Data capture and analysis Books

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

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Book SynopsisThis approachable text teaches all the concepts, the context, and the code needed to understand deep learning. Suitable for students and professionals, the book doesn't require any previous background in machine learning or deep learning. Interactive examples feature throughout, with runnable code and executable Jupyter notebooks available online.Trade Review'In less than a decade, the AI revolution has swept from research labs to broad industries to every corner of our daily life. Dive into Deep Learning is an excellent text on deep learning and deserves attention from anyone who wants to learn why deep learning has ignited the AI revolution: the most powerful technology force of our time.' Jensen Huang, Founder and CEO, NVIDIA'This is a timely, fascinating book, providing not only a comprehensive overview of deep learning principles but also detailed algorithms with hands-on programming code, and moreover, a state-of-the-art introduction to deep learning in computer vision and natural language processing. Dive into this book if you want to dive into deep learning!' Jiawei Han, Michael Aiken Chair Professor, University of Illinois at Urbana-Champaign'This is a highly welcome addition to the machine learning literature, with a focus on hands-on experience implemented via the integration of Jupyter notebooks. Students of deep learning should find this invaluable to become proficient in this field.' Bernhard Schölkopf,, Director, Max Planck Institute for Intelligent Systems'Dive into Deep Learning strikes an excellent balance between hands-on learning and in-depth explanation. I've used it in my deep learning course and recommend it to anyone who wants to develop a thorough and practical understanding of deep learning.' Colin Raffel, Assistant Professor, University of North Carolina, Chapel HillTable of ContentsInstallation; Notation; 1. Introduction; 2. Preliminaries; 3. Linear neural networks for regression; 4. Linear neural networks for classification; 5. Multilayer perceptrons; 6. Builders guide; 7. Convolutional neural networks; 8. Modern convolutional neural networks; 9. Recurrent neural networks; 10. Modern recurrent neural networks; 11. Attention mechanisms and transformers; Appendix. Tools for deep learning; Bibliography; Index.

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Book SynopsisWhen 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. Data Analysis with Python and PySpark is yourguide to delivering successful Python-driven data projects. Data 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. The 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. Trade Review“A great and gentle introduction to spark.” Javier Collado Cabeza “A phenomenal introduction to PySpark from the ground up.”Anonymous Reviewer “A great book to get you started with PySpark!” Jeremy Loscheider “Takes you on an example focused tour of building pyspark data structures from the data you provide and processing them at speed.” Alex Lucas “If you need to learn PySpark (as a Data Scientist or Data Wrangler) start with this book!”Geoff Clark

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Book SynopsisWith 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.

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Book SynopsisTrade Review"John Ladley’s latest book is the second edition of his Data Governance book, first published in 2012. If you know the first edition of this book, you’ll certainly appreciate this updated second edition. Reading this book will provide you with a comprehensive overview of why data governance is needed, how to design, initiate, and execute a data governance program, and how to keep the program sustainable. There is a ton of new content in this second edition, including new case studies, updated industry details, and updated coverage of the available data governance tools that can help.The book will be useful to you whether you are a novice or a seasoned professional. At the heart of this book is the framework that Ladley communicates that you can follow to build and maintain successful data governance at your organization. In combination with the use cases that he walks through in the book, you have a powerful guide for launching your data governance program. Useful for both small and large organizations, be sure to pick up a copy of this book if you are charged with any aspect of data management and data governance within your shop." --Data and Technology TodayTable of Contents1. Prologue – An Executive Overview 2. Introduction 3. Data Literacy and Concepts 4 Overview – A Day in the Life of a data governance program and its capabilities 5. The data governance business case 6. Process overview for deploying data governance 7. Scope and initiation 8. Assess 9. Vision 10. Align and business value 11. Functional design 12. Governing framework design 13. Road map 14. Rollout and sustain 15. Data governance artifacts and tools 16. Final remarks Appendix Data Governance Charters Template Data Governance Orientation and ongoing knowledge transfer template Stakeholder Analysis Leadership Alignment Assessment Communications Plan Training Plan Example Post-rollout Checklist

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Book SynopsisMaster the demand forecasting skills you need to decide what resources to acquire, products to produce, and where and how to distribute them. For demand planners, S&OP managers, supply chain leaders, and data scientists. Demand Forecasting Best Practices is a unique step-by-step guide, demonstrating forecasting tools, metrics, and models alongside stakeholder management techniques that work in a live business environment. You will learn how to: Lead a demand planning team to improve forecasting quality while reducing workload Properly define the objectives, granularity, and horizon of your demand planning process Use smart, value-weighted KPIs to track accuracy and bias Spot areas of your process where there is room for improvement Help planners and stakeholders (sales, marketing, finances) add value to your process Identify what kind of data you should be collecting, and how Utilise different types of statistical and machine learning models Follow 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. About the technology Demand 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.

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

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Book SynopsisIf 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.

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Book SynopsisTrade ReviewPraise for the first edition "Biologists . . . have been relatively slow to take advantage of enhanced computing power and unlock the potential of these techniques. This book removes any excuse."—Frontiers of Biogeography"This volume would be an excellent text for an introductory course in modeling as science, or for self-study by a mature researcher interested in learning about this important new way of doing science."—H. Van Dyke Parunak, JASSS"This book represents something I have been [awaiting] for some years now: a good and solid introduction to the field of individual- and agent-based models. . . . The book is not only a practical guide but also serves as a good introduction to the basics of 'healthy' programming. These authors are the right ones to do this as they have a strong background in the philosophical aspects as well as the practical issues of modelling."—Basic and Applied Ecology"Agent-Based and Individual-Based Modeling has the potential to foster an appreciation of the value and place of individual-based models in our field in the next generation of emerging ecologists."—Christopher X. Jon Jensen, Ecology

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Book SynopsisSnowflake'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.

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

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Book SynopsisAn anthropological account of how rankings, statistics and numbers are reshaping the world we live inTrade Review'A new and compelling argument for why so many institutions continue to be spellbound by rankings and metrics - despite the cultural carnage they cause in schools, hospitals, universities, corporations and governmental agencies. How can we halt this 'death by audit' craze that has swept through modern society like a deadly virus? In this thought provoking book, the authors develop a radical agenda that will strike fear into number-loving technocrats around the world.' -- Peter Fleming, author of 'Dark Academia: How Universities Die''If you want to go and see a film, choose a university or find the best restaurant, you are likely to consult some sort of ranking ... In this timely work, Shore and Wright ask us to question this contemporary common sense and the market managerialism that lies behind it. Can we imagine a world without audit, one in which our choices are not counted, and trust does not rely on numbers?' -- Professor Martin Parker, University of Bristol Business School'The expansion of audits, indicators and rankings has become a pressing issue for governance and democracy. Cris Shore and Susan Wright build on decades of work to provide a powerful and definitive critical diagnosis of the effects of this audit culture on individuals, public organisations and society. Their book should be essential reading for scholars and policy makers.' -- Michael Power, Professor of Accounting, London School of Economics and Political Science'A visionary book. Two anthropologists piece together a global jigsaw: how for 25 years practices of accountability have been transforming almost every aspect of organisational and personal life. A brilliantly lucid, vigorously argued critique, clear-eyed about the structures that undermine us.' -- Marilyn Strathern, Emeritus Professor of Social Anthropology, University of CambridgeTable of ContentsList of Figures Abbreviations Acknowledgements Series Preface Preface 1. Introduction: Audit Culture and the New World (Dis)Order 2. Rankings as Populist Project: Governing by Numbers and Hollowing out Democracy 3. The Big Four Accountancy Firms and the Evolution of Contemporary Capitalism 4. Global Governance through Standards, Seduction and Soft Power 5. Metrics, Managerialism and Market Making: Unlocking Value in Healthcare 6. Reforming Higher Education: The Kafkaesque Pursuit of ‘World Class’ Status 7. The New Subjects of Audit: Performance Management and Quantified Selves 8. Conclusion: Repurposing Audit – Restoring Trust, Accountability and Democracy Bibliography Index

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

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Book SynopsisBernard Marr is one of the leading voices in Technology and Innovation. A futurist and strategic performance consultant, he has advised many of the world's best-known organizations on their business and data strategies. A frequent keynote speaker, he also writes on the topic of data and analytics for various publications including Forbes and the Huffington Post. Bernard Marr is also the author of Data Strategy (2021) and The Intelligence Revolution (2020) published by Kogan Page.Trade Review"Without a doubt human capability (talent + leadership + organization + HR) increasingly delivers value to all stakeholders. This excellent book provides business and HR leaders the information required to improve decision making. Bernard's insights on analytics and AI will be the keys for progress." * Dave Ulrich, Rensis Likert Professor, Ross School of Business, University of Michigan Partner, The RBL Group *"If anyone was going to publish a book about the impact of the latest technology developments such as AI on the field of HR and People Analytics my bets were on Bernard Marr. And you won't be disappointed. The book offers a deep dive into the world of data of every kind, every possible use case, honest overview of technology and important considerations. It has never been more critical to educate ourselves about it." * Maja Luckos, VP, Employee Success, Salesforce *"This book propelled me into a world of possibilities for HR leaders in embracing the 'intelligence revolution' to shape people strategies that add value to their organizations and their people. It's enlightened me to the power of AI-enabled HR and how I might use it, and it's made me want to learn more. This is a must read for all HR leaders." * Linda Sleath, Group HR Director, Topps Tiles Plc. *"Data-Driven HR strikes a nice balance between exploring emerging trends in people analytics while primarily serving as a practical guide to HR professionals at any stage of their data journey. The second edition seamlessly weaves AI into a narrative that's easy to engage with and is packed full of examples that bring the theories to life." * Mark Ferrie, People Analytics Director, Meta *"Data-Driven HR is a terrific overview of the enormous world of people analytics and AI. For people trying to understand this important space, this book shows you the way." * Josh Bersin, Global Industry Analyst and CEO of The Josh Bersin Company *"Data, analytics and AI provides to elevate HR from its traditional role as a support function to one of a strategic partner creating value for the enterprise, its customers and its employees. There's a well-thumbed copy of the first edition of Data Driven HR on my bookshelf, and in this timely update Marr, one of the most knowledgeable people on the topic, explains how data and AI can enable HR to drive better decision making about people, deliver an enhanced service to employees; and make HR processes more efficient." * David Green, Managing Partner at Insight222, co-author of Excellence in People Analytics, and host of the Digital HR Leaders podcast. *"Bernard Marr has once again delivered an indispensable guide to harnessing the power of data, analytics and AI in HR. This updated edition thoroughly captures the latest innovations shaping human resources while still being accessible for HR professionals at any level. Through compelling examples and clear frameworks, Marr demonstrates how to drive business value through evidence-based talent practices. This is a must-read playbook for any HR leader looking to build capabilities in data-driven decision-making." * Professor Max Blumberg, PhD, University of Leeds *"This is a great guide for HR professionals who are grappling with the transition to becoming data led. It's easy to read, and with real examples and case studies across the employee lifecycle, it's also a pragmatic resource to have in your HR toolkit." * Ashish Sinha Korn Ferry Head of People Analytics, AI & Strategy EMEA Practice Leader *"AI is transforming the world of work and our personal lives. With a people-centric approach, Bernard Marr demystifies data driven AI enabled HR with context, thought provoking insights and examples of AI at the time this book was written. We all have a role to play when it comes to this rapidly evolving space as the output of AI will be a reflection of our culture and values. Staying on top of leading practices, lessons learned, emerging regulations and standards is critical so we can unlock AI's potential and value add to the business, our customers and employees while minimizing risk. This book sets the foundation so we can do just that!" * Terilyn Juarez Monroe, Terilyn Juarez Monroe, Chief People Officer *"Data-Driven HR is an indispensable resource for Career Services professionals looking to equip their students with cutting-edge strategies in today's competitive job market. This comprehensive book offers invaluable insights into recruitment and candidate selection, employer branding, pinpointing the most effective recruitment channels, and harnessing AI-enhanced automation to identify and assess the best candidates for businesses. It's a game-changer for career advisors committed to empowering their students with the knowledge and skills needed to excel in the evolving world of talent acquisition and HR." * Dr. Amber Wigmore Álvarez, Associate Professor, IE Business School and IE University *Table of Contents Chapter - 00: Preface; Section - ONE: Data, Analytics and AI in HR; Chapter - 01: How data and AI are transforming HR; Chapter - 02: How data and AI have come to revolutionise HR; Chapter - 03: The Data, Analytics and AI tools available to HR; Section - TWO: Data-Driven and AI-enabled HR in Practice; Chapter - 04: Better HR insights and decision-making; Chapter - 05: Recruitment and candidate selection; Chapter - 06: Employee Onboarding; Chapter - 07: Performance Monitoring and Management; Chapter - 08: Employee Training and Development; Chapter - 09: Performance monitoring and management; Chapter - 10: Identify the use cases; Chapter - 11: Building skills and aligning culture; Section - THREE: Making data-driven and AI enabled HR happen; Chapter - 12: Identifying the use cases for your organization; Chapter - 13: The future of HR

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

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

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Book SynopsisThe past two decades have seen an explosion both in the volume of data we use, and our understanding of its management.However, 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 in their data, the value of their data and how exploit that value. Halo Data 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.Ideal for data leaders and executives who are looking to leverage the data at their fingertips.Table of ContentsIntroduction1 Who owns the definitions and terms about data?2 What is metadata?3 Other ideas of data value and monetization4 Value from a different source5 Hello Halo Data6 Getting to know Halo Data7 Early examples of Halo data approaches8 Halo data and data ethics9 Halo data framework10 Halo Data applied risk assessment, regulation, customer, the citizen11 Halo Data and storytelling

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Book SynopsisLearn 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.

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

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

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Book SynopsisTable of ContentsPart I: Introduction 1. The Database Environment 2. Systems Analysis and Database Requirements Part II: Relational database design theory 3. Why Good Design Matters 4. Entities and Relationships 5. The Relational Data Model 6. Relational Algebra 7. Normalization 8. Database Design and Performance Tuning 9. Codd’s Rules for Relational DBMSs Part III: Relational database design practice 10. Introduction to SQL 11. Using SQL to Implement a Relational Design 12. Using CASE Tools for Database Design 13. Database Design Case Study #1: Mighty-Mite Motors 14. Database Design Case Study #2: East Coast Aquarium 15. Database Design Case Study #3: SmartMart Part IV: Using interactive SQL to manipulate a relational database 16. Simple SQL Retrieval 17. Retrieving Data from More Than One Table 18. Advanced Retrieval Operations 19. Working With Groups of Rows 20. Data Modification 21. Creating Additional Structural Elements Part V: Database implementation issues 22. Concurrency Control 23. Database Security 24. Data Warehousing 25. Data Quality Part VI: Beyond the relational data model 26. XML Support 27. Object-Relational Databases 28. Relational Databases and “Big Data”: The Alternative of a NoSQL Solution Part VII: Appendices A. Historical Antecedents B. SQL Programming C. SQL Syntax Summary

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

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Book SynopsisAn 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.Trade ReviewBig 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 *This is a very useful, concise introduction to the topic of big data. * Jonathan Cowie, Science Fact & Science Fiction Concatenation *A 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&T Magazine *Table of ContentsBYTE SIZE CHART; REFERENCES; FURTHER READING; INDEX

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Book SynopsisThis is a rigorous and complete textbook for a first course on information retrieval from the computer science perspective. It provides an up-to-date student oriented treatment of information retrieval including extensive coverage of new topics such as web retrieval, web crawling, open source search engines and user interfaces.Table of Contents Contents Preface Acknowledgements 1 Introduction 2 User Interfaces for Search by Marti Hearst 3 Modeling 4 Retrieval Evaluation 5 Relevance Feedback and Query Expansion 6 Documents: Languages & Properties with Gonzalo Navarro and Nivio Ziviani 7 Queries: Languages & Properties with Gonzalo Navarro 8 Text Classification with Marcos Gon¸calves 9 Indexing and Searching with Gonzalo Navarro 10 Parallel and Distributed IR with Eric Brown 11 Web Retrieval with Yoelle Maarek 12 Web Crawling with Carlos Castillo 13 Structured Text Retrieval with Mounia Lalmas 14 Multimedia Information Retrieval by Dulce Poncele´on and Malcolm Slaney 15 Enterprise Search by David Hawking 16 Library Systems by Edie Rasmussen 17 Digital Libraries by Marcos Gon¸calves A Open Source Search Engines with Christian Middleton B Biographies Bibliography Index

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Book SynopsisInterest in applying analytics, machine learning, and artificial intelligence to sales and marketing has grown dramatically, with no signs of slowing down. This book provides essential guidance to apply advanced analytics and data mining techniques to real-world business applications. The foundation of this text is the author's 20-plus years of developing and delivering big data and artificial intelligence solutions across multiple industries: financial services, pharmaceuticals, consumer packaged goods, media, and retail. He provides guidelines and summarized cases for those studying or working in the fields of data science, data engineering, and business analytics. The book also offers a distinctive style: a series of essays, each of which summarizes a critical lesson or provides a step-by-step business process, with specific examples of successes and failures.Sales and marketing executives, project managers, business and engineering professionals, and graduate studeTrade Review"This book should be considered required reading for analysts, managers and executives seeking to drive business performance and gain competitive advantage through data-driven strategy and decision making. Dr. Haimowitz has distilled his considerable experience into a practical how-to guide that is sure to become a fundamental reference text for the modern analytics practitioner or manager, regardless of industry." Zaheer Benjamin, Group Leader in Business Intelligence & Analytics across Life Sciences, Broadcasting, and Major League SportsThis Data Analytics for Business written by Dr. Ira Haimowitz is an experience laden, practical book with real world examples for data analyst, marketing and business executives. The book is well written and provides a good background in design of the big data organization, data analytical applications, and implementation and delivery of the projects in easy-to- read format. The book is a must read for anyone who is interested in data analytics related to sales, marketing and strategy. Thani Jambulingam Ph.D, Professor, Department of Pharmaceutical and Healthcare Marketing, Haub School of Business, Saint Joseph’s University, Philadelphia Table of ContentsOrganizational Design Principles 1. Linking Business Challenges to Big Data Solutions 2. Selling the Big Data Analytics Initiative 3. Organizational Structures for Advanced Analytics 4. Lessons Learned Managing Big Data Departments Analytics Business Applications 5. Segmentation: Categorizing Your Customers 6. Targeting: Getting it "Right" 7. Campaign Measurement with Learning Objectives 8. Strategic Text Mining 9. Predictive Modeling for Business Implementation and Delivery 10. Privacy Considerations for Big Data Analytics 11. Delivering Results with Actionable Insights 12. Scalability and Long Term Success

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Book SynopsisThe primary goal of this text is a practical one. Equipping students with enough knowledge and creating an independent research platform, the author strives to prepare students for professional careers. Providing students with a marketable skill set requires topics from many areas of optimization. The initial goal of this text is to develop a marketable skill set for mathematics majors as well as for students of engineering, computer science, economics, statistics, and business. Optimization reaches into many different fields.This text provides a balance where one is needed. Mathematics optimization books are often too heavy on theory without enough applications; texts aimed at business students are often strong on applications, but weak on math. The book represents an attempt at overcoming this imbalance for all students taking such a course.The book contains many practical applications but also explains the mathematics behind the techniques, including stating definitTable of Contents1. 1. Preamble. 2. The Language of Optimization. 3. Computational Complexity. 4. Algebra Review. 5. Matrix Factorization. 6. Linear Programming. 7. Sensitivity Analysis. 8. Integer Linear Programing. 9. Calculus Review. 10. A Calculus Approach to Nonlinear Programming. 11. Constrained Nonlinear Programming: Lagrange Multipliers and the KKT Conditions. 12. Optimization involving Quadratic Forms. 13. Iterative Methods. 14. Derivative-Free Methods. 15. Search Algorithms. 16. Important Sets for Optimization. 17. The Fundamental Theorem of Linear Programming. 18. Convex Functions. 19. Convex Optimization. 20. An Introduction to Combinatorics. 21. An Introduction to Graph Theory. 22. Network Flows. 23. Minimum-Weight Spanning Trees and Shortest Paths. 24. Network Modeling and the Transshipment Problem. 25. The Traveling Salesperson Problem. Probability. 27. Regression Analysis via Least Squares. 28. Forecasting. 29. Introduction to Machine Learning.

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Book SynopsisThis second edition of Digital Analytics for Marketing provides students with a comprehensive overview of the tools needed to measure digital activity and implement best practices when using data to inform marketing strategy. It is the first text of its kind to introduce students to analytics platforms from a practical marketing perspective.Demonstrating how to integrate large amounts of data from web, digital, social, and search platforms, this helpful guide offers actionable insights into data analysis, explaining how to connect the dots and humanize information to make effective marketing decisions. The authors cover timely topics, such as social media, web analytics, marketing analytics challenges, and dashboards, helping students to make sense of business measurement challenges, extract insights, and take effective actions. The bookâs experiential approach, combined with chapter objectives, summaries, and review questions, will engage readers, deepening their learning by helping them to think outside the box.Filled with engaging, interactive exercises and interesting insights from industry experts, this book will appeal to undergraduate and postgraduate students of digital marketing, online marketing, and analytics.Online support materials for this book include an instructorâs manual, test bank, and PowerPoint slides.Table of Contents1. Introduction to Digital Marketing and Analytics 2. Digital Marketing KPIs, Strategy, Ecosystems, Governance, and More 3. The Evolution of Digital Analytics and the Internet 4. The Growth and Relevance of Social Media in Analytics and Digital Marketing 5. Data for Digital Marketing Analytics 6. Social Media Analytics for Digital Marketing 7. Actions, Hyperlink, and Mobile Analytics in Digital Marketing 8. Advanced AI and Algorithms 9. Basic Web Analytics and Web Intelligence 10. Advanced Web Analytics and Web Intelligence 11. Aligning Digital Marketing with Business Strategy 12. Deriving Strategic Insights and ‘Digital Value’ from Digital Marketing Analytics

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Book SynopsisThis second edition of Digital Analytics for Marketing provides students with a comprehensive overview of the tools needed to measure digital activity and implement best practices when using data to inform marketing strategy. It is the first text of its kind to introduce students to analytics platforms from a practical marketing perspective.Demonstrating how to integrate large amounts of data from web, digital, social, and search platforms, this helpful guide offers actionable insights into data analysis, explaining how to connect the dots and humanize information to make effective marketing decisions. The authors cover timely topics, such as social media, web analytics, marketing analytics challenges, and dashboards, helping students to make sense of business measurement challenges, extract insights, and take effective actions. The book's experiential approach, combined with chapter objectives, summaries, and review questions, will engage readers, deepening their learTable of Contents1. Introduction to Digital Marketing and Analytics 2. Digital Marketing KPIs, Strategy, Ecosystems, Governance, and More 3. The Evolution of Digital Analytics and the Internet 4. The Growth and Relevance of Social Media in Analytics and Digital Marketing 5. Data for Digital Marketing Analytics 6. Social Media Analytics for Digital Marketing 7. Actions, Hyperlink, and Mobile Analytics in Digital Marketing 8. Advanced AI and Algorithms 9. Basic Web Analytics and Web Intelligence 10. Advanced Web Analytics and Web Intelligence 11. Aligning Digital Marketing with Business Strategy 12. Deriving Strategic Insights and ‘Digital Value’ from Digital Marketing Analytics

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Book SynopsisMatch analysis is a performance-diagnostic procedure, which can be used to carry out systematic gaming analysis during competition and training. The analysis of team and racket sports, whether in competition, for opponent preparation (match plan), follow-up, or training is nowadays indispensable in many sports games at different levels. This analysis nevertheless presents many open questions and problem areas: Which data should be used? Who manages the data? Who provides whom with which information? How is this information presented, digested, and applied? The more complex and anonymous the data management is, the more commercial, expensive, and uncontrollable information management and provision becomes. Match Analysis: How to Use Data in Professional Sport is the first book to examine this topic through three types of data sets; video, event, and position data and show how to interpret this data and apply the findings for better team and individual sport perfoTrade Review"If you want to build a successful professional sports organization it is essential to pay attention to creating a winning culture. This includes the incorporation and integration of performance analytics and sports science data, which are invaluable tools for strategic competition preparation, game analysis, injury prevention and rehabilitation." Don Smolenski, President, Philadelphia Football Eagles, Superbowl LII Champions "As a professional squash player, I understand first-hand the importance of science and cognitive paradigms involved in world-class sports. If your goal is to be the best, you will have to check out the book on Match Analysis in order to appreciate systematic gaming analysis during competition and practice."Amanda Sobhy, Professional Squash Player (# 1 USA, # 5 World Ranking)"A unique overview of the very latest theoretical and methodological analysis possibilities of the identification of complex patterns and relationships associated with successful performance in 13 kinds of sport."Raul Pelaez Blanco, Sports Analysis, Innovation & Technology Manager, Football Club FC Barcelona"Especially as a former national coach, I know what extremely significant potential a professional match analysis has in performance soccer. The present book is the first to describe fundamental, novel and complex content in a simple and target-oriented way, not only in soccer." Jürgen KlinsmannTable of Contents1. Match Analysis in 20202. History of Match Analysis3. Match Analysis in Practice: Football4. Match Analysis in Practice: Beach VolleyballPart 1: Match Analysis on the Basis of Video Data5. Match Analysis in American Football6. Match Analysis in Basketball7. Match Analysis in Cricket8. Match Analysis in Field Hockey 9. Opponent Analysis in Football 10. Visual Exploratory Scanning in Football11. Match Analysis in Ice Hockey12. Match Analysis in Rugby13. Match Analysis in Squash14. Match Analysis in Table Tennis15. Match Analysis in Team Handball16. Match Analysis in Tennis17. Match Analysis in Volleyball Part 2: Match Analysis on the Basis of Event Data18. KPIs19. Scouting20. Normalizing Kpi’s Based on PossessionPart 3: Match Analysis on the Basis of Position Data21. Model-Based Performance Analysis in Football22. Tactical KPIs in Football23. Physiological KPIs24. KPI: Collective Behavior in Football25. Applying Machine Learning in Football: The Identification of Counterpressing in Football26. KPI in the German Bundesliga 27. Communication of Match Analysis28. Limits of Match Analysis29. Match Analysis in 2030

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Book SynopsisThe increasing availability of data in our current, information overloaded society has led to the need for valid tools for its modelling and analysis. Data mining and applied statistical methods are the appropriate tools to extract knowledge from such data. This book provides an accessible introduction to data mining methods in a consistent and application oriented statistical framework, using case studies drawn from real industry projects and highlighting the use of data mining methods in a variety of business applications. Introduces data mining methods and applications. Covers classical and Bayesian multivariate statistical methodology as well as machine learning and computational data mining methods. Includes many recent developments such as association and sequence rules, graphical Markov models, lifetime value modelling, credit risk, operational risk and web mining. Features detailed case studies based on applied projects within industry. Trade Review“If I had to recommend a good introduction to data mining, I would choose this one.” (Stat Papers, 2011) Table of Contents1 Introduction. Part I Methodology. 2 Organisation of the data. 2.1 Statistical units and statistical variables. 2.2 Data matrices and their transformations. 2.3 Complex data structures. 2.4 Summary. 3 Summary statistics. 3.1 Univariate exploratory analysis. 3.2 Bivariate exploratory analysis of quantitative data. 3.3 Multivariate exploratory analysis of quantitative data. 3.4 Multivariate exploratory analysis of qualitative data. 3.5 Reduction of dimensionality. 3.6 Further reading. 4 Model specification. 4.1 Measures of distance. 4.2 Cluster analysis. 4.3 Linear regression. 4.4 Logistic regression. 4.5 Tree models. 4.6 Neural networks. 4.7 Nearest-neighbour models. 4.8 Local models. 4.9 Uncertainty measures and inference. 4.10 Non-parametric modelling. 4.11 The normal linear model. 4.12 Generalised linear models. 4.13 Log-linear models. 4.14 Graphical models. 4..15 Survival analysis models. 4.16 Further reading. 5 Model evaluation. 5.1 Criteria based on statistical tests. 5.2 Criteria based on scoring functions. 5.3 Bayesian criteria. 5.4 Computational criteria. 5.5 Criteria based on loss functions. 5.6 Further reading. Part II Business caste studies. 6 Describing website visitors. 6.1 Objectives of the analysis. 6.2 Description of the data. 6.3 Exploratory analysis. 6.4 Model building. 6.5 Model comparison. 6.6 Summary report. 7 Market basket analysis. 7.1 Objectives of the analysis. 7.2 Description of the data. 7.3 Exploratory data analysis. 7.4 Model building. 7.5 Model comparison. 7.6 Summary report. 8 Describing customer satisfaction. 8.1 Objectives of the analysis. 8.2 Description of the data. 8.3 Exploratory data analysis. 8.4 Model building. 8.5 Summary. 9 Predicting credit risk of small businesses. 9.1 Objectives of the analysis. 9.2 Description of the data. 9.3 Exploratory data analysis. 9.4 Model building. 9.5 Model comparison. 9.6 Summary report. 10 Predicting e-learning student performance. 10.1 Objectives of the analysis. 10.2 Description of the data. 10.3 Exploratory data analysis. 10.4 Model specification. 10.5 Model comparison. 10.6 Summary report. 11 Predicting customer lifetime value. 11.1 Objectives of the analysis. 11.2 Description of the data. 11.3 Exploratory data analysis. 11.4 Model specification. 11.5 Model comparison. 11.6 Summary report. 12 Operational risk management. 12.1 Context and objectives of the analysis. 12.2 Exploratory data analysis. 12.3 Model building. 12.4 Model comparison. 12.5 Summary conclusions. References. Index.

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Book SynopsisThe increasing availability of data in our current, information overloaded society has led to the need for valid tools for its modelling and analysis. Data mining and applied statistical methods are the appropriate tools to extract knowledge from such data. This book provides an accessible introduction to data mining methods in a consistent and application oriented statistical framework, using case studies drawn from real industry projects and highlighting the use of data mining methods in a variety of business applications. Introduces data mining methods and applications. Covers classical and Bayesian multivariate statistical methodology as well as machine learning and computational data mining methods. Includes many recent developments such as association and sequence rules, graphical Markov models, lifetime value modelling, credit risk, operational risk and web mining. Features detailed case studies based on applied projects within industry. Trade Review“If I had to recommend a good introduction to data mining, I would choose this one.” (Stat Papers, 2011) Table of Contents1 Introduction. Part I Methodology. 2 Organisation of the data. 2.1 Statistical units and statistical variables. 2.2 Data matrices and their transformations. 2.3 Complex data structures. 2.4 Summary. 3 Summary statistics. 3.1 Univariate exploratory analysis. 3.1.1 Measures of location. 3.1.2 Measures of variability. 3.1.3 Measures of heterogeneity. 3.1.4 Measures of concentration. 3.1.5 Measures of asymmetry. 3.1.6 Measures of kurtosis. 3.2 Bivariate exploratory analysis of quantitative data. 3.3 Multivariate exploratory analysis of quantitative data. 3.4 Multivariate exploratory analysis of qualitative data. 3.4.1 Independence and association. 3.4.2 Distance measures. 3.4.3 Dependency measures. 3.4.4 Model-based measures. 3.5 Reduction of dimensionality. 3.5.1 Interpretation of the principal components. 3.6 Further reading. 4 Model specification. 4.1 Measures of distance. 4.1.1 Euclidean distance. 4.1.2 Similarity measures. 4.1.3 Multidimensional scaling. 4.2 Cluster analysis. 4.2.1 Hierarchical methods. 4.2.2 Evaluation of hierarchical methods. 4.2.3 Non-hierarchical methods. 4.3 Linear regression. 4.3.1 Bivariate linear regression. 4.3.2 Properties of the residuals. 4.3.3 Goodness of fit. 4.3.4 Multiple linear regression. 4.4 Logistic regression. 4.4.1 Interpretation of logistic regression. 4.4.2 Discriminant analysis. 4.5 Tree models. 4.5.1 Division criteria. 4.5.2 Pruning. 4.6 Neural networks. 4.6.1 Architecture of a neural network. 4.6.2 The multilayer perceptron. 4.6.3 Kohonen networks. 4.7 Nearest-neighbour models. 4.8 Local models. 4.8.1 Association rules. 4.8.2 Retrieval by content. 4.9 Uncertainty measures and inference. 4.9.1 Probability. 4.9.2 Statistical models. 4.9.3 Statistical inference. 4.10 Non-parametric modelling. 4.11 The normal linear model. 4.11.1 Main inferential results. 4.12 Generalised linear models. 4.12.1 The exponential family. 4.12.2 Definition of generalised linear models. 4.12.3 The logistic regression model. 4.13 Log-linear models. 4.13.1 Construction of a log-linear model. 4.13.2 Interpretation of a log-linear model. 4.13.3 Graphical log-linear models. 4.13.4 Log-linear model comparison. 4.14 Graphical models. 4.14.1 Symmetric graphical models. 4.14.2 Recursive graphical models. 4.14.3 Graphical models and neural networks. 4.15 Survival analysis models. 4.16 Further reading. 5 Model evaluation. 5.1 Criteria based on statistical tests. 5.1.1 Distance between statistical models. 5.1.2 Discrepancy of a statistical model. 5.1.3 Kullback–Leibler discrepancy. 5.2 Criteria based on scoring functions. 5.3 Bayesian criteria. 5.4 Computational criteria. 5.5 Criteria based on loss functions. 5.6 Further reading. Part II Business case studies. 6 Describing website visitors. 6.1 Objectives of the analysis. 6.2 Description of the data. 6.3 Exploratory analysis. 6.4 Model building. 6.4.1 Cluster analysis. 6.4.2 Kohonen networks. 6.5 Model comparison. 6.6 Summary report. 7 Market basket analysis. 7.1 Objectives of the analysis. 7.2 Description of the data. 7.3 Exploratory data analysis. 7.4 Model building. 7.4.1 Log-linear models. 7.4.2 Association rules. 7.5 Model comparison. 7.6 Summary report. 8 Describing customer satisfaction. 8.1 Objectives of the analysis. 8.2 Description of the data. 8.3 Exploratory data analysis. 8.4 Model building. 8.5 Summary. 9 Predicting credit risk of small businesses. 9.1 Objectives of the analysis. 9.2 Description of the data. 9.3 Exploratory data analysis. 9.4 Model building. 9.5 Model comparison. 9.6 Summary report. 10 Predicting e-learning student performance. 10.1 Objectives of the analysis. 10.2 Description of the data. 10.3 Exploratory data analysis. 10.4 Model specification. 10.5 Model comparison. 10.6 Summary report. 11 Predicting customer lifetime value. 11.1 Objectives of the analysis. 11.2 Description of the data. 11.3 Exploratory data analysis. 11.4 Model specification. 11.5 Model comparison. 11.6 Summary report. 12 Operational risk management. 12.1 Context and objectives of the analysis. 12.2 Exploratory data analysis. 12.3 Model building. 12.4 Model comparison. 12.5 Summary conclusions. References. Index.

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Book SynopsisKnowing everything you can about each click to your Web site can help you make strategic decisions regarding your business. This book is about the why, not just the how, of web analytics and the rules for developing a "culture of analysis" inside your organization. Why you should collect various types of data. Why you need a strategy.Table of ContentsForeword xv Introduction xxvii Part I The Changing Landscape of Marketing Online 1 Chapter 1 The Big Picture 3 New Marketing Trends 4 The Consumer Revolution 5 The Shift from Offline to Online Marketing 8 Instant Brand Building (and Destruction) 10 Rich Media and Infinite Variety 12 The Analysis Mandate 13 ROI Marketing 14 Innovation 15 Some Final Thoughts 16 Chapter 2 Performance Marketing 17 Data vs. Design 18 Web Design Today 18 The Web Award Fallacy 19 When Visual Design Goes Wrong 19 Where Data Goes Wrong 21 Performance-Driven Design: Balancing Logic and Creativity 22 Case Study: Dealing with Star Power 23 Case Study: Forget Marketing at All 24 Recap 25 Part II Shifting to a Culture of Analysis 27 Chapter 3 What “Culture of Analysis” Means 29 What Is a Data-Driven Organization? 30 Data-Driven Decision Making 31 Dynamic Prioritization 32 Perking Up Interest in Web Analytics 34 Establishing a Web Analytics Steering Committee 34 Starting Out Small with a Win 35 Empowering Your Employees 36 Managing Up 36 Impact on Roles beyond the Analytics Team 37 Cross-Channel Implications 40 Questionnaire: Rating Your Level of Data Drive 41 Recap 42 Chapter 4 Avoiding Stumbling Points 43 Do You Need an Analytics Intervention? 44 Analytics Intervention Step 1: Admitting the Problem 44 Analytics Intervention Step 2: Admit That You Are the Problem 46 Analytics Intervention Step 3: Agree That This Is a Corporate Problem 47 The Road to Recovery: Overcoming Real Gaps 48 Issue #1: Lack of Established Processes and Methodology 49 Issue #2: Failure to Establish Proper KPIs and Metrics 49 Issue #3: Data Inaccuracy 50 Issue #4: Data Overload 52 Issue #5: Inability to Monetize the Impact of Changes 53 Issue #6: Inability to Prioritize Opportunities 54 Issue #7: Limited Access to Data 54 Issue #8: Inadequate Data Integration 55 Issue #9: Starting Too Big 56 Issue #10: Failure to Tie Goals to KPIs 57 Issue #11: No Plan for Acting on Insight 58 Issue #12: Lack of Committed Individual and Executive Support 58 Recap 59 Part III Proven Formula for Success 61 Chapter 5 Preparing to Be Data-Driven 63 Web Analytics Methodology 64 The Four Steps of Web Analytics 65 Defining Business Metrics (KPIs) 65 Reports 66 Analysis 67 Optimization and Action 67 Results and Starting Again 68 Recap 68 Chapter 6 Defining Site Goals, KPIs, and Key Metrics 71 Defining Overall Business Goals 72 Defining Site Goals: The Conversion Funnel 73 Awareness 73 Interest 73 Consideration 74 Purchase 74 Website Goals and the Marketing Funnel 74 Understanding Key Performance Indicators (KPIs) 75 Constructing KPIs 76 Creating Targets for KPIs 79 Common KPIs for Different Site Types 80 E-Commerce 80 Lead Generation 82 Customer Service 83 Content Sites 85 Branding Sites 87 Recap 88 Chapter 7 Monetizing Site Behaviors 89 The Monetization Challenge 90 Case Study: Monetization and Motivation 90 Web-Monetization Models 93 Top 10 Ways Monetization Models Can Help Your Company 94 How to Create Monetization Models 95 Assembling a Monetization Model 97 Monetization Models for Different Site Types and Behaviors 100 E-Commerce Opportunity 100 Lead Generation 102 Customer Service 104 Ad-Supported Content Sites 106 Recap 108 Chapter 8 Getting the Right Data 109 Primary Data Types 110 Warning: Avoid Data Smog 110 Behavioral Data 111 Attitudinal Data 112 Balancing Behavioral and Attitudinal Data 112 Competitive Data 113 Secondary Data Types 116 Customer Interaction and Data 116 Third-Party Research 117 Usability Benchmarking 117 Heuristic Evaluation and Expert Reviews 118 Community Sourced Data 119 Leveraging These Data Types 120 Comparing Performance with Others 120 What Is a Relative Index? 122 Examples of Relative Indices 122 Customer Engagement 123 Methodology: Leveraging Indices across Your Organization 124 Case Study: Leveraging Different Data Types to Improve Site Performance 126 Recap 128 Chapter 9 Analyzing Site Performance 129 Analysis vs. Reporting 130 Don’t Blame Your Tools 131 Examples of Analysis 132 Analyzing Purchasing Processes to Find Opportunities 132 Analyzing Lead Processes to Find Opportunities 135 Understanding What Onsite Search Is Telling You 136 Evaluating the Effectiveness of Your Home Page 138 Evaluating the Effectiveness of Branding Content: Branding Metrics 138 Evaluating the Effectiveness of Campaign Landing Pages 140 Segmenting Traffic to Identify Behavioral Differences 142 Segmenting Your Audience 142 Case Study: Segmenting for a Financial Services Provider 143 Analyzing Drivers to Offline Conversion 144 Tracking Online Partner Handoffs and Brick-And-Mortar Referrals 144 Tracking Offline Handoffs to Sales Reps 144 Tracking Visitors to a Call Center 145 Delayed Conversion 146 Tracking Delayed Conversion 146 Reporting in a Timely Manner 147 Recap 147 Chapter 10 Prioritizing 149 How We Prioritize 150 The Principles of Dynamic Prioritization 150 Traditional Resource Prioritization 151 Dynamic Prioritization 152 Dynamic Prioritization Scorecard 154 Dynamic Prioritization in Action 154 Forecasting Potential Impact 155 Comparing Opportunities 157 Moving Your Company Toward Dynamic Prioritization 157 Overcoming Common Excuses 158 Conclusion 159 Recap 160 Chapter 11 Moving from Analysis to Site Optimization 161 Testing Methodologies and Tools 162 A/B Testing 162 A/B/n Testing 162 Multivariate Tests 162 How to Choose a Test Type 163 Testing Tools 164 What to Test 164 Prioritizing Tests 166 Creating a Successful Test 167 Understanding Post-Test Analysis 168 Optimizing Segment Performance 168 Example One: Behavior-Based Testing 169 Example Two: Day-of-the-Week Testing 169 Planning for Optimization 169 Budgeting for Optimization 170 Skills Needed for a Successful Optimization Team 171 Overcoming IT Doubts 173 IT Doesn’t Understand the Process 174 Testing Prioritization 174 Lack of Executive Support 174 Learning from Your Successes and Mistakes 175 Learning from the Good and the Bad 175 A Quick Way Up the Learning Curve 176 Spreading the Word 176 Test Examples 176 Price 177 Promotional 178 Message 179 Page Layout 180 New Site Launches or New Functionality 180 Site Navigation and Taxonomy 181 Recap 182 Chapter 12 Agencies 185 Why Use an Agency at All? 186 Finding an Agency 187 Creating an RFP 188 Introduction and Company Background 189 Scope of Work and Business Goals 191 Timelines 193 Financials 194 The Rest of the RFP: Asking the Right Questions 195Mutual Objective: Success 196 Doing the Work 198 The Secret Agency Sauce 199 Recap 200 Chapter 13 The Creative Brief 201 What Is a Creative Brief? 202 The Brief 202 Components of a Data-Driven Brief 203 Creative Brief Metrics 203 Analytics and Creativity 205 The Iterative Design Cycle 206 A Sample Creative Brief 206 Creative Brief: Robotwear.Com 206 Recap 210 Chapter 14 Staffing and Tuning Your Web Team 211 Skills That Make a Great Web Analyst 212 Technical vs. Interpretive Expertise 212 Key Web Analyst Skills 213 The Roles of the Web Analyst 214 Building Your Web-Analytics Team: Internal and External Teams 215 Estimating Your Cost 215 Key Analytics Positions 216 Expanding the Circle of Influence 217 Internal vs. External Teams 217 Education and Training for Web Analysts 219 Web Analytics Association 219 Conferences 219 University of British Columbia Courses 220 Message Boards 220 ClickZ and Other Online Media 220 Blogs 220 Web Analytics Wednesdays 220 Vendor Training 221 Agency Partners 221 Hands-on Experience 221 Recap 221 Chapter 15 Partners 223 When to Choose an Analytics Tool Vendor 224 Methodology for Selecting a Tool 225 Selecting a Review Committee 225 Establishing a Timeline 226 Criteria to Review and Select Vendors 226 10 Questions to Ask Web Analytics Vendors 228 Comparing to Free Tools 229 ASP or Software Version 229 Data Capture 230 Total Cost of Ownership 230 Support 231 Data Segmentation 232 Data Export and Options 232 Data Integration 233 The Future 233 References 234 Recap 234 Conclusion 235 Appendix:Web Analytics “Big Three” Definitions 237 How We Define Terms 238 Definition Framework Overview 239 Term: Unique Visitors 239 Term: Visits/Sessions 240 Term: Page Views 240 Index 243

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Book SynopsisProvides a powerful and versatile methodology that enables researchers to design their investigations and analyse data effectively and safely, without the need for formal statistical training.Trade Review"This is an important book; the authors have done a quality job..." (N.I.R. News, Vol 12/1, 2001) "This book is recommended to students of chemical, biochemical and food engineering, scientists and industrial practitioners". (Chemical Biochemical Engineering, June 2001) "a possible source of inspiration" (Measurement Science Technology, October 2001) "a powerful and versatile methodology" (Chemie Plus, June 2001) "...should prove a very useful text for this target readership." (Short Book Reviews, Vol. 22, No. 1, April 2002) "...Through the book, there is a solid philosophy and opinions supported by the intelligence and experience of the couple [authors]..." (Applied Spectroscopy, Vol.56, No.8, 2002) "...The book is written by two experts in the field with nearly 30 years of experience, and this is reflected in every aspect of the book..." (Journal of Chemometrics, No.16, 2002)Table of ContentsPreface. Acknowledgements. OVERVIEW. Why Multivariate Data Analysis? Qualimetrics for Determining Quality. A Layman's Guide to Multivariate Data Analysis. METHODOLOGY. Some Estimation Concepts. Analysis of One Data Table X: Principle Component Analysis. Analysis of Two Data Tables X and Y: Partial Least Squares Regression (PLSR). Example of Multivariate Calibration Project. Interpretation of Many Types of Data X and Y: Exploring Relationships in Interdisciplinary Data Sets. Classification and Discrimination X_1, X_2, X_3: Handling Heterogeneous Sample Sets. Validation X and Y. Experimental Planning Y and X. APPLICATIONS. Multivariate Calibration: Quality Determination of Wheat From High-speed NIR Spectra. Analysis of Questionnaire Data: What Determines Quality of the Working Environment? Analysis of a Heterogeneous Sample Set: Predicting Toxicity From Quantum Chemistry. Multivariate Statistical Process Control: Quality Monitoring of a Sugar Production Process. Design and Analysis of Controlled Experiments: Reducing Loss of Quality in Stored Food. Appendix A1: How the Present Book Relates to Some Mathematical Modelling Traditions in Science. Appendix A2: Sensory Science. Appendix A3.1: Bi-linear Modelling Has Many Applications. Appendix A3.2: Common Problems and Pitfalls in Soft Modelling. Appendix A4: Mathematical Details. Appendix A5: PCA Details. Appendix A6: PLS Regression Details. Appendix A7: Modelling the Unknown. Appendix A8: Non-linearity and Weighting. Appendix A9: Classification and Outlier Detection. Appendix A10: Cross-validation Details. Appendix A11: Power Estimation Details. Appendix A12: What Makes NIR Data So Information-rich? Appendix A13: Consequences of the Working Environment Survey. Appendix A14: Details of the Molecule Class Models. Appendix A15: Forecasting the Future. Appendix A16: Significance Testing with Cross-validation vs. ANOVA. References. Index.

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Book SynopsisFuzzy clustering, which combines fuzzy logic and cluster analysis techniques, has experienced a spur of interest in recent years owing to its important applications in image recognition. This revised, updated, and expanded translation of the German book deals with the ideas and algorithms of fuzzy clustering and their applications.Table of ContentsIntroduction. Basic Concepts. Classical Fuzzy Clustering Algorithms. Linear and Ellipsoidal Prototypes Shell Prototypes. Polygonal Object Boundaries. Cluster Estimation Models. Cluster Validity. Rule Generation with Clustering. Appendix. Bibliography.

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Book SynopsisThe kernel functions methodology described here provides a powerful and unified framework for disciplines ranging from neural networks and pattern recognition to machine learning and data mining. This book provides practitioners with a large toolkit of algorithms, kernels and solutions ready to be implemented, suitable for standard pattern discovery problems.Trade Review'Kernel methods form an important aspect of modern pattern analysis, and this book gives a lively and timely account of such methods. … if you want to get a good idea of the current research in this field, this book cannot be ignored.' SIAM Review'… the book provides an excellent overview of this growing field. I highly recommend it to those who are interested in pattern analysis and machine learning, and especailly to those who want to apply kernel-based methods to text analysis and bioinformatics problems.' Computing Reviews' … I enjoyed reading this book and am happy about is addition to my library as it is a valuable practitioner's reference. I especially liked the presentation of kernel-based pattern analysis algorithms in terse mathematical steps clearly identifying input data, output data, and steps of the process. The accompanying Matlab code or pseudocode is al extremely useful.' IAPR NewsletterTable of ContentsPreface; Part I. Basic Concepts: 1. Pattern analysis; 2. Kernel methods: an overview; 3. Properties of kernels; 4. Detecting stable patterns; Part II. Pattern Analysis Algorithms: 5. Elementary algorithms in feature space; 6. Pattern analysis using eigen-decompositions; 7. Pattern analysis using convex optimisation; 8. Ranking, clustering and data visualisation; Part III. Constructing Kernels: 9. Basic kernels and kernel types; 10. Kernels for text; 11. Kernels for structured data: strings, trees, etc.; 12. Kernels from generative models; Appendix A: proofs omitted from the main text; Appendix B: notational conventions; Appendix C: list of pattern analysis methods; Appendix D: list of kernels; References; Index.

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

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Book SynopsisDemonstrates how odd and unusual math problems can be solved by bringing together basic physics ideas and powerful computers. This title looks at how the art of number-crunching has changed since the advent of computers, and how high-speed technology helps to solve conundrums such as the three-body, Monte Carlo, and gambler's ruin problems.Trade Review"Great stories. Interesting and challenging problems. Instructive MATLAB code. Lots of physics. That's my in-a-nutshell assessment... Nahin takes on the subject of using computers to solve difficult problems, many in physics, that couldn't be solved before computers... This is one of those books that one can read as a spectator, enjoying the scenery, taking in the landscape, appreciating the rich stories--my relationship with the book--or one can dive in, study the many equations, run the code, and have a personal experience of how problems that were unsolvable just a few decades ago have succumbed to computers."--Sol Lederman, Wild About Math "Number Crunching is most timely, given the escalating scale of economic, commercial, and financial transactions, necessitating thinking about, evaluating, and communicating on a much larger scale... The presentation in Number Crunching is simultaneously accessible, readable, entertaining, daunting, sophisticated, and technical."--Stephen E. Roulac, New York Journal of Books "Number-Crunching is packed with copious notes and references and augmented by significant challenge problems that take the reader beyond the text and which would make good undergraduate projects... Nahin's aim is clearly to convey enthusiasm for the subject to a younger reader and to give a glimpse of what is technically possible... He looks to convey the excitement that he and many of us had when first attracted to the physical sciences as we were growing up--the excitement at the realisation that, given a few tools, even an awkward teenager can make quantitative statements about the world."--C.J. Howls, Times Higher Education "Paul Nahin, a prolific and knowledgeable expository writer, is a professor emeritus of electrical engineering at the University of New Hampshire. What he offers in Number-Crunching might be described as a mix of (1) supplementary readings for courses in mathematics, physics, or electrical engineering, (2) 'challenge problems' intended as a brain-jogging call to prospective professionals, and (3) a garage sale of mathematical miscellania and esoterica."--Philip J. Davis, SIAM News "[Number-Crunching] is impressive for several reasons. First, Nahin has found the right level--not too easy and not too hard. Second, the problem selections and topics are interesting and in several cases give surprising results. Finally, the book is just plain fun."--Choice "[A] highly entertaining and rewarding read."--Dean Rickles, Mathematical Reviews "Unlike writers of popular mathematics books, Nahin is more then happy to spray the pages with equations! Since these are surrounded by lucid and informal explanations, they add significant value to the book... Whether or not you are the computing type, there is a lot to enjoy about this book."--Alan Stevens, Mathematics TODAY "Many of these challenge problems would make excellent projects--both in mathematics or physics--for undergraduates to explore. Number-Crunching's greatest strength is providing a perspective on how three unique yet vastly intertwined fields interact while keeping readers more and more intrigued as they progress through the text. Readers will find abundant resources to quench their thirst for knowledge of computational mathematics and physics."--Brandon Milanovich, Mathematics TeacherTable of ContentsIntroduction x Chapter 1: FEYNMAN MEETS FERMAT 1 1.1 The Physicist as Mathematician 1 1.2 Fermat's Last Theorem 2 1.3 "Proof" by Probability 3 1.4 Feynman's Double Integral 6 1.5 Things to come 10 1.6 Challenge Problems 11 1.7 Notes and References 13 Chapter 2: Just for Fun: Two Quick Number-Crunching Problems 16 2.1 Number-Crunching in the Past 16 2.2 A Modern Number-Cruncher 20 2.3 Challenge Problem 25 2.4 Notes and References 25 Chapter 3: Computers and Mathematical Physics 27 3.1 When Theory Isn't Available 27 3.2 The Monte Carlo Technique 28 3.3 The Hot Plate Problem 34 3.4 Solving the Hot Plate Problem with Analysis 38 3.5 Solving the Hot Plate Problem by Iteration 44 3.6 Solving the Hot Plate Problem with the Monte Carlo Technique 50 3.7 ENIAC and MANIAC-I: the Electronic Computer Arrives 55 3.8 The Fermi-Pasta-Ulam Computer Experiment 58 3.9 Challenge Problems 73 3.10 Notes and References 74 Chapter 4: The Astonishing Problem of the Hanging Masses 82 4.1 Springs and Harmonic Motion 82 4.2 A Curious Oscillator 87 4.3 Phase-Plane Portraits 96 4.4 Another (Even More?) Curious Oscillator 99 4.5 Hanging Masses 104 4.6 Two Hanging Masses and the Laplace Transform 108 4.7 Hanging Masses and MATLAB 113 4.8 Challenge Problems 124 4.9 Notes and References 124 Chapter 5: The Three-Body Problem and Computers 131 5.1 Newton's Theory of Gravity 131 5.2 Newton's Two-Body Solution 139 5.3 Euler's Restricted Three-Body Problem 147 5.4 Binary Stars 155 5.5 Euler's Problem in Rotating Coordinates 166 5.6 Poincare and the King Oscar II Competition 177 5.7 Computers and the Pythagorean Three-Body Problem 184 5.8 Two Very Weird Three-Body Orbits 195 5.9 Challenge Problems 205 5.10 Notes and References 207 Chapter 6: Electrical Circuit Analysis and Computers 218 6.1 Electronics Captures a Teenage Mind 218 6.2 My First Project 220 6.3 "Building" Circuits on a Computer 230 6.4 Frequency Response by Computer Analysis 234 6.5 Differential Amplifiers and Electronic Circuit Magic 249 6.6 More Circuit Magic: The Inductor Problem 260 6.7 Closing the Loop: Sinusoidal and Relaxation Oscillators by Computer 272 6.8 Challenge Problems 278 6.9 Notes and References 281 Chapter 7: The Leapfrog Problem 288 7.1 The Origin of the Leapfrog Problem 288 7.2 Simulating the Leapfrog Problem 290 7.3 Challenge Problems 296 7.4 Notes and References 296 Chapter 8: Science Fiction: When Computers Become Like Us 297 8.1 The Literature of the Imagination 297 8.2 Science Fiction "Spoofs" 300 8.3 What If Newton Had Owned a Calculator? 305 8.4 A Final Tale: the Artificially Intelligent Computer 314 8.5 Notes and References 324 Chapter 9: A Cautionary Epilogue 328 9.1 The Limits of Computation 328 9.2 The Halting Problem 330 9.3 Notes and References 333 Appendix (FPU Computer Experiment MATLAB Code) 335 Solutions to the Challenge Problems 337 Acknowledgments 371 Index 373 Also by Paul J. Nahin 377

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Book SynopsisAn 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 inequitiesTrade Review"Winner of a Millard Meiss Publication Fund Grant, College Art Association""Painting by Numbers…[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, Hyperallergic""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 *"Diana Seave Greenwald’s Painting by Numbers: Data-Driven Histories of Nineteenth-Century Art 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. . . . Painting by Numbers shows the promise of what can be achieved when an abundance of information is wedded with insightful scholarship."---Matt Garklavs, ARLIS/NA Reviews"[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. . . . [Painting by Numbers] represents a concrete application of the benefits of an interdisciplinary approach in humanities and social sciences."---Laura Paganl, Journal of Cultural Economics"[A] great benefit to art historians unpracticed in economic theory."---Elizabeth L. Block, Panorama"Painting By Numbers offers methods and interpretations that may revise art historians’ assumptions about what we do and how we do it."---Julie Codell, Winterthur Portfolio"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."---Jorge Sebastián Lozano, Art History

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Book SynopsisYour life online is their product.In 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.Colonialism has not disappeared it has taken on a new form.In 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.In this searinTrade ReviewI wish that Data Grab 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 -- Timnit Gebru, founder of the Distributed AI Research InstituteA 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 -- Mike Savage, author of 'Social Class in the 21st Century'Remarkable... Data Grab helps us understand that the historical and ongoing relations of power have extended to the realm of data, a new raw material of digital capitalism. Mejias and Couldry place us on a path to recognise, resist, and challenge these forces -- Dr Ramesh Srinivasan, Professor at the UCLA Department of Information Studies and Director of UC Digital Cultures LabAs in their previous work, Mejias and Couldry show how important it is to take the perspective of the colonized, not the colonizer, in explaining how the digital world is governed. Data Grab offers important insights into 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. A true eye-opener -- José van Dijck, Distinguished Professor of Media and Digital Society, Utrecht UniversityIn this essential and original work, Mejias and Couldry lay out a powerful and persuasive analysis of the logical continuity between modern colonialism and the extraction of data by Big Tech and its platforms. Their call to resist data colonialism could not be more urgent or more timely -- Jeremy Gilbert, author of 'Hegemony Now: How Big Tech and Wall Street Won the World' and 'Twenty-First Century Socialism'

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Book SynopsisThis text describes the development of magnetic recording over the past century, by selecting major product designs and the innovative technology that they introduced. Separated into three parts, the book deals specifically with the history of audio recording, video recording and data recording.Table of ContentsAcknowledgments. Contributors. Introduction (C. Mee & E. Daniel). AUDIO RECORDING. The Magnetic Recording of Sound (M. Clark). The Telegraphone (M. Clark & H. Nielsen). Steel Tape and Wire Recorders (M. Clark). The Introduction of the Magnetophon (F. Engel). Building on the Magnetophon (B. Gooch). Product Diversification (M. Clark). The History of Digital Audio (J. Watkinson). VIDEO RECORDING. The Challenge of Recording Video (F. Remley). Early Fixed-Head Video Recorders (F. Jorgensen). The Ampex Quadruplex Recorders (J. Mallinson). Helical-Scan Recorders for Broadcasting (H. Sugaya). Consumer Video Recorders (H. Sugaya). Digital Video Recording (K. Sadashige). DATA RECORDING. Capturing Data Magnetically (J. Monson). Data Storage on Drums (S. Rubens). Data Storage on Tape (W. Phillips). Data Storage on Hard Magnetic Disks (L. Stevens). Data Storage on Floppy Disks (D. Noble). Instrumentation Recording on Magnetic Tape (F. Jorgensen). Index. About the Editors.

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Book SynopsisThe Average is Always Wrong is a completely pragmatic and hands-on guide to harnessing data to transform your business for the better.

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

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

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