\u003eIntroduction\u003c\/p\u003e \u003cp\u003eData Classification\u003c\/p\u003e \u003cp\u003eData Safety\u003c\/p\u003e \u003cp\u003eData Security\u003c\/p\u003e \u003cp\u003eKey Takeaways\u003c\/p\u003e \u003cp\u003eConclusion\u003c\/p\u003e \u003cp\u003eReferences\u003c\/p\u003e \u003cp\u003eChapter 12: Data Ethics\u003c\/p\u003e \u003cp\u003eIntroduction\u003c\/p\u003e \u003cp\u003eData Ethics\u003c\/p\u003e \u003cp\u003eImportance of Data Ethics\u003c\/p\u003e \u003cp\u003ePrinciples of Data Ethics\u003c\/p\u003e \u003cp\u003eModel Drift in Data Ethics\u003c\/p\u003e \u003cp\u003eData Privacy\u003c\/p\u003e \u003cp\u003eManaging Data Ethically\u003c\/p\u003e \u003cp\u003eKey Takeaways\u003c\/p\u003e \u003cp\u003eConclusion\u003c\/p\u003e \u003cp\u003eReferences\u003c\/p\u003e \u003cp\u003eAppendix 1: Abbreviations and Acronyms\u003c\/p\u003e \u003cp\u003eAppendix 2: Glossary\u003c\/p\u003e \u003cp\u003eAppendix 3: Data Literacy Competencies\u003c\/p\u003e \u003cp\u003eAbout the Author\u003c\/p\u003e \u003cp\u003eIndex\u003c\/p\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":48738658812247,"sku":"9781394165230","price":24.79,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781394165230.jpg?v=1720049803"},{"product_id":"behavioral-data-analysis-with-r-and-python-9781492061373","title":"Behavioral Data Analysis with R and Python","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eCommon 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.","brand":"O'Reilly Media","offers":[{"title":"Default Title","offer_id":48739719184727,"sku":"9781492061373","price":47.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781492061373.jpg?v=1720052986"},{"product_id":"colorwise-9781492097846","title":"Colorwise","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eWith this book, author and DATAcated founder Kate Strachnyi provides the ultimate guide to the correct use of color for representing data in graphs, charts, tables, and infographics.","brand":"O'Reilly Media","offers":[{"title":"Default Title","offer_id":48739719381335,"sku":"9781492097846","price":23.19,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781492097846.jpg?v=1720052987"},{"product_id":"real-world-machine-learning-9781617291920","title":"Real-World Machine Learning","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eDESCRIPTION\u003c\/b\u003e    \u003cp\u003eIn a world where big data is the norm and near-real-time decisions are crucial, machine learning (ML) is a critical component of the data workflow. Machine learning systems can quickly crunch massive amounts of information to offer insights and make decisions in a way that matches or even surpasses human cognitive abilities. These systems use sophisticated computational and statistical tools to build models that can recognize and visualize patterns, predict outcomes, forecast values, and make recommendations. \u003c\/p\u003e  \u003cp\u003e \u003c\/p\u003e  \u003cp\u003e\u003ci\u003eReal-World Machine Learning\u003c\/i\u003e is a practical guide designed to teach developers the art of ML project execution. The book introduces the day-to-day practice of machine learning and prepares readers to successfully build and deploy powerful ML systems. Using the Python language and the R statistical package, it starts with core concepts like data acquisition and modeling, classification, and regression. Then it moves through the most important ML tasks, like model validation, optimization and feature engineering. It uses real-world examples that help readers anticipate and overcome common pitfalls. Along the way, they will discover scalable and online algorithms for large and streaming data sets. Advanced readers will appreciate the in-depth discussion of enhanced ML systems through advanced data exploration and pre-processing methods.\u003c\/p\u003e         \u003cb\u003eKEY FEATURES\u003c\/b\u003e  \u003cul\u003e\n\u003cli\u003eAccessible and  practical introduction to machine learning \u003c\/li\u003e\n\u003cli\u003eContains big-picture ideas and real-world examples \u003c\/li\u003e\n\u003cli\u003ePrepares reader to build and deploy powerful predictive systems \u003c\/li\u003e\n\u003cli\u003eOffers tips \u0026amp; tricks and highlights common pitfalls\u003c\/li\u003e\n\u003c\/ul\u003e   \u003cb\u003eAUDIENCE\u003c\/b\u003e  \u003cp\u003eCode examples are in Python and R. No prior machine learning experience required. \u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e   \u003cb\u003eABOUT THE TECHNOLOGY\u003c\/b\u003e  \u003cp\u003eMachine learning has gained prominence due to the overwhelming successes of Google, Microsoft, Amazon, LinkedIn, Facebook, and others in their use of ML. The Gartner report predicts that big data analytics will be a $25 billion market by 2017, and financial firms, marketing organizations, scientific facilities, and Silicon Valley startups are all demanding machine learning skills from their developers.\u003c\/p\u003e","brand":"Manning Publications","offers":[{"title":"Default Title","offer_id":48740642029911,"sku":"9781617291920","price":37.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781617291920.jpg?v=1720055221"},{"product_id":"data-analysis-with-python-and-pyspark-9781617297205","title":"Data Analysis with Python and PySpark","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eWhen it comes to data analytics, it pays tothink big. PySpark blends the powerful Spark big data processing engine withthe Python programming language to provide a data analysis platform that can scaleup for nearly any task. \u003cb\u003e\u003ci\u003eData Analysis with Python and PySpark \u003c\/i\u003e\u003c\/b\u003eis yourguide to delivering successful Python-driven data projects.   \u003c\/p\u003e \u003cp\u003eData Analysis with Python and PySpark is a carefully engineered tutorial that helps you use PySpark to deliver your data-driven applications at any scale. This clear and hands-on guide shows you how to enlarge your processing capabilities across multiple machines with data from any source, ranging from Had oop-based clusters to Excel worksheets. You'll learn how to break down big analysis tasks into manageable chunks and how to choose and use the best PySpark data abstraction for your unique needs.   \u003c\/p\u003e \u003cp\u003eThe Spark data processing engine is an amazing analytics factory: raw data comes in,and insight comes out. Thanks to its ability to handle massive amounts of data distributed across a cluster, Spark has been adopted as standard by organizations both big and small. PySpark, which wraps the core Spark engine with a Python-based API, puts Spark-based data pipelines in the hands of programmers and data scientists working with the Python programming language. PySpark simplifies Spark's steep learning curve, and provides a seamless bridge between Spark and an ecosystem of Python-based data science tools.   \u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e“A great and gentle introduction to spark.” \u003cb\u003eJavier Collado Cabeza    \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e“A phenomenal introduction to PySpark from the ground up.”\u003cb\u003eAnonymous Reviewer\u003c\/b\u003e   \u003c\/p\u003e \u003cp\u003e“A great book to get you started with PySpark!” \u003cb\u003eJeremy Loscheider    \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e“Takes you on an example focused tour of building pyspark data structures from the data you provide and processing them at speed.” \u003cb\u003eAlex Lucas\u003c\/b\u003e   \u003c\/p\u003e \u003cp\u003e“If you need to learn PySpark (as a Data Scientist or Data Wrangler) start with this book!”\u003cb\u003eGeoff Clark    \u003c\/b\u003e\u003c\/p\u003e","brand":"Manning Publications","offers":[{"title":"Default Title","offer_id":48740645732695,"sku":"9781617297205","price":40.85,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781617297205.jpg?v=1720055231"},{"product_id":"how-to-lead-in-data-science-9781617298899","title":"How to Lead in Data Science","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eTo lead a data science team, you need to expertly articulate technology roadmaps, support a data-driven culture, and plan a data strategy that drives a competitive business plan. In this practical guide, you'll learn leadership techniques the authors have developed building multiple high-performance data teams.   \u003c\/p\u003e \u003cp\u003eIn \u003cb\u003e\u003ci\u003eHow to Lead in Data Science\u003c\/i\u003e\u003c\/b\u003e you'll master techniques for leading data science at every seniority level, from heading up a single project to overseeing a whole company's data strategy. You'll find advice on plotting your long-term career advancement, as well as quick wins you can put into practice right away. Throughout, carefully crafted assessments and interview scenarios encourage introspection, reveal personal blind spots, and show development areas to help advance your career.\u003c\/p\u003e \u003cp\u003eLeading a data science team takes more than the typical set of business management skills. You need specific know-how to articulate technology roadmaps, support a data-driven culture, and plan a data strategy that drives a competitive business plan. Whether you're looking to manage your team better or work towards a seat at your company's top leadership table, this book will show you how.\u003c\/p\u003e \u003cbr\u003e \u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e“Improveleadership skills, irrespective of the domain you are in.”   \u003c\/p\u003e \u003cp\u003e\u003cb\u003eVishwesh RaviShrimali    \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e“Whether you are new to managing, new to data science, or just want tobe a better advocate for your data team there are a lot of tips to improve yourpractice.”   \u003c\/p\u003e \u003cp\u003e\u003cb\u003eMichaelPetrey    \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e“This is a book that surpasses the boundaries of mining data and coding,but warns you about not forgetting them in the effort to successfully lead datascience teams.”   \u003c\/p\u003e \u003cp\u003e\u003cb\u003eJesúsJuárez-Guerrero    \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e“Excellent book. Covers a large complex topic in a clear and understandableway.”   \u003c\/p\u003e \u003cp\u003e\u003cb\u003eGaryBake    \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e“Excellent and ambitious book that provides actionable insight on how tolead in data science. Filled with insightful vignettes, anecdotes, and casestudies to bring life and relevance to the frameworks and discussion.”   \u003c\/p\u003e \u003cp\u003e\u003cb\u003eMarcParadis    \u003c\/b\u003e\u003c\/p\u003e","brand":"Manning Publications","offers":[{"title":"Default Title","offer_id":48740646289751,"sku":"9781617298899","price":37.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781617298899.jpg?v=1720055234"},{"product_id":"statistics-playbook-9781633438683","title":"Statistics Playbook","description":"Learn statistics by analysing professional basketball data! \u003cp\u003e\u003cstrong\u003eStatistics Slam Dunk\u003c\/strong\u003e 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.\u003c\/p\u003e \u003cp\u003eYou 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.\u003c\/p\u003e \u003cp\u003eYou will develop a toolbox of R data skills including:\u003c\/p\u003e \u003cul\u003e\n\u003cli\u003eReading and writing data\u003c\/li\u003e\n\u003cli\u003eInstalling and loading packages\u003c\/li\u003e\n\u003cli\u003eTransforming, tidying, and wrangling data\u003c\/li\u003e\n\u003cli\u003eApplying best-in-class exploratory data analysis techniques\u003c\/li\u003e\n\u003cli\u003eCreating compelling visualizations\u003c\/li\u003e\n\u003cli\u003eDeveloping supervised and unsupervised machine learning algorithms\u003c\/li\u003e\n\u003cli\u003eExecute hypothesis tests, including t-tests and chi-square tests for independence\u003c\/li\u003e\n\u003cli\u003eCompute expected values, Gini coefficients, and z-scores\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003eIs 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.\u003c\/p\u003e About the technology \u003cp\u003eAmazing 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.\u003c\/p\u003e","brand":"Manning Publications","offers":[{"title":"Default Title","offer_id":48740723294551,"sku":"9781633438682","price":45.04,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781633438682.jpg?v=1720055454"},{"product_id":"managing-data-quality-a-practical-guide-9781780174594","title":"Managing Data Quality: A practical guide","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eData is an increasingly important business asset and enabler for organisational activities. With growth in data sets and data volumes, it's becoming ever harder to manage. Data quality - the fitness for purpose of data - is a key aspect of data management and failure to understand it increases organisational risk and decreases efficiency and profitability.  This book explains data quality management in practical terms, focusing on three key areas - the nature of data in enterprises, the purpose and scope of data quality management, and implementing a data quality management system, in line with ISO 8000-61.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e'Written by two world-renowned experts, this book is the world's first comprehensive guide to the management of \"data quality\" and the ISO 8000 series.' -- Yoshiaki Sonoda * Engineering Manager and Data Quality evangelist, Mitsubishi Heavy Industries, Ltd. *\u003cbr\u003e'Tim King and Julian Schwarzenbach have a wealth of experience both in improving the way organizations manage information quality and in developing standards to support managing data quality, and as a result they bring not just ideas but examples of both good and bad practice that you can learn from...the best book on the subject I’ve read.' -- Matthew West * Director, Information Junction *\u003cbr\u003e'I often hear the phrase ‘we don’t trust the data’ and often this stems from poor data quality. Tim and Julian have brought their considerable experience together to give you a detailed and practical guide on how to improve the quality of your data, including real world examples to bring their points to life.' -- Caroline Carruthers * Chief Executive, Carruthers and Jackson, Co-author of ‘The CDO Playbook’ *\u003cbr\u003e'Managing Data Quality shines a light on the true nature of data quality, and its fundamental contribution to effective decision making.  The Authors guide the reader through an accessible and logical journey, one that is anchored in real-world application, providing valuable frameworks for data management professionals and business leaders alike.' -- Dr Mark Parsons * Chief Proposition Development Officer, Arcadis Gen *\u003cbr\u003e'This book is a very valuable and welcome addition to the literature on data quality best practice.' -- Nigel Turner * Principal Information Management Consultant, Global Data Strategy *\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePart 1: The Challenge of Enterprise Data\u003cul\u003e\n\u003cli\u003eThe Data Asset\u003c\/li\u003e\n\u003cli\u003eChallenges When Exploiting and Managing Data\u003c\/li\u003e\n\u003cli\u003eThe Impact of People on Data Quality\u003c\/li\u003e\n\u003cli\u003eCase Studies and Examples\u003c\/li\u003e\n\u003c\/ul\u003e Part 2: A Framework for Data Quality Management \u003cul\u003e\n\u003cli\u003eThe Purpose and Scope of Data Quality Management\u003c\/li\u003e\n\u003cli\u003eThe ISO 8000-61 Approach\u003c\/li\u003e\n\u003cli\u003eData Quality Management Capability Levels\u003c\/li\u003e\n\u003cli\u003eISO 8000-61 Processes\u003c\/li\u003e\n\u003cli\u003eThe Maturity Journey \u003c\/li\u003e\n\u003c\/ul\u003e Part 3: Implementing Data Quality Management \u003cul\u003e\n\u003cli\u003ePreparing the Organisation for Data Quality Management\u003c\/li\u003e\n\u003cli\u003eImplementing Data Quality Management\u003c\/li\u003e\n\u003cli\u003eThe Human Factor - Ensuring People Support Data Quality Management\u003c\/li\u003e\n\u003cli\u003eConclusions \u003c\/li\u003e\n\u003c\/ul\u003e \u003cbr\u003e","brand":"BCS Learning \u0026 Development Limited","offers":[{"title":"Default Title","offer_id":48740982194519,"sku":"9781780174594","price":28.49,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781780174594.jpg?v=1720056206"},{"product_id":"data-science-and-analytics-9781800438774","title":"Data Science and Analytics","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eData Science and Analytics\u003c\/i\u003e 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.\u003cbr\u003e  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. \u003ci\u003eData Science and Analytics \u003c\/i\u003ebrings together researchers, engineers and practitioners to encompass a wide and diverse range of topics in a wide range of fields. \u003cbr\u003e  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.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eChapter 1. Data Visualization \u003ci\u003eAarti Mehta Sharma\u003c\/i\u003e Chapter 2. Analytical aspects of Multimedia Big Data Computing and Future Scope \u003ci\u003eHiral R. Patel, Ajay M Patel Satyen M Parikh\u003c\/i\u003e Chapter 3. Predictive Analysis: Comprehensive study of popular open source tools \u003ci\u003eGauri Rajendra Virkar, Supriya Sunil Shinde\u003c\/i\u003e Chapter 4. Market Opportunities through Effective Market Analytics \u003ci\u003eShakti Ranjan Panigrahy\u003c\/i\u003e Chapter 5. Stochastic point process techniques for modelling problems in IoT and Marketing: Technique of “Random Point Process” (RPP) \u0026amp; “Product density” (PD)techniques in Stochastic Modeling \u003ci\u003eKSS Iyer, Madhavi Damle\u003c\/i\u003e Chapter 6. Real-Time Data Analytics - A Contemporary Approach towards Customer Relationship Management \u003ci\u003eSamir Yerpude\u003c\/i\u003e Chapter 7. Application of Big Data for Sustainable Rural Development with Special Reference to MNREGA \u003ci\u003eK. K. Tripathy, Sneha Kumari\u003c\/i\u003e Chapter 8. Challenges of Digital Technologies in The Development of Supply Chains: A Guide for Their Selection \u003ci\u003eJorge Tarifa-Fernandez, Almudena Martínez Aguilera, José Felipe Jiménez-Guerrero\u003c\/i\u003e","brand":"Emerald Publishing Limited","offers":[{"title":"Default Title","offer_id":48741740282199,"sku":"9781800438774","price":69.34,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781800438774.jpg?v=1720058640"},{"product_id":"the-elements-of-big-data-value-foundations-of-the-research-and-innovation-ecosystem-9783030681784","title":"The Elements of Big Data Value: Foundations of","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis open access book presents the foundations of the Big Data research and innovation ecosystem and the associated enablers that facilitate delivering value from data for business and society. It provides insights into the key elements for research and innovation, technical architectures, business models, skills, and best practices to support the creation of data-driven solutions and organizations.   \u003cbr\u003e\u003c\/p\u003e  \u003cp\u003eThe book is a compilation of selected high-quality chapters covering best practices, technologies, experiences, and practical recommendations on research and innovation for big data. The contributions are grouped into four parts: \u003c\/p\u003e  \u003cp\u003e·         \u003ci\u003ePart I: Ecosystem Elements of Big Data Value\u003c\/i\u003e focuses on establishing the big data value ecosystem using a holistic approach to make it attractive and valuable to all stakeholders.  \u003c\/p\u003e  \u003cp\u003e·         \u003ci\u003ePart II: Research and Innovation Elements of Big Data Value\u003c\/i\u003e details the key technical and capability challenges to be addressed for delivering big data value.  \u003c\/p\u003e  \u003cp\u003e·         \u003ci\u003ePart III: Business, Policy, and Societal Elements of Big Data Value\u003c\/i\u003e investigates the need to make more efficient use of big data and understanding that data is an asset that has significant potential for the economy and society.  \u003c\/p\u003e  \u003cp\u003e·         \u003ci\u003ePart IV: Emerging Elements of Big Data Value \u003c\/i\u003eexplores the critical elements to maximizing the future potential of big data value. \u003c\/p\u003e\u003cp\u003e \u003cbr\u003e\u003c\/p\u003e  \u003cp\u003eOverall, readers are provided with insights which can support them in creating data-driven solutions, organizations, and productive data ecosystems. The material represents the results of a collective effort undertaken by the European data community as part of the Big Data Value Public-Private Partnership (PPP) between the European Commission and the Big Data Value Association (BDVA) to boost data-driven digital transformation. \u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePart I: Ecosystem Elements of Big Data Value.- The European Big Data Value Ecosystem.- Stakeholder Analysis of Data Ecosystems.- A Roadmap to Drive Adoption of Data Ecosystems.- Achievements and Impact of the Big Data Value Public-Private Partnership: The Story so Far.- Part II: Research and Innovation Elements of Big Data Value.- Technical Research Priorities for Big Data.- A Reference Model for Big Data Technologies.- Data Protection in the Era of Artificial Intelligence: Trends, Existing Solutions and Recommendations for Privacy-Preserving Technologies.- A Best Practice Framework for Centres of Excellence in Big Data and Artificial Intelligence.- Data Innovation Spaces.- Part III: Business, Policy, and Societal Elements of Big Data Value.- Big Data Value Creation by Example.- Business Models and Ecosystem for Big Data.- Innovation in Times of Big Data and AI: Introducing the Data-Driven Innovation (DDI) Framework.- The Road to Big Data Standardisation.- The Role of Data Regulation in Shaping AI: An Overview of Challenges and Recommendations for SMEs.- Part IV: Emerging Elements of Big Data Value.- Data Economy 2.0: From Big Data Value to AI Value and a European Data Space.","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":48743044841815,"sku":"9783030681784","price":34.99,"currency_code":"GBP","in_stock":true}]},{"product_id":"modern-data-strategy-9783319689920","title":"Modern Data Strategy","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis book contains practical steps business users can take to implement data management in a number of ways, including data governance, data architecture, master data management, business intelligence, and others. It defines data strategy, and covers chapters that illustrate how to align a data strategy with the business strategy, a discussion on valuing data as an asset, the evolution of data management, and who should oversee a data strategy. This provides the user with a good understanding of what a data strategy is and its limits.\u003c\/p\u003e  \u003cp\u003eCritical to a data strategy is the incorporation of one or more data management domains. Chapters on key data management domains—data governance, data architecture, master data management and analytics, offer the user a practical approach to data management execution within a data strategy. The intent is to enable the user to identify how execution on one or more data management domains can help solve business issues.\u003c\/p\u003e  \u003cp\u003eThis book is intended for business users who work with data, who need to manage one or more aspects of the organization’s data, and who want to foster an integrated approach for how enterprise data is managed. This book is also an excellent reference for students studying computer science and business management or simply for someone who has been tasked with starting or improving existing data management.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1 Evolution to Modern Data Management.- 2 Big Data and Data Management.- 3 Valuing Data as an Asset.- 4 Physical Asset Management vs. Data Management.- 5 Leading Data Strategy.- 6 Implementing a Data Strategy.- 7 Overview of Data Management Frameworks.- 8 Data Governance.- 9 Data Architecture.- 10 Master Data Management.- 11 Data Quality.- 12 Data Warehousing and Business Intelligence.- 13 Data Analytics.- 14 Data Privacy.- 15 Data Security.- 16 Metadata.- 17 Records Management.","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":48743102153047,"sku":"9783319689920","price":999.99,"currency_code":"GBP","in_stock":false}]},{"product_id":"big-data-im-gesundheitswesen-kompakt-konzepte-losungen-visionen-9783658210953","title":"Big Data im Gesundheitswesen kompakt: Konzepte,","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eDas kompakte Fachbuch gibt einen Überblick über die Möglichkeiten von „Big Data“ im Gesundheitswesen und beschreibt anhand von ausgewählten Szenarien mögliche Einsatzgebiete.\u003c\/p\u003e\u003cp\u003eDie Autoren erläutern zentrale Systemkomponenten und IT-Standards und thematisieren anhand wichtiger Daten des Gesundheitswesens die Notwendigkeit der Strukturierung und Modellierung von Daten. Das Buch gibt Hinweise wie Geschäftsprozesse im Gesundheitswesen dokumentiert, analysiert und verbessert werden können. Anwendungsszenarien, wie die Datenanalysen für Krankenhäuser, Labore, Versicherungen und die Pharmaindustrie, zeigen die praktische Relevanz des Themas. Aber auch rechtliche und ethische Aspekte werden inhaltlich angeschnitten.\u003c\/p\u003e\u003cp\u003eEin Buch für Entscheider in der medizinischen Leitung und Verwaltung von Krankenhäusern, Fachleute sowie niedergelassene Ärzte und Apotheker, aber auch Personen in Ausbildung und Studium im Gesundheitswesen. \u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eBig-Data-Analytics im Gesundheitswesen - Medizin - Verwaltung - Forschung: Anwendungsgebiete für Big-Data-Analytics - Gesetzliche Rahmenbedingungen und Big-Data-Ethik\u003c\/p\u003e","brand":"Springer Fachmedien Wiesbaden","offers":[{"title":"Default Title","offer_id":48743137837399,"sku":"9783658210953","price":13.49,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783658210953.jpg?v=1720064275"},{"product_id":"exploring-big-historical-data-the-historians-macroscope-9789811243981","title":"Exploring Big Historical Data: The Historian's","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eEvery 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.","brand":"World Scientific Publishing Co Pte Ltd","offers":[{"title":"Default Title","offer_id":48743281459543,"sku":"9789811243981","price":42.75,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9789811243981.jpg?v=1720064909"},{"product_id":"definitive-guide-to-dax-the-business-intelligence-for-microsoft-power-bi-sql-server-analysis-services-and-excel-9781509306978","title":"Definitive Guide to DAX, The: Business","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis 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.  \u003cbr\u003e   \u003cbr\u003e  \u003cb\u003ePerform powerful data analysis with DAX for Microsoft SQL Server Analysis Services, Excel, and Power BI\u003c\/b\u003e  \u003cbr\u003e  \u003cul\u003e\n\u003cli\u003eMaster core DAX concepts, including calculated columns, measures, and error handling\u003c\/li\u003e\n\u003cli\u003eUnderstand evaluation contexts and the CALCULATE and CALCULATETABLE functions\u003c\/li\u003e\n\u003cli\u003ePerform time-based calculations: YTD, MTD, previous year, working days, and more\u003c\/li\u003e\n\u003cli\u003eWork with expanded tables, complex functions, and elaborate DAX expressions\u003c\/li\u003e\n\u003cli\u003ePerform calculations over hierarchies, including parent\/child hierarchies\u003c\/li\u003e\n\u003cli\u003eUse DAX to express diverse and unusual relationships\u003c\/li\u003e\n\u003cli\u003eMeasure DAX query performance with SQL Server Profiler and DAX Studio\u003c\/li\u003e\n\u003c\/ul\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cul\u003e\n\u003cli\u003eIntroduction \u003c\/li\u003e\n\u003cli\u003eChapter 1: What is DAX? \u003c\/li\u003e\n\u003cli\u003eChapter 2: Introducing DAX \u003c\/li\u003e\n\u003cli\u003eChapter 3: Using basic table functions \u003c\/li\u003e\n\u003cli\u003eChapter 4: Understanding evaluation contexts \u003c\/li\u003e\n\u003cli\u003eChapter 5: Understanding CALCULATE and CALCULATETABLE \u003c\/li\u003e\n\u003cli\u003eChapter 6: DAX examples \u003c\/li\u003e\n\u003cli\u003eChapter 7: Time intelligence calculations \u003c\/li\u003e\n\u003cli\u003eChapter 8: Statistical functions \u003c\/li\u003e\n\u003cli\u003eChapter 9: Advanced table functions \u003c\/li\u003e\n\u003cli\u003eChapter 10: Advanced evaluation context \u003c\/li\u003e\n\u003cli\u003eChapter 11: Handling hierarchies \u003c\/li\u003e\n\u003cli\u003eChapter 12: Advanced relationships \u003c\/li\u003e\n\u003cli\u003eChapter 13: The VertiPaq engine \u003c\/li\u003e\n\u003cli\u003eChapter 14: Optimizing data models \u003c\/li\u003e\n\u003cli\u003eChapter 15: Analyzing DAX query plans \u003c\/li\u003e\n\u003cli\u003eChapter 16: Optimizing DAX \u003c\/li\u003e\n\u003cli\u003eIndex\u003c\/li\u003e\n\u003c\/ul\u003e","brand":"Microsoft Press,U.S.","offers":[{"title":"Default Title","offer_id":48861589373271,"sku":"9781509306978","price":34.84,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781509306978.jpg?v=1722247393"},{"product_id":"queer-data-studies-9780295751979","title":"Queer Data Studies","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e","brand":"University of Washington Press","offers":[{"title":"Default Title","offer_id":48864323731799,"sku":"9780295751979","price":29.66,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780295751979.jpg?v=1722271414"},{"product_id":"weapons-of-math-destruction-9780553418835","title":"Weapons of Math Destruction","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cb\u003e\u003ci\u003eLonglisted for the National Book Award\u003cbr\u003e\u003c\/i\u003eNew York Times \u003ci\u003eBestseller\u003c\/i\u003e\u003c\/b\u003e\u003cp\u003e\u003c\/p\u003eA former...","brand":"Random House USA Inc","offers":[{"title":"Default Title","offer_id":48865066189143,"sku":"9780553418835","price":11.7,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780553418835.jpg?v=1722273489"},{"product_id":"painting-by-numbers-9780691192451","title":"Painting by Numbers","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eAn innovative application of economic methods to the study of art history, demonstrating that new insights can be uncovered by using quantitative and qualitative methods together, which sheds light on longstanding disciplinary inequities\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"Winner of a Millard Meiss Publication Fund Grant, College Art Association\"\u003cbr\u003e\"\u003ci\u003ePainting by Numbers\u003c\/i\u003e…[is] careful and systematic…it is a solid demonstration that “counting things” matters. It leaves audiences to wonder what work the book will inspire as other researchers draw from the quantitative foundation Greenwald has established… [I]t’s clear that the author’s expertise in art and data pair brilliantly” –Lydia Pyne, \u003ci\u003eHyperallergic\u003c\/i\u003e\"\u003cbr\u003e\"The real power of [Painting by Numbers] is. . . . prompting art historians to ask questions about the values underpinning their definition of their objects of study. . . . [Diana Greenwald] has done a valuable service to the field in asking us to rethink our fundamental categories of disciplinary concern and our responsibilities to the vast range of visual and material culture that might fall within their purview.\" * CAA Reviews *\u003cbr\u003e\"Diana Seave Greenwald’s \u003ci\u003ePainting by Numbers: Data-Driven Histories of Nineteenth-Century Art\u003c\/i\u003e is an ambitious study that synthesizes two disparate approaches of scholarship: art history and economic analysis. . . . Greenwald is a pioneer in the field who is willing to explore new perspectives and challenge past presumptions. The book paves the way for similar interdisciplinary studies to follow. . . . \u003ci\u003ePainting by Numbers\u003c\/i\u003e shows the promise of what can be achieved when an abundance of information is wedded with insightful scholarship.\"\u003cb\u003e---Matt Garklavs, \u003ci\u003eARLIS\/NA Reviews\u003c\/i\u003e\u003c\/b\u003e\u003cbr\u003e\"[Diana Greenwald] presents novel evidence on the artistic production of the nineteenth-century in France, the USA, and England and focusses on crucial topics in the art history of that period, namely, industrialization, gender, and the history of empire, providing new points of view. . . . [\u003ci\u003ePainting by Numbers\u003c\/i\u003e] represents a concrete application of the benefits of an interdisciplinary approach in humanities and social sciences.\"\u003cb\u003e---Laura Paganl, \u003ci\u003eJournal of Cultural Economics\u003c\/i\u003e\u003c\/b\u003e\u003cbr\u003e\"[A] great benefit to art historians unpracticed in economic theory.\"\u003cb\u003e---Elizabeth L. Block, \u003ci\u003ePanorama\u003c\/i\u003e\u003c\/b\u003e\u003cbr\u003e\"\u003ci\u003ePainting By Numbers\u003c\/i\u003e offers methods and interpretations that may revise art historians’ assumptions about what we do and how we do it.\"\u003cb\u003e---Julie Codell, \u003ci\u003eWinterthur Portfolio\u003c\/i\u003e\u003c\/b\u003e\u003cbr\u003e\"Using hard, quantitative data in order to test, critique or support conventional wisdom is very unusual in art-historical research. Painting by Numbers succeeds in making a convincing case for that kind of study, which makes it a model of methodological innovation, and a very welcome one.\"\u003cb\u003e---Jorge Sebastián Lozano, \u003ci\u003eArt History\u003c\/i\u003e\u003c\/b\u003e","brand":"Princeton University Press","offers":[{"title":"Default Title","offer_id":48865543127383,"sku":"9780691192451","price":28.8,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780691192451.jpg?v=1722274477"},{"product_id":"data-grab-9780753560211","title":"Data Grab","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003e\u003ci\u003eYour \u003c\/i\u003elife online is \u003ci\u003etheir \u003c\/i\u003eproduct.\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eIn the past, colonialism was a landgrab of natural resources, exploitative labour and private property from countries around the world. It promised to modernise and civilise, but actually sought to control. It stole from native populations and made them sign contracts they didn't understand. It took resources just because they were there.\u003cbr\u003e\u003cbr\u003eColonialism has not disappeared  it has taken on a new form.\u003cbr\u003e\u003cbr\u003eIn the new world order, data is the new oil. Big Tech companies are grabbing our most basic natural resources  our data  exploiting our labour and connections, and repackaging our information to control our views, track our movements, record our conversations and discriminate against us. Every time we unthinkingly click Accept' on Terms and Conditions, we allow our most personal information to kept indefinitely, repackaged by big Tech companies to control and exploit us for their own profit.\u003cbr\u003e\u003cbr\u003eIn this searin\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eI wish that \u003ci\u003eData Grab\u003c\/i\u003e was required reading when I was a graduate student working in the field of AI. Perspectives like these are crucial if we are to break the colonial paradigm that pervades computing disciplines\u003c\/b\u003e -- Timnit Gebru, founder of the Distributed AI Research Institute\u003cbr\u003e\u003cb\u003eA blistering, vital exposure of the predatory world of data colonialism. In this vivid and passionately written book, Mejias and Couldry urge us to wake up to the invasive and extractive world of today’s Big Tech\u003c\/b\u003e -- Mike Savage, author of 'Social Class in the 21st Century'\u003cbr\u003e\u003cb\u003eRemarkable...\u003c\/b\u003e\u003ci\u003e Data Grab\u003c\/i\u003e helps us understand that \u003cb\u003ethe historical and ongoing relations of power have extended to the realm of data\u003c\/b\u003e, a new raw material of digital capitalism. \u003cb\u003eMejias and Couldry place us on a path to recognise, resist, and challenge\u003c\/b\u003e these forces -- Dr Ramesh Srinivasan, Professor at the UCLA Department of Information Studies and Director of UC Digital Cultures Lab\u003cbr\u003eAs in their previous work, Mejias and Couldry\u003cb\u003e show how important it is to take the perspective of the colonized, not the colonizer, in explaining how the digital world is governed.\u003c\/b\u003e \u003ci\u003eData Grab\u003c\/i\u003e\u003cb\u003e offers important insights \u003c\/b\u003einto how we should analyse power and counter-power in terms of data control. I particularly recommend this book for providing examples of local and vocal initiatives across various continents. \u003cb\u003eA true eye-opener\u003c\/b\u003e -- José van Dijck, Distinguished Professor of Media and Digital Society, Utrecht University\u003cbr\u003eIn this \u003cb\u003eessential and original \u003c\/b\u003ework, Mejias and Couldry lay out a \u003cb\u003epowerful and persuasive\u003c\/b\u003e analysis of the logical continuity between modern colonialism and the extraction of data by Big Tech and its platforms. \u003cb\u003eTheir call to resist data colonialism could not be more urgent or more timely\u003c\/b\u003e -- Jeremy Gilbert, author of 'Hegemony Now: How Big Tech and Wall Street Won the World' and 'Twenty-First Century Socialism'\u003c\/p\u003e","brand":"Ebury Publishing","offers":[{"title":"Default Title","offer_id":48865770078551,"sku":"9780753560211","price":15.29,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780753560211.jpg?v=1722275479"},{"product_id":"snowflake-the-definitive-guide-9781098103828","title":"Snowflake  The Definitive Guide","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eSnowflake's ability to eliminate data silos and run workloads from a single platform creates opportunities to democratize data analytics, allowing users within an organization to make data-driven decisions. This clear, comprehensive guide will show you how to build integrated data applications and develop new revenue streams based on data.","brand":"O'Reilly Media","offers":[{"title":"Default Title","offer_id":48866330837335,"sku":"9781098103828","price":47.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781098103828.jpg?v=1722278164"},{"product_id":"fundamentals-of-data-engineering-9781098108304","title":"Fundamentals of Data Engineering","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eWith this practical book, you'll learn how to plan and build systems to serve the needs of your organization and customers by evaluating the best technologies available through the framework of the data engineering lifecycle.","brand":"O'Reilly Media","offers":[{"title":"Default Title","offer_id":48866330935639,"sku":"9781098108304","price":47.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781098108304.jpg?v=1722278165"},{"product_id":"r-packages-9781098134945","title":"R Packages","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eIdeal 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.","brand":"O'Reilly Media","offers":[{"title":"Default Title","offer_id":48866332082519,"sku":"9781098134945","price":39.74,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781098134945.jpg?v=1722278170"},{"product_id":"delta-lake-up-and-running-9781098139728","title":"Delta Lake Up and Running","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eWith the surge in big data and AI, organizations can rapidly create data products. However, the effectiveness of their analytics and machine learning models depends on the data's quality. Delta Lake's open source format offers a robust lakehouse framework over platforms like Amazon S3, ADLS, and GCS.","brand":"O'Reilly Media","offers":[{"title":"Default Title","offer_id":48866332377431,"sku":"9781098139728","price":39.74,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781098139728.jpg?v=1722278172"},{"product_id":"the-data-warehouse-toolkit-9781118530801","title":"The Data Warehouse Toolkit","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis revised and updated edition of the bestseller provides a complete library of dimensional modeling techniques, the most comprehensive collection ever written.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eIntroduction xxvii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Data Warehousing, Business Intelligence, and Dimensional Modeling Primer 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDifferent Worlds of Data Capture and Data Analysis 2\u003c\/p\u003e \u003cp\u003eGoals of Data Warehousing and Business Intelligence 3\u003c\/p\u003e \u003cp\u003eDimensional Modeling Introduction 7\u003c\/p\u003e \u003cp\u003eKimball’s DW\/BI Architecture 18\u003c\/p\u003e \u003cp\u003eAlternative DW\/BI Architectures 26\u003c\/p\u003e \u003cp\u003eDimensional Modeling Myths 30\u003c\/p\u003e \u003cp\u003eMore Reasons to Think Dimensionally 32\u003c\/p\u003e \u003cp\u003eAgile Considerations 34\u003c\/p\u003e \u003cp\u003eSummary 35\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 \u003c\/b\u003e\u003cb\u003eKimball Dimensional Modeling Techniques Overview 37\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eFundamental Concepts 37\u003c\/p\u003e \u003cp\u003eBasic Fact Table Techniques 41\u003c\/p\u003e \u003cp\u003eBasic Dimension Table Techniques 46\u003c\/p\u003e \u003cp\u003eIntegration via Conformed Dimensions 50\u003c\/p\u003e \u003cp\u003eDealing with Slowly Changing Dimension Attributes 53\u003c\/p\u003e \u003cp\u003eDealing with Dimension Hierarchies 56\u003c\/p\u003e \u003cp\u003eAdvanced Fact Table Techniques 58\u003c\/p\u003e \u003cp\u003eAdvanced Dimension Techniques 62\u003c\/p\u003e \u003cp\u003eSpecial Purpose Schemas 67\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 \u003c\/b\u003e\u003cb\u003eRetail Sales 69\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eFour-Step Dimensional Design Process 70\u003c\/p\u003e \u003cp\u003eRetail Case Study 72\u003c\/p\u003e \u003cp\u003eDimension Table Details 79\u003c\/p\u003e \u003cp\u003eRetail Schema in Action 94\u003c\/p\u003e \u003cp\u003eRetail Schema Extensibility 95\u003c\/p\u003e \u003cp\u003eFactless Fact Tables 97\u003c\/p\u003e \u003cp\u003eDimension and Fact Table Keys 98\u003c\/p\u003e \u003cp\u003eResisting Normalization Urges 104\u003c\/p\u003e \u003cp\u003eSummary 109\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 \u003c\/b\u003e\u003cb\u003eInventory 111\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eValue Chain Introduction 111\u003c\/p\u003e \u003cp\u003eInventory Models 112\u003c\/p\u003e \u003cp\u003eFact Table Types 119\u003c\/p\u003e \u003cp\u003eValue Chain Integration 122\u003c\/p\u003e \u003cp\u003eEnterprise Data Warehouse Bus Architecture 123\u003c\/p\u003e \u003cp\u003eConformed Dimensions 130\u003c\/p\u003e \u003cp\u003eConformed Facts 138\u003c\/p\u003e \u003cp\u003eSummary 139\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 \u003c\/b\u003e\u003cb\u003eProcurement 141\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eProcurement Case Study 141\u003c\/p\u003e \u003cp\u003eProcurement Transactions and Bus Matrix 142\u003c\/p\u003e \u003cp\u003eSlowly Changing Dimension Basics 147\u003c\/p\u003e \u003cp\u003eHybrid Slowly Changing Dimension Techniques 159\u003c\/p\u003e \u003cp\u003eSlowly Changing Dimension Recap 164\u003c\/p\u003e \u003cp\u003eSummary 165\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 \u003c\/b\u003e\u003cb\u003eOrder Management 167\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eOrder Management Bus Matrix 168\u003c\/p\u003e \u003cp\u003eOrder Transactions 168\u003c\/p\u003e \u003cp\u003eInvoice Transactions 187\u003c\/p\u003e \u003cp\u003eAccumulating Snapshot for Order Fulfillment Pipeline 194\u003c\/p\u003e \u003cp\u003eSummary 199\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 \u003c\/b\u003e\u003cb\u003eAccounting 201\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAccounting Case Study and Bus Matrix 202\u003c\/p\u003e \u003cp\u003eGeneral Ledger Data 203\u003c\/p\u003e \u003cp\u003eBudgeting Process 210\u003c\/p\u003e \u003cp\u003eDimension Attribute Hierarchies 214\u003c\/p\u003e \u003cp\u003eConsolidated Fact Tables 224\u003c\/p\u003e \u003cp\u003eRole of OLAP and Packaged Analytic Solutions 226\u003c\/p\u003e \u003cp\u003eSummary 227\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 \u003c\/b\u003e\u003cb\u003eCustomer Relationship Management 229\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eCRM Overview 230\u003c\/p\u003e \u003cp\u003eCustomer Dimension Attributes 233\u003c\/p\u003e \u003cp\u003eBridge Tables for Multivalued Dimensions 245\u003c\/p\u003e \u003cp\u003eComplex Customer Behavior 249\u003c\/p\u003e \u003cp\u003eCustomer Data Integration Approaches 256\u003c\/p\u003e \u003cp\u003eLow Latency Reality Check 260\u003c\/p\u003e \u003cp\u003eSummary 261\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 \u003c\/b\u003e\u003cb\u003eHuman Resources Management 263\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eEmployee Profile Tracking 263\u003c\/p\u003e \u003cp\u003eHeadcount Periodic Snapshot 267\u003c\/p\u003e \u003cp\u003eBus Matrix for HR Processes 268\u003c\/p\u003e \u003cp\u003ePackaged Analytic Solutions and Data Models 270\u003c\/p\u003e \u003cp\u003eRecursive Employee Hierarchies 271\u003c\/p\u003e \u003cp\u003eMultivalued Skill Keyword Attributes 274\u003c\/p\u003e \u003cp\u003eSurvey Questionnaire Data 277\u003c\/p\u003e \u003cp\u003eSummary 279\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 \u003c\/b\u003e\u003cb\u003eFinancial Services 281\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBanking Case Study and Bus Matrix 282\u003c\/p\u003e \u003cp\u003eDimension Triage to Avoid Too Few Dimensions 283\u003c\/p\u003e \u003cp\u003eSupertype and Subtype Schemas for Heterogeneous Products 293\u003c\/p\u003e \u003cp\u003eHot Swappable Dimensions 296\u003c\/p\u003e \u003cp\u003eSummary 296\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 \u003c\/b\u003e\u003cb\u003eTelecommunications 297\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eTelecommunications Case Study and Bus Matrix 297\u003c\/p\u003e \u003cp\u003eGeneral Design Review Considerations 299\u003c\/p\u003e \u003cp\u003eDesign Review Guidelines 304\u003c\/p\u003e \u003cp\u003eDraft Design Exercise Discussion 306\u003c\/p\u003e \u003cp\u003eRemodeling Existing Data Structures 309\u003c\/p\u003e \u003cp\u003eGeographic Location Dimension 310\u003c\/p\u003e \u003cp\u003eSummary 310\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 \u003c\/b\u003e\u003cb\u003eTransportation 311\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAirline Case Study and Bus Matrix 311\u003c\/p\u003e \u003cp\u003eExtensions to Other Industries 317\u003c\/p\u003e \u003cp\u003eCombining Correlated Dimensions 318\u003c\/p\u003e \u003cp\u003eMore Date and Time Considerations 321\u003c\/p\u003e \u003cp\u003eLocalization Recap 324\u003c\/p\u003e \u003cp\u003eSummary 324\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 \u003c\/b\u003e\u003cb\u003eEducation 325\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eUniversity Case Study and Bus Matrix 325\u003c\/p\u003e \u003cp\u003eAccumulating Snapshot Fact Tables 326\u003c\/p\u003e \u003cp\u003eFactless Fact Tables 329\u003c\/p\u003e \u003cp\u003eMore Educational Analytic Opportunities 336\u003c\/p\u003e \u003cp\u003eSummary 336\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 \u003c\/b\u003e\u003cb\u003eHealthcare 339\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eHealthcare Case Study and Bus Matrix 339\u003c\/p\u003e \u003cp\u003eClaims Billing and Payments 342\u003c\/p\u003e \u003cp\u003eElectronic Medical Records 348\u003c\/p\u003e \u003cp\u003eFacility\/Equipment Inventory Utilization 351\u003c\/p\u003e \u003cp\u003eDealing with Retroactive Changes 351\u003c\/p\u003e \u003cp\u003eSummary 352\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 \u003c\/b\u003e\u003cb\u003eElectronic Commerce 353\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eClickstream Source Data 353\u003c\/p\u003e \u003cp\u003eClickstream Dimensional Models 357\u003c\/p\u003e \u003cp\u003eIntegrating Clickstream into Web Retailer’s Bus Matrix 368\u003c\/p\u003e \u003cp\u003eProfitability Across Channels Including Web 370\u003c\/p\u003e \u003cp\u003eSummary 373\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 \u003c\/b\u003e\u003cb\u003eInsurance 375\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eInsurance Case Study 376\u003c\/p\u003e \u003cp\u003ePolicy Transactions 379\u003c\/p\u003e \u003cp\u003ePremium Periodic Snapshot 385\u003c\/p\u003e \u003cp\u003eMore Insurance Case Study Background 388\u003c\/p\u003e \u003cp\u003eClaim Transactions 390\u003c\/p\u003e \u003cp\u003eClaim Accumulating Snapshot 392\u003c\/p\u003e \u003cp\u003ePolicy\/Claim Consolidated Periodic Snapshot 395\u003c\/p\u003e \u003cp\u003eFactless Accident Events 396\u003c\/p\u003e \u003cp\u003eCommon Dimensional Modeling Mistakes to Avoid 397\u003c\/p\u003e \u003cp\u003eSummary 401\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 \u003c\/b\u003e\u003cb\u003eKimball DW\/BI Lifecycle Overview 403\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eLifecycle Roadmap 404\u003c\/p\u003e \u003cp\u003eLifecycle Launch Activities 406\u003c\/p\u003e \u003cp\u003eLifecycle Technology Track 416\u003c\/p\u003e \u003cp\u003eLifecycle Data Track 420\u003c\/p\u003e \u003cp\u003eLifecycle BI Applications Track 422\u003c\/p\u003e \u003cp\u003eLifecycle Wrap-up Activities 424\u003c\/p\u003e \u003cp\u003eCommon Pitfalls to Avoid 426\u003c\/p\u003e \u003cp\u003eSummary 427\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 \u003c\/b\u003e\u003cb\u003eDimensional Modeling Process and Tasks 429\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eModeling Process Overview 429\u003c\/p\u003e \u003cp\u003eGet Organized 431\u003c\/p\u003e \u003cp\u003eDesign the Dimensional Model 434\u003c\/p\u003e \u003cp\u003eSummary 441\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 \u003c\/b\u003e\u003cb\u003eETL Subsystems and Techniques 443\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eRound Up the Requirements 444\u003c\/p\u003e \u003cp\u003eThe 34 Subsystems of ETL 449\u003c\/p\u003e \u003cp\u003eExtracting: Getting Data into the Data Warehouse 450\u003c\/p\u003e \u003cp\u003eCleaning and Conforming Data 455\u003c\/p\u003e \u003cp\u003eDelivering: Prepare for Presentation 463\u003c\/p\u003e \u003cp\u003eManaging the ETL Environment 483\u003c\/p\u003e \u003cp\u003eSummary 496\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20 \u003c\/b\u003e\u003cb\u003eETL System Design and Development Process and Tasks 497\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eETL Process Overview 497\u003c\/p\u003e \u003cp\u003eDevelop the ETL Plan 498\u003c\/p\u003e \u003cp\u003eDevelop One-Time Historic Load Processing 503\u003c\/p\u003e \u003cp\u003eDevelop Incremental ETL Processing 512\u003c\/p\u003e \u003cp\u003eReal-Time Implications 520\u003c\/p\u003e \u003cp\u003eSummary 526\u003c\/p\u003e \u003cp\u003e\u003cb\u003e21 \u003c\/b\u003e\u003cb\u003eBig Data Analytics 527\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBig Data Overview 527\u003c\/p\u003e \u003cp\u003eRecommended Best Practices for Big Data 531\u003c\/p\u003e \u003cp\u003eSummary 542\u003c\/p\u003e \u003cp\u003eIndex 543\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":48866373206359,"sku":"9781118530801","price":47.5,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781118530801.jpg?v=1722278339"},{"product_id":"gdpr-for-dummies-9781119546092","title":"GDPR For Dummies","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":48866401747287,"sku":"9781119546092","price":20.39,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781119546092.jpg?v=1722278472"},{"product_id":"artificial-intelligence-for-hr-9781398604001","title":"Artificial Intelligence for HR","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eBen Eubanks\u003c\/b\u003e is an HR industry analyst and influencer. He is the Principal Analyst at Lighthouse Research \u0026amp; Advisory where he oversees the development of research, assets and insights to support HR, learning and talent executives. Based in Huntsville, Alabama, he is also the founder of HR community upstartHR, the co-founder of the HRevolution movement and host of the We're Human podcast.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"I found Ben's book to be a very useful distillation of a complex and increasingly important area of HR capability and investment. My students, future HR leaders, gained tremendous insight into AI for HR and the facilitating role it will soon play in the best organizations for people. \u003ci\u003eArtificial Intelligence for HR\u003c\/i\u003e demystifies a complex topic and contextualizes what some regard as just the latest HR fad. It is a well-organized and well-written book worth reading by HR leaders, educators and students. Despite the title, it's really all about people!\" * John Haggerty, Ph.D. Senior Lecturer, HR Studies at Cornell University *\u003cbr\u003e\"The HR landscape has changed dramatically over the last three years. As the \"War for Talent\" continues to escalate, the value of the business minded, technologically aware HR professional has increased. What's needed are HR professionals who view AI and other technology as an augmentation to HR, giving them the ability to be business consultants versus the more common, reactive HR. Ben's book explores how HR professionals are using new technology to transform their businesses and the industry as a whole. If you are in search of a data-packed, transformational book, I'd encourage you to take the time to dive deep, take notes, and transform your own business with what you learn.\" * Trent Cotton, VP Talent Acquisition and Retention at Bureau Veritas and Author of Sprint Recruiting *\u003cbr\u003e\"Ben Eubanks' \u003ci\u003eArtificial Intelligence for HR\u003c\/i\u003e is a must-read for those involved with human resources. Ben's dedication and passion for HR promotes instrumental success for organizations and leaders alike. This book offers insights into the ever-changing HR environment and navigation through new AI technology while still promoting the evolutionary development of human resource professionals.\" * Jamie McCall, Director, Talent Acquisition at The Henry M. Jackson Foundation for the Advancement of Military Medicine *\u003cbr\u003e\"Ben does an excellent job helping HR leaders who have historically been trained with deep departmental expertise apply new AI technologies to their domains. More importantly, he helps them transition to strategically addressing the workforce with consumer experiences which earn their loyalty, net promotion, retention, and engagement the same as we experience in our personal lives.\" * Randy Womack, CEO, Socrates.ai *\u003cbr\u003e\"Today's HR leaders must understand technology to thrive in the modern business environment and AI is one of the most transformational technologies of our age. This is often an intimidating reality for HR professionals since many signed up not realizing digital acumen was a top skill for career success. Fortunately, Ben has translated his wealth of experience and expertise into an easy-to-follow handbook. He simplifies the complexity of AI and highlights pragmatic opportunities for every HR function. It's a must-read for any HR professional looking to thrive in today's disruptive environment.\" * Christopher Lind, Chief Learning Officer at ChenMed and Founder, Learning Sharks *\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cul\u003e\u003cli\u003e\u003cul\u003e\n\u003cli\u003eChapter - 01: A snapshot of HR today;\u003c\/li\u003e\n\u003cli\u003eChapter - 02: The basics of artificial intelligence;\u003c\/li\u003e\n\u003cli\u003eChapter - 03: General AI Applications with HCM;\u003c\/li\u003e\n\u003cli\u003eChapter - 04: Core HR and workforce management;\u003c\/li\u003e\n\u003cli\u003eChapter - 05: Talent acquisition;\u003c\/li\u003e\n\u003cli\u003eChapter - 06: Learning and development;\u003c\/li\u003e\n\u003cli\u003eChapter - 07: Talent management;\u003c\/li\u003e\n\u003cli\u003eChapter - 08: Challenges of adopting AI technology;\t\u003c\/li\u003e\n\u003cli\u003eChapter - 09: HR skills of the future\t\u003c\/li\u003e\n\u003c\/ul\u003e\u003c\/li\u003e\u003c\/ul\u003e","brand":"Kogan Page Ltd","offers":[{"title":"Default Title","offer_id":48866638790999,"sku":"9781398604001","price":32.99,"currency_code":"GBP","in_stock":true}]},{"product_id":"organizational-planning-and-analysis-9781398605817","title":"Organizational Planning and Analysis","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eRupert Morrison\u003c\/b\u003e 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 \u003cb\u003e\u003ci\u003eData-Driven Organization Design\u003c\/i\u003e\u003c\/b\u003e, also published by Kogan Page.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"Building a forward-looking OP\u0026amp;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 *\u003cbr\u003e\"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 *\u003cbr\u003e\"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 *\u003cbr\u003e\"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 *\u003cbr\u003e\"In his new book \u003ci\u003eOrganizational Planning and Analysis\u003c\/i\u003e, 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\u0026amp;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 *\u003cbr\u003e\"If you enjoyed \u003ci\u003eData-Driven Organization Design\u003c\/i\u003e, the prequel to this book, then you'll love \u003ci\u003eOrganizational Planning and Analysis\u003c\/i\u003e. 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 \u003ci\u003eDigital HR Leaders\u003c\/i\u003e 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. *\u003cbr\u003e\"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 *\u003cbr\u003e\"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 \u0026amp; Design Lead, Accenture *\u003cbr\u003e\"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 *\u003cbr\u003e\"Simplicity is something hard to achieve. OP\u0026amp;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 \u0026amp; Communications at Headspring Executive Development *\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cul\u003e\n\u003cli\u003eSection - ONE: Introduction;\u003c\/li\u003e\n\u003cli\u003e\u003cul\u003e\n\u003cli\u003eChapter - 1.1: Organizational planning and analytics;\u003c\/li\u003e\n\u003cli\u003eChapter - 1.2: Data-driven organization design;\u003c\/li\u003e\n\u003cli\u003eChapter - 1.3: Financial planning and analytics;\u003c\/li\u003e\n\u003c\/ul\u003e\u003c\/li\u003e\n\u003cli\u003eSection - TWO: Making it real - Transformation and optimization;\u003c\/li\u003e\n\u003cli\u003e\u003cul\u003e\n\u003cli\u003eChapter - 2.1: Introduction;\u003c\/li\u003e\n\u003cli\u003eChapter - 2.2: Preparing to execute;\u003c\/li\u003e\n\u003cli\u003eChapter - 2.3: Implementation;\u003c\/li\u003e\n\u003cli\u003eChapter - 2.4: Ongoing optimization;\u003c\/li\u003e\n\u003c\/ul\u003e\u003c\/li\u003e\n\u003cli\u003eSection - THREE: Workforce Planning;\u003c\/li\u003e\n\u003cli\u003e\u003cul\u003e\n\u003cli\u003eChapter - 3.1: Introduction;\u003c\/li\u003e\n\u003cli\u003eChapter - 3.2: Supply forecasting and top-down demand planning;\u003c\/li\u003e\n\u003cli\u003eChapter - 3.3: Bottom-up position planning, finalizing the workforce plan and monitoring progress;\u003c\/li\u003e\n\u003c\/ul\u003e\u003c\/li\u003e\n\u003cli\u003eSection - FOUR: OP\u0026amp;A analysis;\u003c\/li\u003e\n\u003cli\u003e\u003cul\u003e\n\u003cli\u003eChapter - 4.1: Introduction;\u003c\/li\u003e\n\u003cli\u003eChapter - 4.2: Laying the foundations for effective OP\u0026amp;A analysis;\u003c\/li\u003e\n\u003cli\u003eChapter - 4.3: Ignoring statistical traps and leveraging data science;\u003c\/li\u003e\n\u003c\/ul\u003e\u003c\/li\u003e\n\u003cli\u003eSection - FIVE: The OP\u0026amp;A function;\u003c\/li\u003e\n\u003cli\u003e\u003cul\u003e\n\u003cli\u003eChapter - 5.1: Introduction;\u003c\/li\u003e\n\u003cli\u003eChapter - 5.2: Stage 1: Scoping the macro operating design and writing the business case;\u003c\/li\u003e\n\u003cli\u003eChapter - 5.3: Stage 2: Detailing the OP\u0026amp;A function;\u003c\/li\u003e\n\u003cli\u003eChapter - 5.4: Stage 3: Making the OP\u0026amp;A function real;\u003c\/li\u003e\n\u003cli\u003eChapter - 18: Glossary\u003c\/li\u003e\n\u003c\/ul\u003e\u003c\/li\u003e\n\u003c\/ul\u003e","brand":"Kogan Page Ltd","offers":[{"title":"Default Title","offer_id":48866639020375,"sku":"9781398605817","price":33.24,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781398605817.jpg?v=1722279588"},{"product_id":"talent-intelligence-9781398607231","title":"Talent Intelligence","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eToby Culshaw \u003c\/b\u003eis 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.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"\u003cb\u003eToby Culshaw\u003c\/b\u003e wrote an insightful book to help you execute your talent strategy. What I like most about \u003cb\u003e\u003ci\u003eTalent Intelligence\u003c\/i\u003e\u003c\/b\u003e is how actionable it is. \u003cb\u003eToby \u003c\/b\u003eshares 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 *\u003cbr\u003e\"This is the first comprehensive discussion on Talent Intelligence I have seen. This is a topic much discussed, but little understood. \u003cb\u003eToby\u003c\/b\u003e has finally given us a clear definition and a practical way to implement this powerful process.\" * Kevin Wheeler, Founder, The Future of Talent Institute *\u003cbr\u003e\"Wow, from the maestro of TI, \u003cb\u003eToby \u003c\/b\u003ehimself. 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 *\u003cbr\u003e\"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 \u003cb\u003eToby \u003c\/b\u003ehas 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 \u0026amp; Partner Boston Consulting Group *\u003cbr\u003e\"\u003cb\u003eToby \u003c\/b\u003eis a recognized \u0026amp; 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 *\u003cbr\u003e\"\u003cb\u003eToby Culshaw\u003c\/b\u003e 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 *\u003cbr\u003e\"The most inclusive and comprehensive work on Talent Intelligence I've seen to date.  \u003cb\u003eToby\u003c\/b\u003e'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 *\u003cbr\u003e\"Timely and comprehensive examination of an often under-explored but critical area of talent strategy. \u003cb\u003eToby \u003c\/b\u003emanages 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 *\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cul\u003e\u003cli\u003e\u003cul\u003e\n\u003cli\u003eChapter - 00: Introduction;\u003c\/li\u003e\n\u003cli\u003eChapter - 01: Context;\u003c\/li\u003e\n\u003cli\u003eChapter - 02: Types of Intelligence;\u003c\/li\u003e\n\u003cli\u003eChapter - 03: The great debate;\u003c\/li\u003e\n\u003cli\u003eChapter - 04: Building the case for Talent Intelligence;\u003c\/li\u003e\n\u003cli\u003eChapter - 05: What type of work can TI functions support?;\u003c\/li\u003e\n\u003cli\u003eChapter - 06: Metrics for Success and KPIs;\u003c\/li\u003e\n\u003cli\u003eChapter - 07: Where to sit TI function within organizations;\u003c\/li\u003e\n\u003cli\u003eChapter - 08: Talent Intelligence Maturity Model;\u003c\/li\u003e\n\u003cli\u003eChapter - 09: Tooling and Resources;\u003c\/li\u003e\n\u003cli\u003eChapter - 10: Potential structures of Talent Intelligence teams;\u003c\/li\u003e\n\u003cli\u003eChapter - 11: Roles and skills needed in teams;\u003c\/li\u003e\n\u003cli\u003eChapter - 12: Career pathing;\u003c\/li\u003e\n\u003cli\u003eChapter - 13: In House and partner landscape;\u003c\/li\u003e\n\u003cli\u003eChapter - 14: Examples of use of talent intelligence;\u003c\/li\u003e\n\u003cli\u003eChapter - 15: What does good look like?;\u003c\/li\u003e\n\u003cli\u003eChapter - 16: What is the future of Talent Intelligence?;\u003c\/li\u003e\n\u003cli\u003eChapter - 17: Tales from the trenches;\u003c\/li\u003e\n\u003cli\u003eChapter - 18: Well that’s a wrap\u003c\/li\u003e\n\u003c\/ul\u003e\u003c\/li\u003e\u003c\/ul\u003e","brand":"Kogan Page Ltd","offers":[{"title":"Default Title","offer_id":48866639610199,"sku":"9781398607231","price":28.49,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781398607231.jpg?v=1722279593"},{"product_id":"datadriven-hr-9781398614567","title":"DataDriven HR","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eBernard Marr\u003c\/b\u003e 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 \u003ci\u003eForbes \u003c\/i\u003eand the Huffington Post. Bernard Marr is also the author of \u003ci\u003e\u003cb\u003eData Strategy\u003c\/b\u003e\u003c\/i\u003e (2021) and \u003ci\u003e\u003cb\u003eThe Intelligence Revolution\u003c\/b\u003e \u003c\/i\u003e(2020) published by Kogan Page.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"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. \u003cb\u003eBernard\u003c\/b\u003e'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 *\u003cbr\u003e\"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 \u003cb\u003eBernard Marr\u003c\/b\u003e. 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 *\u003cbr\u003e\"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. *\u003cbr\u003e\"\u003ci\u003e\u003cb\u003eData-Driven HR\u003c\/b\u003e\u003c\/i\u003e 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 *\u003cbr\u003e\"\u003cb\u003e\u003ci\u003eData-Driven HR\u003c\/i\u003e\u003c\/b\u003e 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 *\u003cbr\u003e\"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 \u003cb\u003e\u003ci\u003eData Driven HR\u003c\/i\u003e\u003c\/b\u003e on my bookshelf, and in this timely update \u003cb\u003eMarr\u003c\/b\u003e, 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. *\u003cbr\u003e\"\u003cb\u003eBernard Marr\u003c\/b\u003e 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, \u003cb\u003eMarr \u003c\/b\u003edemonstrates 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 *\u003cbr\u003e\"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 \u0026amp; Strategy EMEA Practice Leader *\u003cbr\u003e\"AI is transforming the world of work and our personal lives. With a people-centric approach, \u003cb\u003eBernard Marr\u003c\/b\u003e 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 *\u003cbr\u003e\"\u003ci\u003e\u003cb\u003eData-Driven HR\u003c\/b\u003e\u003c\/i\u003e 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 *\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cul\u003e\n\u003cli\u003e\u003cul\u003e\u003cli\u003eChapter - 00: Preface;\u003c\/li\u003e\u003c\/ul\u003e\u003c\/li\u003e\n\u003cli\u003eSection - ONE: Data, Analytics and AI in HR;\u003c\/li\u003e\n\u003cli\u003e\u003cul\u003e\n\u003cli\u003eChapter - 01: How data and AI are transforming HR;\u003c\/li\u003e\n\u003cli\u003eChapter - 02: How data and AI have come to revolutionise HR;\u003c\/li\u003e\n\u003cli\u003eChapter - 03: The Data, Analytics and AI tools available to HR;\u003c\/li\u003e\n\u003c\/ul\u003e\u003c\/li\u003e\n\u003cli\u003eSection - TWO: Data-Driven and AI-enabled HR in Practice;\u003c\/li\u003e\n\u003cli\u003e\u003cul\u003e\n\u003cli\u003eChapter - 04: Better HR insights and decision-making;\u003c\/li\u003e\n\u003cli\u003eChapter - 05: Recruitment and candidate selection;\u003c\/li\u003e\n\u003cli\u003eChapter - 06: Employee Onboarding;\u003c\/li\u003e\n\u003cli\u003eChapter - 07: Performance Monitoring and Management;\u003c\/li\u003e\n\u003cli\u003eChapter - 08: Employee Training and Development;\u003c\/li\u003e\n\u003cli\u003eChapter - 09: Performance monitoring and management;\u003c\/li\u003e\n\u003cli\u003eChapter - 10: Identify the use cases;\u003c\/li\u003e\n\u003cli\u003eChapter - 11: Building skills and aligning culture;\u003c\/li\u003e\n\u003c\/ul\u003e\u003c\/li\u003e\n\u003cli\u003eSection - THREE: Making data-driven and AI enabled HR happen;\u003c\/li\u003e\n\u003cli\u003e\u003cul\u003e\n\u003cli\u003eChapter - 12: Identifying the use cases for your organization;\u003c\/li\u003e\n\u003cli\u003eChapter - 13: The future of HR\u003c\/li\u003e\n\u003c\/ul\u003e\u003c\/li\u003e\n\u003c\/ul\u003e","brand":"Kogan Page Ltd","offers":[{"title":"Default Title","offer_id":48866641281367,"sku":"9781398614567","price":31.34,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781398614567.jpg?v=1722279603"},{"product_id":"machine-learning-pocket-reference-9781492047544","title":"Machine Learning Pocket Reference","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eWith detailed notes, tables, and examples, this handy reference will help you navigate the basics of structured machine learning. Author Matt Harrison delivers a valuable guide that you can use for additional support during training and as a convenient resource when you dive into your next machine learning project.","brand":"O'Reilly Media","offers":[{"title":"Default Title","offer_id":48867307684183,"sku":"9781492047544","price":20.39,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781492047544.jpg?v=1722282712"},{"product_id":"mastering-shiny-9781492047384","title":"Mastering Shiny","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eHadley Wickham from RStudio shows data scientists, data analysts, statisticians, and scientific researchers with no knowledge of HTML, CSS, or JavaScript how to create rich web apps from R.","brand":"O'Reilly Media","offers":[{"title":"Default Title","offer_id":48867307716951,"sku":"9781492047384","price":47.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781492047384.jpg?v=1722282713"},{"product_id":"the-selfservice-data-roadmap-9781492075257","title":"The SelfService Data Roadmap","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eData-driven insights are a key competitive advantage for any industry today, but deriving insights from raw data can still take days or weeks. With this practical book, data engineers, data scientists, and team managers will learn how to build a self-service data science platform that helps anyone in your organization extract insights from data.","brand":"O'Reilly Media","offers":[{"title":"Default Title","offer_id":48867309224279,"sku":"9781492075257","price":42.39,"currency_code":"GBP","in_stock":true}]},{"product_id":"handson-data-visualization-9781492086000","title":"HandsOn Data Visualization","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eTell 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.","brand":"O'Reilly Media","offers":[{"title":"Default Title","offer_id":48867309912407,"sku":"9781492086000","price":39.74,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781492086000.jpg?v=1722282725"},{"product_id":"think-bayes-9781492089469","title":"Think Bayes","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eIf you know how to program, you're ready to tackle Bayesian statistics. With this book, you'll learn how to solve statistical problems with Python code instead of mathematical formulas, using discrete probability distributions rather than continuous mathematics.","brand":"O'Reilly Media","offers":[{"title":"Default Title","offer_id":48867309945175,"sku":"9781492089469","price":33.74,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781492089469.jpg?v=1722282724"},{"product_id":"time-series-forecasting-in-python-9781617299889","title":"Time Series Forecasting in Python","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eBuild predictive models from time-based patterns in your data. Master statistical models including new deep learning approaches for time series forecasting.\u003c\/b\u003e   \u003cbr\u003e   \u003cbr\u003e   In    \u003ci\u003eT\u003cb\u003eime Series Forecasting in Python\u003c\/b\u003e\u003c\/i\u003e    you will learn how to:   \u003cbr\u003e   \u003cbr\u003e   \u003cul\u003e\n\u003cli\u003eRecognize a time series forecasting problem and build a performant predictive model\u003c\/li\u003e\n\u003cli\u003eCreate univariate forecasting models that account for seasonal effects and external variables\u003c\/li\u003e\n\u003cli\u003eBuild multivariate forecasting models to predict many time series at once\u003c\/li\u003e\n\u003cli\u003eLeverage large datasets by using deep learning for forecasting time series\u003c\/li\u003e\n\u003cli\u003eAutomate the forecasting process\u003c\/li\u003e\n\u003c\/ul\u003e   \u003cbr\u003e   \u003ci\u003e\u003cb\u003eDESCRIPTION \u003c\/b\u003e\u003c\/i\u003e       \u003ci\u003e\u003cb\u003eTime Series Forecasting in Python\u003c\/b\u003e teaches you to build powerful predictive models from time-based data. Every model you create is relevant, useful, and easy to implement with Python. You'll explore interesting real-world datasets like Google's daily stock price and economic data for the USA, quickly progressing from the basics to developing large-scale models that use deep learning tools like TensorFlow.Time Series Forecasting in Python teaches you to apply time series forecasting and get immediate, meaningful predictions. You'll learn both traditional statistical and new deep learning models for time series forecasting, all fully illustrated with Python source code.\u003c\/i\u003e       \u003ci\u003e\u003cbr\u003e\u003c\/i\u003e       \u003ci\u003e\u003cb\u003eTime Series Forecasting in Python\u003c\/b\u003e\u003c\/i\u003e teaches you to build powerful predictive models from time-based data. Every model you create is relevant, useful, and easy to implement with Python. You'll explore interesting real-world datasets like Google's daily stock price and economic data for the USA, quickly progressing from the basics to developing large-scale models that use deep learning tools like TensorFlow.      about the technology  Time series forecasting reveals hidden trends and makes predictions about the future from your data. This powerful technique has proven incredibly valuable across multiple fields—from tracking business metrics, to healthcare and the sciences. Modern Python libraries and powerful deep learning tools have opened up new methods and utilities for making practical time series forecasts.    about the book    \u003ci\u003e\u003cb\u003eTime Series Forecasting in Python\u003c\/b\u003e\u003c\/i\u003e teaches you to apply time series forecasting and get immediate, meaningful predictions. You'll learn both traditional statistical and new deep learning models for time series forecasting, all fully illustrated with Python source code. Test your skills with hands-on projects for forecasting air travel, volume of drug prescriptions, and the earnings of Johnson \u0026amp; Johnson. By the time you're done, you'll be ready to build accurate and insightful forecasting models with tools from the Python ecosystem.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003etable of contents   detailed TOC      PART 1: TIME WAITS FOR NO ONE    READ IN LIVEBOOK  1UNDERSTANDING TIME SERIES FORECASTING      READ IN LIVEBOOK  2A NAÏVE PREDICTION OF THE FUTURE      READ IN LIVEBOOK  3GOING ON A RANDOM WALK    PART 2: FORECASTING WITH STATISTICAL MODELS    READ IN LIVEBOOK  4MODELING A MOVING AVERAGE PROCESS      READ IN LIVEBOOK  5MODELING AN AUTOREGRESSIVE PROCESS      READ IN LIVEBOOK  6MODELING COMPLEX TIME SERIES      READ IN LIVEBOOK  7FORECASTING NON-STATIONARY TIME SERIES      READ IN LIVEBOOK  8ACCOUNTING FOR SEASONALITY      READ IN LIVEBOOK  9ADDING EXTERNAL VARIABLES TO OUR MODEL      READ IN LIVEBOOK  10FORECASTING MULTIPLE TIME SERIES      READ IN LIVEBOOK  11CAPSTONE: FORECASTING THE NUMBER OF ANTIDIABETIC DRUG PRESCRIPTIONS IN AUSTRALIA    PART 3: LARGE-SCALE FORECASTING WITH DEEP LEARNING    READ IN LIVEBOOK  12INTRODUCING DEEP LEARNING FOR TIME SERIES FORECASTING      READ IN LIVEBOOK  13DATA WINDOWING AND CREATING BASELINES FOR DEEP LEARNING      READ IN LIVEBOOK  14BABY STEPS WITH DEEP LEARNING      READ IN LIVEBOOK  15REMEMBERING THE PAST WITH LSTM      READ IN LIVEBOOK  16FILTERING OUR TIME SERIES WITH CNN      READ IN LIVEBOOK  17USING PREDICTIONS TO MAKE MORE PREDICTIONS      READ IN LIVEBOOK  18CAPSTONE: FORECASTING THE ELECTRIC POWER CONSUMPTION OF A HOUSEHOLD    PART 4: AUTOMATING FORECASTING AT SCALE    READ IN LIVEBOOK  19AUTOMATING TIME SERIES FORECASTING WITH PROPHET      READ IN LIVEBOOK  20CAPSTONE: FORECASTING THE MONTHLY AVERAGE RETAIL PRICE OF STEAK IN CANADA      21 GOING ABOVE AND BEYOND    APPENDIX    APPENDIX A: INSTALLATION INSTRUCTIONS","brand":"Manning Publications","offers":[{"title":"Default Title","offer_id":48867785310551,"sku":"9781617299889","price":43.69,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781617299889.jpg?v=1722284952"},{"product_id":"concurrent-data-processing-in-elixir-fast-resilient-applications-with-otp-genstage-flow-and-broadway-9781680508192","title":"Concurrent Data Processing in Elixir: Fast,","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eLearn different ways of writing concurrent code in Elixir and increase your application's performance, without sacrificing scalability or fault-tolerance. Most projects benefit from running background tasks and processing data concurrently, but the world of OTP and various libraries can be challenging. Which Supervisor and what strategy to use? What about GenServer? Maybe you need back-pressure, but is GenStage, Flow, or Broadway a better choice? You will learn everything you need to know to answer these questions, start building highly concurrent applications in no time, and write code that's not only fast, but also resilient to errors and easy to scale.  Whether you are building a high-frequency stock trading application or a consumer web app, you need to know how to leverage concurrency to build applications that are fast and efficient. Elixir and the OTP offer a range of powerful tools, and this guide will show you how to choose the best tool for each job, and use it effectively to quickly start building highly concurrent applications.  Learn about Tasks, supervision trees, and the different types of Supervisors available to you. Understand why processes and process linking are the building blocks of concurrency in Elixir. Get comfortable with the OTP and use the GenServer behaviour to maintain process state for long-running jobs. Easily scale the number of running processes using the Registry. Handle large volumes of data and traffic spikes with GenStage, using back-pressure to your advantage. Create your first multi-stage data processing pipeline using producer, consumer, and producer-consumer stages. Process large collections with Flow, using MapReduce and more in parallel. Thanks to Broadway, you will see how easy it is to integrate with popular message broker systems, or even existing GenStage producers.  Start building the high-performance and fault-tolerant applications Elixir is famous for today.  What You Need:  You'll need Elixir 1.9+ and Erlang\/OTP 22+ installed on a Mac OS X, Linux, or Windows machine.","brand":"The Pragmatic Programmers","offers":[{"title":"Default Title","offer_id":48868033986903,"sku":"9781680508192","price":30.35,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781680508192.jpg?v=1722286109"},{"product_id":"halo-data-understanding-and-leveraging-the-value-of-your-data-9781783306176","title":"Halo Data: Understanding and Leveraging the Value","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThe past two decades have seen an explosion both in the volume of data we use, and our understanding of its management.\u003c\/p\u003e\u003cp\u003eHowever, while techniques and technology for manipulating data have advanced rapidly in this time, the concepts around the value of our data have not. This lack of progress has made it increasingly difficult for organisations to understand the value \u003cem\u003ein\u003c\/em\u003e their data, the value \u003cem\u003eof\u003c\/em\u003e their data and how exploit that value. \u003c\/p\u003e\u003cp\u003e\u003cem\u003eHalo Data\u003c\/em\u003e proposes a paradigm shift in methodology for organisations to properly appreciate and leverage the value of their data. Written by an author team with many years’ experience in data strategy, management and technology, the book will first review the current state of our understanding of data. This opening will demonstrate the limitations of this status quo, including a discussion on metadata and its limitations, data monetisation and data-driven business models. Following this, the book will present a new concept and framework for understanding and quantifying value in an organisation’s data and a practical methodology for using this in practice.\u003c\/p\u003e\u003cp\u003eIdeal for data leaders and executives who are looking to leverage the data at their fingertips.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eIntroduction\u003c\/p\u003e\u003cp\u003e1 Who owns the definitions and terms about data?\u003c\/p\u003e\u003cp\u003e2 What is metadata?\u003c\/p\u003e\u003cp\u003e3 Other ideas of data value and monetization\u003c\/p\u003e\u003cp\u003e4 Value from a different source\u003c\/p\u003e\u003cp\u003e5 Hello Halo Data\u003c\/p\u003e\u003cp\u003e6 Getting to know Halo Data\u003c\/p\u003e\u003cp\u003e7 Early examples of Halo data approaches\u003c\/p\u003e\u003cp\u003e8 Halo data and data ethics\u003c\/p\u003e\u003cp\u003e9 Halo data framework\u003c\/p\u003e\u003cp\u003e10 Halo Data applied risk assessment, regulation, customer, the citizen\u003c\/p\u003e\u003cp\u003e11 Halo Data and storytelling\u003c\/p\u003e","brand":"Facet Publishing","offers":[{"title":"Default Title","offer_id":48868278010199,"sku":"9781783306176","price":29.33,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781783306176.jpg?v=1722287257"},{"product_id":"r-for-data-analysis-in-easy-steps-9781840789980","title":"R for Data Analysis in easy steps","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThe 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.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eR for Data Analysis in easy steps, 2nd edition\u003c\/strong\u003e 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.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eR for Data Analysis in easy steps, 2nd edition\u003c\/strong\u003e 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.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003ci\u003eUpdated for the latest version of R.\u003c\/i\u003e\u003c\/p\u003e","brand":"In Easy Steps Limited","offers":[{"title":"Default Title","offer_id":48868654285143,"sku":"9781840789980","price":12.34,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781840789980.jpg?v=1722289078"},{"product_id":"quick-start-guide-to-azure-data-factory-azure-data-lake-server-and-azure-data-warehouse-9781547417353","title":"Quick Start Guide to Azure Data Factory, Azure","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eWith 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.","brand":"De Gruyter","offers":[{"title":"Default Title","offer_id":48886243656023,"sku":"9781547417353","price":16.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781547417353.jpg?v=1722539337"},{"product_id":"big-data-analytics-methods-analytics-techniques-in-data-mining-deep-learning-and-natural-language-processing-9781547417957","title":"Big Data Analytics Methods: Analytics Techniques","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eBig 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.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eIntroduction     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 \u0026amp; Predictive Modeling     Chapter 7. Ensemble of Models     Chapter 8. Machine Learning, Deep Learning – Artificial Neural Networks     Chapter 9. Model Accuracy \u0026amp; Optimization     PART III: Case Study – Prediction \u0026amp; 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","brand":"De Gruyter","offers":[{"title":"Default Title","offer_id":48886243754327,"sku":"9781547417957","price":48.38,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781547417957.jpg?v=1722539338"},{"product_id":"cognitive-sciences-research-progress-9781604563924","title":"Cognitive Sciences Research Progress","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis book presents new research on cognitive science which is most simply defined as the scientific study either of mind or of intelligence. It is an interdisciplinary study drawing from relevant fields including psychology, philosophy, neuroscience, linguistics, anthropology, computer science, biology, and physics. There are several approaches to the study of cognitive science. These approaches may be classified broadly as symbolic, connectionist, and dynamic systems. Symbolic holds that cognition can be explained using operations on symbols, by means of explicit computational theories and models of mental (but not brain) processes analogous to the workings of a digital computer. Connectionist (subsymbolic) holds that cognition can only be modelled and explained by using artificial neural networks on the level of physical brain properties. Hybrid systems hold that cognition is best modelled using both connectionist and symbolic models, and possibly other computational techniques. Dynamic Systems hold that cognition can be explained by means of a continuous dynamical system in which all the elements are interrelated, like the Watt Governor. The essential questions of cognitive science seem to be: What is intelligence? and How is it possible to model it computationally?","brand":"Nova Science Publishers Inc","offers":[{"title":"Default Title","offer_id":48886636314967,"sku":"9781604563924","price":999.99,"currency_code":"GBP","in_stock":false}]},{"product_id":"demand-forecasting-best-practices-9781633438095","title":"Demand Forecasting Best Practices","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eMaster the demand forecasting skills you need to decide what resources to acquire, products to produce, and where and how to distribute them. \u003cp\u003eFor demand planners, S\u0026amp;OP managers, supply chain leaders, and data scientists. \u003cstrong\u003eDemand Forecasting Best Practices\u003c\/strong\u003e is a unique step-by-step guide, demonstrating forecasting tools, metrics, and models alongside stakeholder management techniques that work in a live business environment.\u003c\/p\u003e \u003cp\u003eYou will learn how to:\u003c\/p\u003e \u003cul\u003e\n\u003cli\u003eLead a demand planning team to improve forecasting quality while reducing workload\u003c\/li\u003e\n\u003cli\u003eProperly define the objectives, granularity, and horizon of your demand planning process\u003c\/li\u003e\n\u003cli\u003eUse smart, value-weighted KPIs to track accuracy and bias\u003c\/li\u003e\n\u003cli\u003eSpot areas of your process where there is room for improvement\u003c\/li\u003e\n\u003cli\u003eHelp planners and stakeholders (sales, marketing, finances) add value to your process\u003c\/li\u003e\n\u003cli\u003eIdentify what kind of data you should be collecting, and how\u003c\/li\u003e\n\u003cli\u003eUtilise different types of statistical and machine learning models\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003eFollow author Nicolas Vandeput's original five-step framework for demand planning excellence and learn how to tailor it to your own company's needs. You will learn how to optimise demand planning for a more effective supply chain and will soon be delivering accurate predictions that drive major business value.\u003c\/p\u003e About the technology \u003cp\u003eDemand forecasting is vital for the success of any product supply chain. It allows companies to make better decisions about what resources to acquire, what products to produce, and where and how to distribute them. As an effective demand forecaster, you can help your organisation avoid overproduction, reduce waste, and optimise inventory levels for a real competitive advantage.\u003c\/p\u003e","brand":"Manning Publications","offers":[{"title":"Default Title","offer_id":48887150182743,"sku":"9781633438095","price":41.72,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781633438095.jpg?v=1722543242"},{"product_id":"data-fabric-architectures-web-driven-applications-9783111000824","title":"Data Fabric Architectures: Web-Driven","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThe immense increase on the size and type of real time data generated across various edge computing platform results in unstructured databases and data silos. This edited book gathers together an international set of researchers to investigate the possibilities offered by data-fabric solutions; the volume focuses in particular on data architectures and on semantic changes in future data landscapes. \u003c\/p\u003e","brand":"De Gruyter","offers":[{"title":"Default Title","offer_id":48889051513175,"sku":"9783111000824","price":105.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783111000824.jpg?v=1722552453"},{"product_id":"interrogating-datafication-towards-a-praxeology-of-data-9783837655612","title":"Interrogating Datafication – Towards a Praxeology","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eWhat constitutes a data practice and how do contemporary digital media technologies reconfigure our understanding of practices in general? Autonomously acting media, distributed digital infrastructures, and sensor-based media environments challenge the conditions of accounting for data practices both theoretically and empirically. Which forms of cooperation are constituted in and by data practices? And how are human and nonhuman agencies distributed and interrelated in data-saturated environments? The volume collects theoretical, empirical, and historiographical contributions from a range of international scholars to shed light on the current shift from media to data practices.","brand":"Transcript Verlag","offers":[{"title":"Default Title","offer_id":48889279676759,"sku":"9783837655612","price":35.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783837655612.jpg?v=1722553596"},{"product_id":"linear-regression-analysis-2e-330-wiley-series-in-probability-and-statistics-9780471415404","title":"Linear Regression Analysis 2e 330 Wiley Series in","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eRequiring no specialized knowledge beyond a good grasp of matrix algebra and some acquaintance with straight line regression and simple analysis of variance models, this work covers the diagnostics and methods of model fitting.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"With excellent motivating and presenting style, this book is suitable for a beginning graduate level regression course.\" (\u003ci\u003eJournal of Statistical Computation and Simulation\u003c\/i\u003e, July 2005)  \u003cp\u003e\"...revises and expands the standard text, providing extensive coverage of state-of-the-art theory...\" (\u003ci\u003eZentralblatt Math\u003c\/i\u003e, Vol. 1029, 2004)\u003c\/p\u003e \u003cp\u003e\"...largely rewritten...very useful for self-study...an excellent choice for a course in linear models and researchers who are interested in recent literature in the fields...\" (\u003ci\u003eTechnometrics\u003c\/i\u003e, Vol. 45, No. 4, November 2003)\u003c\/p\u003e \u003cp\u003e“...rewritten to reflect current thinking, such as the major advances in computing during the past 25 years.” (\u003ci\u003eQuarterly of Applied Mathematics\u003c\/i\u003e, Vol. LXI, No. 3, September 2003)\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePreface.  \u003cp\u003eVectors of Random Variables.\u003c\/p\u003e \u003cp\u003eMultivariate Normal Distribution.\u003c\/p\u003e \u003cp\u003eLinear Regression: Estimation and Distribution Theory.\u003c\/p\u003e \u003cp\u003eHypothesis Testing.\u003c\/p\u003e \u003cp\u003eConfidence Intervals and Regions.\u003c\/p\u003e \u003cp\u003eStraight-Line Regression.\u003c\/p\u003e \u003cp\u003ePolynomial Regression.\u003c\/p\u003e \u003cp\u003eAnalysis of Variance.\u003c\/p\u003e \u003cp\u003eDepartures from Underlying Assumptions.\u003c\/p\u003e \u003cp\u003eDepartures from Assumptions: Diagnosis and Remedies.\u003c\/p\u003e \u003cp\u003eComputational Algorithms for Fitting a Regression.\u003c\/p\u003e \u003cp\u003ePrediction and Model Selection.\u003c\/p\u003e \u003cp\u003eAppendix A. Some Matrix Algebra.\u003c\/p\u003e \u003cp\u003eAppendix B. Orthogonal Projections.\u003c\/p\u003e \u003cp\u003eAppendix C. Tables.\u003c\/p\u003e \u003cp\u003eOutline Solutions to Selected Exercises.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003eIndex.\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49083507048791,"sku":"9780471415404","price":141.26,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780471415404.jpg?v=1725549161"}],"url":"https:\/\/bookcurl.com\/collections\/data-capture-and-analysis.oembed?page=7","provider":"Book Curl","version":"1.0","type":"link"}