Data science and analysis Books

Book SynopsisTable of ContentsAcknowledgment xi Preface xiii 1 Data Munging Basics 1 Introduction 1 1.1 Filtering and Selecting Data 6 1.2 Treating Missing Values 11 1.3 Removing Duplicates 14 1.4 Concatenating and Transforming Data 16 1.5 Grouping and Data Aggregation 20 References 20 2 Data Visualization 23 2.1 Creating Standard Plots (Line, Bar, Pie) 26 2.2 Defining Elements of a Plot 30 2.3 Plot Formatting 33 2.4 Creating Labels and Annotations 38 2.5 Creating Visualizations from Time Series Data 42 2.6 Constructing Histograms, Box Plots, and Scatter Plots 44 References 54 3 Basic Math and Statistics 57 3.1 Linear Algebra 57 3.2 Calculus 58 3.2.1 Differential Calculus 58 3.2.2 Integral Calculus 58 3.3 Inferential Statistics 60 3.3.1 Central Limit Theorem 60 3.3.2 Hypothesis Testing 60 3.3.3 ANOVA 60 3.3.4 Qualitative Data Analysis 60 3.4 Using NumPy to Perform Arithmetic Operations on Data 61 3.5 Generating Summary Statistics Using Pandas and Scipy 64 3.6 Summarizing Categorical Data Using Pandas 68 3.7 Starting with Parametric Methods in Pandas and Scipy 84 3.8 Delving Into Non-Parametric Methods Using Pandas and Scipy 87 3.9 Transforming Dataset Distributions 91 References 94 4 Introduction to Machine Learning 97 4.1 Introduction to Machine Learning 97 4.2 Types of Machine Learning Algorithms 101 4.3 Explanatory Factor Analysis 114 4.4 Principal Component Analysis (PCA) 115 References 121 5 Outlier Analysis 123 5.1 Extreme Value Analysis Using Univariate Methods 123 5.2 Multivariate Analysis for Outlier Detection 125 5.3 DBSCan Clustering to Identify Outliers 127 References 133 6 Cluster Analysis 135 6.1 K-Means Algorithm 135 6.2 Hierarchial Methods 141 6.3 Instance-Based Learning w/ k-Nearest Neighbor 149 References 156 7 Network Analysis with NetworkX 157 7.1 Working with Graph Objects 159 7.2 Simulating a Social Network (ie; Directed Network Analysis) 163 7.3 Analyzing a Social Network 169 References 171 8 Basic Algorithmic Learning 173 8.1 Linear Regression 173 8.2 Logistic Regression 183 8.3 Naive Bayes Classifiers 189 References 195 9 Web-Based Data Visualizations with Plotly 197 9.1 Collaborative Aanalytics 197 9.2 Basic Charts 208 9.3 Statistical Charts 212 9.4 Plotly Maps 216 References 219 10 Web Scraping with Beautiful Soup 221 10.1 The BeautifulSoup Object 224 10.2 Exploring NavigableString Objects 228 10.3 Data Parsing 230 10.4 Web Scraping 233 10.5 Ensemble Models with Random Forests 235 References 254 Data Science Projects 257 11 Covid19 Detection and Prediction 259 Bibliography 275 12 Leaf Disease Detection 277 Bibliography 283 13 Brain Tumor Detection with Data Science 285 Bibliography 295 14 Color Detection with Python 297 Bibliography 300 15 Detecting Parkinson’s Disease 301 Bibliography 302 16 Sentiment Analysis 303 Bibliography 306 17 Road Lane Line Detection 307 Bibliography 315 18 Fake News Detection 317 Bibliography 318 19 Speech Emotion Recognition 319 Bibliography 322 20 Gender and Age Detection with Data Science 323 Bibliography 339 21 Diabetic Retinopathy 341 Bibliography 350 22 Driver Drowsiness Detection in Python 351 Bibliography 356 23 Chatbot Using Python 357 Bibliography 363 24 Handwritten Digit Recognition Project 365 Bibliography 368 25 Image Caption Generator Project in Python 369 Bibliography 379 26 Credit Card Fraud Detection Project 381 Bibliography 391 27 Movie Recommendation System 393 Bibliography 411 28 Customer Segmentation 413 Bibliography 431 29 Breast Cancer Classification 433 Bibliography 443 30 Traffic Signs Recognition 445 Bibliography 453

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Book SynopsisDiscover how the explosions in data analytics, AI, and digital communication are benefiting sports and sports fans around the world In Sports and Technology Have The Power To Change The World: Driving Positive Change Through The Use of Data and AI, the Director of Microsoft Sports, Jon Flynn, delivers an insightful new take on the transformative power of sport and its ability to unite people, break down barriers, and generate positive change. The author explains the critical role that technology has played in growing the impact of sporting events and enabling social change while fostering community improvement. In the book, you'll explore many of the ways in which sports, enabled by new tech, have made significant contributions to society and promoted individual development, health, and wellbeing. You'll also find: Discussions of green technologies and climate and sustainability initiatives linked to sport, with a case study about the 2022 Beijing Winter OlympicsExplorations of the impact of advanced data analytics, with a case study focusing on the 2013 NBA Final matchup between the Miami Heat and the San Antonio SpursHow sport scientists are optimizing player performance Perfect for anyone interested in the intersection of sport, society, and technology, Sports and Technology Have The Power To Change The World is an easy to read and endlessly fascinating look at how the unique combination of athletics and tech makes the world a better place.

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Book SynopsisJan-Willem Middelburg is a Dutch entrepreneur and author with a passion for technology and innovation. He is the CEO and co-founder of Cybiant, a global technology that company that helps to create a more sustainable world through analytics, big data and automation. He is also President and Chief Examiner of the Enterprise Big Data Framework, an independent organization dedicated to upskilling individuals with expertise in Big Data. In partnership with APMG-International, the Enterprise Big Data Framework offers vendor-neutral certifications for individuals.Trade Review"The Enterprise Big Data Framework is relevant for everybody within an organisation engaged in driving maximum benefits from data. There is something for everybody; from the board considering governance and ethical behaviour to individuals within the organisation knowing where they fit and the value they can get from better use of their organisation's data. If you are considering a transformation project, this is an excellent guide for your project team." * Richard Pharro, CEO, The APM Group Limited *"If you are looking for a good guide to empower your knowledge on big data and to find a framework to help you on your big data journey, then this book is for you. From learning what big data is to defining a big data strategy, Jan-Willem has built a book to empower the learner on the topic of big data." * Jordan Morrow, Chief Strategy & Transformation Officer, DataPrime and Author of Be Data Literate *"This book is a master piece for those who are familiar and those who discover the world of data. It provides an "a la carte framework" starting with a (big) data strategy and the supporting aspects such as big data functions, architecture and algorithms. It covers in depth data platforms architectures, its management as well as data governance, data catalogue and all the required security considerations associated to the various data classifications. You will find details of data life cycle management, of various machine learning algorithms and an important chapter covering AI ethics when building and deploying sophisticated algorithms using data. The concepts covered in this book apply to on-premises and in the (public) cloud environments, making this book a must read." * Jean-Michel Coeur, APAC Technology Practice Lead, Data Analytics, Google Cloud *Table of Contents Section - ONE: Introduction to Big Data; Chapter - 01: Introduction to Big Data; Chapter - 02: The Big Data framework; Chapter - 03: Big Data strategy; Chapter - 04: Big Data architecture; Chapter - 05: Big Data algorithms; Chapter - 06: Big Data processes; Chapter - 07: Big Data functions; Chapter - 08: Artificial intelligence; Section - TWO: Enterprise Big Data analysis; Chapter - 09: Introduction to Big Data analysis; Chapter - 10: Defining the business objective; Chapter - 11: Data ingestion – importing and reading data sets; Chapter - 12: Data preparation – cleaning and wrangling data; Chapter - 13: Data analysis – model building; Chapter - 14: Data presentation; Section - THREE: Enterprise Big Data engineering; Chapter - 15: Introduction to Big Data engineering; Chapter - 16: Data modelling; Chapter - 17: Constructing the data lake; Chapter - 18: Building an enterprise Big Data warehouse; Chapter - 19: Design and structure of Big Data pipelines; Chapter - 20: Managing data pipelines; Chapter - 21: Cluster technology; Section - FOUR: enterprise Big Data algorithm design; Chapter - 22: Introduction to Big Data algorithm design; Chapter - 23: Algorithm design – fundamental concepts; Chapter - 24: Statistical machine learning algorithms; Chapter - 25: The data science roadmap; Chapter - 26: Programming languages 26 visualization and simple metrics; Chapter - 27: Advanced machine learning algorithms; Chapter - 28: Advanced machine learning classification algorithms; Chapter - 29: Technical communication and documentation; Section - FIVE: Enterprise Big Data architecture; Chapter - 30: Introduction to the Big Data architecture; Chapter - 31: Strength and resilience – the Big Data platform; Chapter - 32: Design principles for Big Data architecture; Chapter - 33: Big Data infrastructure; Chapter - 34: Big Data platforms; Chapter - 35: The Big Data application provider; Chapter - 36: System orchestration in Big Data

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Book SynopsisNicholas Kelly is a principal at G&K Consulting, based in Bonney Lake, Washington. He is a leader in analytics adoption having designed and developed dashboards for some of the world's largest companies, from global banks to Formula 1 teams. He is a frequent speaker at international conferences, has trained thousands of professionals in data visualization and analytics adoption and is the inventor of the Dashboard Wireframe KitTrade Review"Over the many years I have worked in data analytics the field has grown significantly. It's no longer enough to deliver accurate numbers and charts: we need to consider business value, governance, adoption, story-telling and even corporate culture. Nick Kelly's book covers all that ground and more." * Donald Farmer, Principle, Tree Hive Strategy *"Achieving real business impact with data goes far beyond technical considerations - you must focus on the human considerations. Through practical examples and real-world stories, Nick has crafted a book that will teach you to capitalize on the human side of data analytics and deliver business-changing results." * David Langer, Founder, Dave on Data *"If you are looking to build an analytic capability or wondering how to improve one, this book covers the why what and how in a down to earth narrative. If you want to fast track from lessons learned and get your program running from the get-go, read this book first." * Akihiko Katayama, Chief Technology Officer, BaronsAI *Table of Contents Chapter - 01: Insight mindset; Chapter - 02: Strategy and planning; Chapter - 03: UX principles; Chapter - 04: Requirements gathering; Chapter - 05: Data assessment; Chapter - 06: The agile process; Chapter - 07: Storytelling; Chapter - 08: Crafting the vision; Chapter - 09: Managing change; Chapter - 10: Adoption and ownership; Chapter - 11: Training and documentation; Chapter - 12: Launch

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Book SynopsisDr Antonio Weiss, based in London, UK, is a Senior Partner at The PSC, an award-winning public service consultancy specialising in user-centred design, digital, strategy and delivery. He has advised the Office for Artificial Intelligence, the UK Space Agency and NHSx as well as numerous other pioneering digital organizations and frequently trains leaders to become digital transformation experts. He is also an Affiliated Researcher at the University of Cambridge's Digital State programme and the co-founder of Thomas Clipper, an e-commerce lifestyle brand for men featured in GQ, The Guardian and The Telegraph.Trade Review"Refreshingly free of waffle and ego, this is an incredibly valuable guide - in fact almost a recipe - for launching and landing meaningful digital and business transformation. Every page shares precision insights and immediately actionable suggestions with a simplicity and clarity that only comes from many years of walking the walk. As useful for someone starting their digital transformation journey as one despairing about their progress to date!" * Pete Herlihy, Lead Product Manager, UK Government Digital Service *"This is a great book that will be of huge help to those involved in digitally enabled transformation. It is intensely practical, with many good case studies, but also very readable. The book is aimed squarely at those responsible for initiating and leading the change rather than technical experts. From me it comes strongly recommended. I wish it had been written a long time ago." * Lord Bob Kerslake, former Head of the UK Civil Service *"Digitalisation will have a major impact on the way services are provided and how business is conducted. Digital Transformation can seem like a daunting challenge to any business leader and it is essential that we all build our understanding and knowledge as these major changes take place. This excellent practical guide, written by Antonio Weiss will help you achieve this and de-risk your transformation programme. The guide gives you the tools and advice that you will need to succeed as well as being a valuable resource for those leading any digital change process." * Sir Ian Carruthers, Chancellor of the University of the West of England and former NHS Chief Executive *"Organizations globally are spending billions on digital transformation right now; for many, it is one of their very largest investment areas. But it is easy to waste money on technology, consulting and internal costs because organisations and leaders don't know what they are really trying to do, or how to achieve their goals. The Practical Guide to Digital Transformation is truly practical, with great examples, case studies and tips that will be of value to anyone tasked with delivery. I particularly liked the "what you might say in your next meeting" list at the end of each chapter, and this book is destined to become a well-used friend to many senior managers, consultants and business students." * Peter Smith, Procurement expert and author of Bad Buying *"Organisations are more aware than ever of the need to transform themselves for the digital age. But many, perhaps most, still struggle. The Practical Guide to Digital Transformation is the ideal companion for any company serious about adaptation. Written by a leading digital transformation expert, it provides the ideas and vocabulary to bring about change, illuminated by incisive case studies. It is written in user-friendly language, guiding the reader through the "whys" and "hows" of making change happen. This outstanding guide is essential reading for decision-makers, digital leaders, and practitioners seeking to ensure that their organisations thrive now and in the future." * Dr Tanya Filer, Digital State Project Lead, Bennett Institute for Public Policy, University of Cambridge and founder of StateUp *"The Practical Guide to Digital Transformation is a really excellent, step by step primer to help your organisation get the very best from digital technology. It is clear but not dumbed-down. It will expand the thinking of new and experienced digital leaders alike, and gives concrete, actionable ways of bringing your organisation with you." * Phil Buckley, Prix Jeunesse and BIMA award-winning Digital Product Manager *"This book provides a unique, pragmatic, real-world guide to digital transformation. Each chapter unlocks different aspects, viewpoints and the considerations needed to embed change in a multitude of environments. The book is pitched to all audiences, providing the tools needed for anyone interested or actively involved in digital transformation. Antonio has managed to pack the learning from his many years as a consultant into this wonderful book." * Gary McAllister, Chief Technology Officer for NHS London and author of An Introduction to Digital Healthcare in the NHS *"For anyone trying to implement transformative change in their organisation - this is a complete must have. It's a remarkably waffle-free book that shows how to put theory into practice in the most practical of ways - with real world examples of where it's worked and where it hasn't. It's genuinely a breath of fresh air and an invaluable read for anyone interested in using digital principles and tools to transform their organisation." * Tom Lillywhite, Director of Digital Transformation, UK Labour Party *Table of Contents Chapter - 00: Introduction; Section - ONE: Getting the strategy and the roadmap right; Chapter - 01: What is a digital strategy?; Chapter - 02: Choosing your strategic delivery vehicle; Chapter - 03: How to do a digital roadmap; Section - TWO: Making change happen; Chapter - 04: Ways of working; Chapter - 05: Senior and organisational buy-in; Chapter - 06: Funding digital; Section - THREE: Doing digital; Chapter - 07: Understanding your users; Chapter - 08: Doing the bare minimum; Chapter - 09: Building new services; Chapter - 10: Buying technology; Chapter - 11: The cloud, APIs and open-source; Chapter - 12: Using data science to inform decision-making; Chapter - 13: Stimulating innovation; Section - FOUR: Future-proofing; Chapter - 14: Protecting and defending your organisation; Chapter - 15: The ethical digital organisation; Chapter - 16: Upskilling and bringing in new talent; Chapter - 17: Defining the next horizon

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Book SynopsisJordan Morrow is known as the "Godfather of Data Literacy", having helped pioneer the field by building one of the world's first data literacy programs. He is the founder and CEO of Bodhi Data, served as the Chair of the Advisory Board for The Data Literacy Project and has helped companies and organizations around the world, including the United Nations, build and understand data literacy. Morrow is the author of three books: Be Data Literate, Be Data Driven, and Be Data Analytical, all published by Kogan Page. He is based near Salt Lake City, Utah.Trade Review"Jordan Morrow has done it again. This flows on perfectly from his first book, Be Data Literate, and creates a realistic and achievable pathway for organizations to become data driven. And, of course, at the center of it is people-a man after my own data heart! I hope that wasn't a spoiler alert..." * Susan Walsh, The Classification Guru and author of Between the Spreadsheets *"Understanding the basics of data science and AI is key to succeeding today. This book provides key insights into preparing better for a more data-driven future. A fascinating read for anyone looking to stay on top of how data science is revolutionizing just about everything. Jordan Morrow has done an amazing job taking a complex topic and synthesizing it for all to understand. Highly recommended." * Manuj Aggarwal, Founder and Chief Innovation Officer, TetraNoodle *"Jordan Morrow delivers a great follow-up to his first book, Be Data Literate. The phrase 'data driven' has evolved into a meaningless term and hollow buzzword through misuse and misunderstanding. With his new book, Jordan helps us understand what it actually means and why it matters. He then offers a simple, practical approach to help companies get there. For organizations and leaders who know there is value in their data but have struggled to unleash it, this is the book for you." * Brian Ferris, Chief Data, Analytics and Technology Officer, Loyalty NZ *"One of the pioneers in the field of data." * Jimmy Rex, investor, author and podcast host of The Jimmy Rex Show *"Jordan Morrow is a true authority on data literacy and the opportunity for organizations that want to become data driven. In this book, he covers all the critical ingredients organizations need to become truly data driven in a world that's been transformed by the Covid-19 pandemic. His second book is a great guide for leaders and practitioners alike and provides the necessary tools for transforming your business into a data-driven powerhouse." * Eva Murray, Lead Evangelist EMEA, Snowflake *"Jordan Morrow has followed his brilliant first book, Be Data Literate, with this masterpiece. It's a comprehensive and insightful book in which he shares his passion and experience in the data space to give everyone the power to harness the true power of data." * Bernard Marr, best-selling author, futurist, business and technology adviser *"This book aims high, with strong data culture principles and data literacy foundations, while maintaining significant focus on the strategy that guides data initiatives to successful completion and mission success. Failure is not an option in this data-intensive world. Be Data Driven is an excellent launchpad and mission guidebook for your organization's data-driven journey." * Kirk Borne, Chief Science Officer, DataPrime *"Jordan Morrow writes brilliantly, engages with the reader and, most crucially, demystifies the world of data and analytics. It all makes sense, even for those not familiar with the subject or struggling to understand. This book is a terrific start and for those passionate about business and success, essential!" * Mike Roe, CEO, Tensense.ai *Table of Contents Section - ONE: Foundational; Chapter - 01: A data-driven world; Chapter - 02: The impact of Covid-19 on organizations and data; Chapter - 03: Technologies advancing data and analytics, and the need for the human element; Chapter - 04: What is a data-driven organization?; Section - TWO: Gaps; Chapter - 05: Foundational skills gaps; Chapter - 06: Pillars of an organizational data strategy; Chapter - 07: The gap in leadership; Chapter - 08: The biggest hurdle: culture; Section - THREE: Building your data-driven organization; Chapter - 09: Decide your outcome; Chapter - 10: Build your strategy; Chapter - 11: Be data driven—start your journey!; Chapter - 12: References

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

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Book SynopsisMike Grigsby, based in Orlando, Florida, has more than 30 years' experience in the field of marketing analytics. He was formerly vice president of customer insights and advanced analytics at Brierley and Partners and of strategic business analysis and advanced analytics at Targetbase and has also held leadership positions at Hewlett-Packard and Gap. Previously an adjunct professor at the University of Texas at Dallas, he taught analytics at both graduate and undergraduate levels. He is the author of Advanced Customer Analytics, also published by Kogan Page.Trade Review"In Marketing Analytics, Mike Grigsby takes passionate marketing strategists on a practical, real-life journey for solving common marketing challenges. By combining the concepts and knowledge areas of statistics, marketing strategy and consumer behaviour, Grigsby recommends scientific and innovative solutions to common marketing problems in the current business environment. I highly recommend reading this book as it adds a completely new dimension to marketing science." * Kristina Domazetoska, Project Manager and Implementation Consultant at Insala – Talent Development and Mentoring Solutions *"Grigsby's book is the right blend of theory applied to the real-world large-scale data problems of marketing. It's exactly the book I wish I'd had when I started out in this field." * Jeff Weiner, Senior Director, Analytics, One10 *Table of Contents Section - 00: Introduction; Section - PART ONE: How can marketing analytics help you?; Chapter - 01: Overview of statistics; Chapter - 02: Consumer behaviour and marketing strategy; Chapter - 03: What is an insight?; Section - PART TWO: Dependent variable techniques; Chapter - 04: Modelling demand and elasticity; Chapter - 05: Polynomial distributed lags; Chapter - 06: Using Poisson regression; Chapter - 07: Logistic regression and market basket analysis; Chapter - 08: Survival modelling and lifetime value; Chapter - 09: Panel regression and same store sales; Chapter - 10: Introduction to forecasting; Section - PART THREE: Interrelationship techniques; Chapter - 11: Simultaneous equations; Chapter - 12: Principal components and factor analysis; Chapter - 13: Segmentation overview; Chapter - 14: Tools of segmentation; Section - PART FOUR: Focus on media and loyalty; Chapter - 15: Modelling marcom value; Chapter - 16: Media mix modelling; Chapter - 17: Overview of loyalty; Chapter - 18: Loyalty with SEM; Chapter - 19: The customer loyalty journey; Section - PART FIVE: More important topics for everyday marketing; Chapter - 20: Statistical testing; Chapter - 21: Introduction to Big Data; Chapter - 22: Conclusion - The finale; Chapter - 23: References; Chapter - 24: Further reading;

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

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Book SynopsisNeetan Chopra is a C-suite level senior digital leader with a track record of driving change and innovation at established enterprises. In his three-decade career, he has led digital transformation in global enterprises operating diverse business models across the aviation, travel, logistics, retail, food and beverage, real estate and entertainment industry sectors. He is currently Chief Digital and Information Officer at IndiGo (InterGlobe Aviation Ltd) in Gurugram, India; prior to this, he also held roles at Dubai Holding, Emirates Airlines and Accenture. He sits on the boards of multiple tech start-ups, has built multiple global innovation labs including at Oxford University and Carnegie Mellon University and is an Adjunct Professor at Botho University, Botswana. He is recognized by the Constellation Research Business Transformation 150 as one of the world's top global executives leading innovative business transformation efforts in their organizations.Trade Review"An insightful, practitioners' guide full of pragmatic ideas and valuable learnings from decades of experience. A must-read for executives vying for industry leadership in digital." * Joydeep Sengupta, Senior Partner, McKinsey & Company *"More than 80% of digital transformation projects fail inside large organizations not through lack of trying. Every organization looking to crack the code for success must read Neetan Chopra's book. By taking a practitioner's point of view, he draws from three decades of experience to provide a proven methodology known as the Honeycomb Framework. Accelerated Digital Transformation is a must-read book for digital leaders." * R ‘Ray’ Wang, Founder and Chairman, Constellation Research *"We live in a time where the fog of technology is often overwhelming, difficult to dissect and changing so rapidly it becomes difficult to have confidence in our decision making. Neetan Chopra takes the mystery out of creating successful digital transformations for companies and their clients in a way that brings light and clarity to an otherwise challenging process. The insights he has gathered through his years of technology experience provide the keys for those who are looking to unlock value during these extraordinary times." * Tim Kobe, Founder and CEO, Eight Inc. *"Every enterprise must succeed on a digital journey to remain relevant, serve customers and add value. Accelerated Digital Transformation provides you with a pragmatic guide and framework to achieve progress and success. I appreciate Neetan Chopra's storytelling approach and many lessons of everyday role models delivering extraordinary customer service. These stories demonstrate the Uplifting Service ethos that earns customer praise and social media attention." * Ron Kaufman, New York Times Bestselling Author, Uplifting Service: The Proven Path to Delighting Your Customers, Colleagues, and Everyone Else You Meet *"Only if one has lived through or, even better, driven digital transformation of a major company, can one seriously understand what it means to write a book about it that is worth reading. Neetan Chopra is exactly one of these few leaders who has lived through such digital transformations and knows exactly what it means, what works and what doesn't. Chopra's book is probably one of the very few books about digital transformation really worth reading, as it is not yet another consultant textbook, far remote from reality, but written by true experience and painful learnings." * Patrick Naef, Managing Partner, Boyden *"Accelerated Digital Transformation deftly fuses practical examples and actionable insights from Neetan Chopra's vast digital experience, with deep foundational learning, borne out of his global academic pursuits. Chopra's storytelling approach, coupled with innovative interventions, such as co-authoring a chapter with an AI-bot, make the book both engaging and first-of-a-kind read. We highly recommend this book, for anyone looking to navigate the ever-evolving digital landscape and staying ahead of the game in the digital era." * Prof Dr Reinhard Jung, Dean, School of Management, University of St Gallen & Prof Dr Ulrike Baumöl, Executive Director, Executive MBA HSG in Business Engineering, University of St Gallen *"Having seen firsthand how Neetan Chopra executes successful digital transformation where others have struggled, I am grateful he has taken the time to write down his framework. His deliberate 'cell by cell' strategy is what it takes to assess, learn and succeed in this complicated mix of technical, business and cultural transformation. And a bonus, he demonstrates learning with this framework by co-authoring one chapter with an AI bot... brilliant!" * Jana Eggers, CEO, Nara Logics *"All transformations, digital or otherwise, are in essence a human journey of change. I like Chopra's storytelling, a colloquial approach to the intimidating topic of digital transformation for business leaders. This will connect with people across all walks of life, demystifying what it takes to thrive in the digital era and create exponential outcomes. The six global experts, the honeycomb archetypes, provide an additional human touch, sharing their experiences and wisdom, using Chopra's Accelerated Digital Transformation as a platform. A must read." * Raj Jain, Former CEO, Bennett Coleman & Co Limited (Times Group) *"Organizations need simple frameworks by which they can plan their digital transitions. Neetan Chopra's efforts on achieving this through a well thought out 'Honeycomb Framework' is commendable. His building blocks of digital transformation provide a succinct and clear framework through which readers can apply these in the context of their organizations. The future is about organizations which will survive and thrive in a digitally driven business ecosystem. Chopra's book will help organizations survive and thrive in a VUCA world." * Krishnakumar Natarajan, Co-founder & Former Executive Chairperson, LTIMindtree *"Chopra's new book helps you reduce bad friction into a manageable hexagon comb while introducing good friction to sweeten the honey from digital transformation." * Soon Yu, Author, Friction: Adding Value by Making People Work For It *Table of Contents Chapter - 01: Breaking organizational inertia; Chapter - 02: Working the Honeycomb – disrupt phase; Chapter - 03: Working the Honeycomb – digital capabilities; Chapter - 04: Accelerated possibilities; Chapter - 05: Honeycomb as a platform; Chapter - 06: The Honeycomb hacks; Chapter - 07: Reflections;

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Book SynopsisJordan Morrow is known as the "Godfather of Data Literacy", having helped pioneer the field by building one of the world's first data literacy programs. He is the founder and CEO of Bodhi Data, served as the Chair of the Advisory Board for The Data Literacy Project and has helped companies and organizations around the world, including the United Nations, build and understand data literacy. Morrow is the author of three books: Be Data Literate, Be Data Driven, and Be Data Analytical, all published by Kogan Page. He is based near Salt Lake City, Utah.Trade Review"A must-read for anyone looking to harness the power of data. Be Data Analytical stands out as a comprehensive guide that empowers readers to unlock the hidden potential within their data, driving innovation and growth in any field." * Bernard Marr, Founder & CEO, Bernard Marr & Co *"If you're looking for a practical guide to learn about the four levels of analytics, look no further than Be Data Analytical. Jordan Morrow's hands-on approach to teaching data analytics makes the book an invaluable resource for anyone who wants to learn the skills needed to succeed in the field. The clear explanations, practical examples and content breakdown make this book an excellent choice for both beginners and experienced professionals." * Chandra Donelson, Washington D.C. Chapter Lead, Women in Data *"Jordan's passion and enthusiasm for data shines through. Breaking down analytics into four accessible levels means this book is for everyone. Its real-life examples and analogies bring to life the importance of understanding and implementing good analytics." * Susan Walsh, Founder & Managing Director, The Classification Guru Ltd *"This book provides an excellent framework for data-driven decision-making in organizations. By framing the analytics implementations progressively through the four levels of analytics, Be Data Analytical is easy to follow as an analytics guidebook. At each stage, the book covers key definitions, roles of the different enterprise players, numerous business examples and strategy suggestions to get the analytics job done." * Kirk Borne, Founder, Data Leadership Group *"Data Analytics is a crucial aspect of decision-making in the modern business landscape, and this book provides a comprehensive guide to understanding its nuances. The author's expertise and passion for the subject is present in every chapter, making this book a must-read for anyone seeking to improve their data literacy and enhance their decision-making skills. I highly recommend this book to anyone looking to unlock the power of data analytics in their organization." * Esther Munyi, Chief Data and Analytics Officer, Sasfin *"Be Data Analytical is a book about leadership, decision making, staying ahead and having your own built-in systems. A consummate storyteller, Jordan speaks to those who know this space and those who perhaps need to. The data environment has changed forever and the complexity and challenge for leaders means the rule book we used to follow, and our previous frames of reference, are redundant. New ways of thinking and improving decision making are therefore vital." * Mike Roe, CEO, Tensense.ai *"Ingenious! Jordan's engaging work propels the reader from data literacy to data analysis." * Major General Dustin Shultz *Table of Contents Chapter - 00: Introduction; Section - ONE: Data and analytics; Chapter - 01: Defining data and analytics; Chapter - 02: Defining the four levels of analytics; Chapter - 03: The power of analytics in decision making; Section - TWO: The four levels of analytics - define, empower, understand and learn; Chapter - 04: Descriptive analytics; Chapter - 05: How are descriptive analytics used today?; Chapter - 06: How individuals and organizations can improve in descriptive analytics; Chapter - 07: Diagnostic analytics; Chapter - 08: How are diagnostic analytics used today?; Chapter - 09: How individuals and organizations can improve in diagnostic analytics; Chapter - 10: Predictive analytics; Chapter - 11: How are predictive analytics used today?; Chapter - 12: How individuals and organizations can improve in predictive analytics; Chapter - 13: Prescriptive analytics; Chapter - 14: How are prescriptive analytics used today?; Chapter - 15: How individuals and organizations can improve in prescriptive analytics; Section - Three: Bringing it all together; Chapter - 16: Using all four levels of analytics to empower decision making; Chapter - 17: Conclusion;

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Book SynopsisThomas C Redman is known as the 'Data Doc' and is the founder and President of Data Quality Solutions. Through this company he helps people and organizations think about data and data quality in new and exciting ways. Based in Rumson, New Jersey, he previously worked at AT&T where he formed their data quality lab.Trade Review"Based on my own decades-long experience working with organizations on removing data-related barriers and building data-driven strategies, I could not agree more with Tom's guidance to put people at the center. He's on to something massively important here - if you internalize and act on the people-focused principles he's suggesting, you will no doubt accelerate and amplify your impact on the business." * Ted Friedman, Former Gartner Analyst and Industry Thought-Leader *"For any organisation to succeed in the 21st Century, it needs people and data. Moreover, as Tom Redman explains in People and Data, when these two elements unite and when the benefits of data are extended to everyone in the organisation you can transform your business. Whether you are a leader, manager or worker or whether you work in HR, Finance, Operations or Marketing, I heartily recommend reading People and Data." * David Green, Co-author of Excellence in People Analytics, Managing Partner at Insight222, and host of the Digital HR Leaders podcast. *"People and Data offers great insights and advice on how organizations can unleash "real people" , working together, to solve the data quality problem, once and for all. We could all benefit from Tom's optimism and experience." * Maria Villar, Head of Enterprise Data Strategy & Transformation, SAP North America *"People and Data is the provocation that many data managers and executives need to spur them into action. Today's world is driven by data, but as Tom reminds us, if the data is poor (garbage in), then the outcomes are as well (garbage out). Tom's solution for maintaining quality data lies not in technology but in people - how they are organized, what tasks they are given, what culture they create, how they are motivated, promoted and trained. Tom explores all aspects of building a world-class data organization." * Theresa Kushner, former Head of Innovation Center, NTT Data, North America *"Tom Redman has a knack for taking the complex world of data and making it simple to understand and improve. What businesses have struggled with for decades is delivered in this next generational approach: data is predominantly a people issue and must be considered a team sport. In this new book, Redman brings solutions that highlight how regular people working together within and across organizations, under the direction of senior leaders, can finally solve this very expensive and seemingly endless enigma and no longer need to stand on the data sidelines." * Bob Palermo, former Vice President, Performance Excellence, Shell *"The book, People and Data: Uniting to transform your business is an exceptional resource by one of the world's leading thinkers and practitioners on 'data' - Dr. Thomas Redman. Every professional, with "data" in their title or not, should read Tom's book and learn from his experiences developed over 25+ years as the 'Data Doc'." * Anne Marie Smith, Ph.D., Alabama Yankee Systems, LLC *"Nothing of quality happens without quality people, fueled with quality data, making quality decisions. Here Redman establishes the case, roadmap and tools for dramatic business growth through quality data. Lead People, Manage Assets goes the adage. Tom shows us sensible ways to Lead regular people in uniting to successfully manage the unique assets of data, and information technology. Fortune Favors The Brave: so read People and Data - then lead bravely for sustained business growth." * Robert Pautke, Founder & Leadership Coach, SOAR with Purpose, LLC *Table of Contents Chapter - 00: Introduction; Section - ONE: The big picture; Chapter - 01: Ann’s data Tuesday; Chapter - 02: The opportunity and the problem; Chapter - 03: Building a better organization for data; Section - TWO: People; Chapter - 04: The data generation and provocateurs; Chapter - 05: All roads lead through quality; Chapter - 06: Putting data to work; Section - THREE: Data is a team sport; Chapter - 07: Fat organizational pipes; Chapter - 08: Don’t confuse apples and oranges; Chapter - 09: Dream big, but change the culture one project at a time; Section - 10: The data teams companies need now; Section - 11: Conclusion - Courage required; Section - 12: Resource Centre 1 - Toolkit; Section - 13: Resource Centre 2 - Curriculum for training regular people;

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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 SynopsisBowei Chen is Associate Professor of Marketing Analytics and Data Science at the Adam Smith Business School, University of Glasgow, UK. He is also the Programme Director of the MSc in Finance and Management and an ESRC IAA Reviewer. Gerhard Kling is Professor in Finance at the University of Aberdeen, UK. He has worked in higher education for over 18 years (SOAS, University of Southampton, UWE, Utrecht University).

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Book SynopsisNicola Askham, known as "The Data Governance Coach", has over two decades of experience in helping organizations understand and manage their data better through simple and custom-designed data governance programmes. She runs training courses on all elements of data governance and provides consultancy for numerous businesses, non-profits and government agencies. She is the host of The Data Governance Podcast and was previously a board member and director of DAMA UK. She lives in London, UK.

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

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Book SynopsisHow data-informed decision making can make colleges and universities more effective institutions. The continuing importance of data analytics is not lost on higher education leaders, who face a multitude of challenges, including increasing operating costs, dwindling state support, limits to tuition increases, and increased competition from the for-profit sector. To navigate these challenges, savvy leaders must leverage data to make sound decisions. In Big Data on Campus, leading data analytics experts and higher ed leaders show the role that analytics can play in the better administration of colleges and universities. Aimed at senior administrative leaders, practitioners of institutional research, technology professionals, and graduate students in higher education, the book opens with a conceptual discussion of the roles that data analytics can play in higher education administration. Subsequent chapters address recent developments in technology, the rapid accumulation of data assetsTable of ContentsForeword, by Christine M. KellerAcknowledgments Part I. Technology, Digitization, Big Data, and Analytics Maturity as the Enabling Conditions for Data-Informed Decision MakingChapter 1. Data Analytics and the Imperatives for Data-Informed Decision Making in Higher Education Karen L. Webber and Henry Y. ZhengChapter 2. Big Data and the Transformation of Decision Making in Higher Education Braden J. HoschChapter 3. Predictive Analytics and Its Uses in Higher Education Henry Y. Zheng and Ying ZhouPart II. The Ethical, Cultural, and Managerial Imperatives of Data-Informed Decision Making in Higher EducationChapter 4. Limitations in Data Analytics: Potential Misuse and Misunderstanding in Data Reports and Visualizations Karen L. Webber and Jillian N. MornChapter 5. Guiding Your Organization's Data Strategy: The Roles of University Senior Leaders and Trustees in Strategic Analytics Gail B. Marsh and Rachit TharianiChapter 6. Data Governance, Data Stewardship, and the Building of an AnalyticsOrganizational Culture Rana Glasgal and Valentina NestorPart III. The Application of Analytics in Higher Education Decision Making: Case StudiesChapter 7. Data Analytics and Decision Making in Admissions and Enrollment Management Tom Gutman and Brian P. HinoteChapter 8. Predictive Analytics, Academic Advising, Early Alerts, and Student Success Timothy M. RenickChapter 9. Constituent Relationship Management and Student Engagement Lifecycle Cathy A. O'Bryan, Chris Tompkins, and Carrie Hancock MarcinkevageChapter 10. Learning Analytics for Learning Assessment: Complexities in Efficacy, Implementation, and Broad Use Carrie Klein, Jaime Lester, Huzefa Rangwala, and Aditya JohriChapter 11. Using Data Analytics to Support Institutional Financial and Operational Efficiency Lindsay K. Wayt, Susan M. Menditto, J. Michael Gower, and Charles TegenPart IV. Concluding CommentsChapter 12. Data-Informed Decision Making and the Pursuit of Analytics Maturity in Higher Education Karen L. Webber and Henry Y. ZhengContributorsIndex

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Book SynopsisThoroughly documents an innovative approach to data collection in developing countries, which combines baseline data from a household survey with subsequent interviews of selected respondents using mobile phones.

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Book SynopsisInformation theory deals with mathematical laws that govern the flow, representation and transmission of information. The most significant achievement of the field is the invention of digital communication which forms the basis of our daily-life digital products such as smart phones, laptops and any IoT devices. Recently it has also found important roles in a spotlight field that has been revolutionized during the past decades: data science.This book aims at demonstrating modern roles of information theory in a widening array of data science applications. The first and second parts of the book covers the core concepts of information theory: basic concepts on several key notions; and celebrated source and channel coding theorems which concern the fundamental limits of communication. The last part focuses on applications that arise in data science, including social networks, ranking, and machine learning.The book is written as a text for senior undergraduate and graduate students working on Information Theory and Communications, and it should also prove to be a valuable reference for professionals and engineers from these fields.Table of Contents 1 Source Coding 1.1 Overview of the book 1.2 Entropy and Python exercise 1.3 Mutual information, Kullback-Leibler (KL) divergence and Python exercise Problem Set 1 1.4 Source coding theorem for i.i.d. sources (1/3) 1.5 Source coding theorem for i.i.d. sources (2/3) 1.6 Source coding theorem for i.i.d. sources (3/3)Problem Set 2 1.7 Source code design 1.8 Source coding theorem for general sources 1.9 Huffman code and Python implementation Problem Set 3 2 Channel Coding 2.1 Statement of channel coding theorem 2.2 Achievability proof for the binary erasure channel 2.3 Achievability proof for the binary symmetric channelProblem Set 4 2.4 Achievability proof for discrete memoryless channels 2.5 Converse proof for discrete memoryless channels 2.6 Source-channel separation theorem and feedback Problem Set 5 2.7 Polar code: Polarization 2.8 Polar code: Implementation of polarization 2.9 Polar code: Proof of polarization and Python simulation Problem Set 6 3 Data Science Applications 3.1 Social networks: Fundamental limits 3.2 Social networks: Achievability proof 3.3 Social networks: Converse proof 3.4 Social networks: Algorithm and Python implementationProblem Set 7 3.5 DNA sequencing: Fundamental limits 3.6 DNA sequencing: Achievability proof 3.7 DNA sequencing: Converse proof 3.8 DNA sequencing: Algorithm and Python implementationProblem Set 8 3.9 Top-K ranking: Fundamental limits 3.10 Top-K ranking: Algorithm 3.11 Top-K ranking: Python implementation Problem Set 9 3.12 Supervised learning: Connection with information theory 3.13 Supervised learning: Logistic regression and cross entropy 3.14 Supervised learning: TensorFlow implementation Problem Set 10 3.15 Unsupervised learning: Generative modeling 3.16 Generative Adversarial Networks (GANs) and KL divergence 3.17 GANs: TensorFlow implementation Problem Set 11 3.18 Fair machine learning and mutual information (1/2) 3.19 Fair machine learning and mutual information (2/2) 3.20 Fair machine learning: TensorFlow implementation Problem Set 12 Appendices A – Python Basics B – TensorFlow and Keras Basics C – Note on Research

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Book SynopsisThe general theme of this book is to encourage the use of relevant methodology in data mining which is or could be applied to the interplay of education, statistics and computer science to solve psychometric issues and challenges in the new generation of assessments. In addition to item response data, other data collected in the process of assessment and learning will be utilized to help solve psychometric challenges and facilitate learning and other educational applications. Process data include those collected or available for collection during the process of assessment and instructional phase such as responding sequence data, log files, the use of help features, the content of web searches, etc. Some book chapters present the general exploration of process data in large -scale assessment. Further, other chapters also address how to integrate psychometrics and learning analytics in assessment and survey, how to use data mining techniques for security and cheating detection, how to use more assessment results to facilitate student’s learning and guide teacher’s instructional efforts. The book includes both theoretical and methodological presentations that might guide the future in this area, as well as illustrations of efforts to implement big data analytics that might be instructive to those in the field of learning and psychometrics. The context of the effort is diverse, including K-12, higher education, financial planning, and survey utilization. It is hoped that readers can learn from different disciplines, especially those who are specialized in assessment, would be critical to expand the ideas of what we can do with data analytics for informing assessment practices.Table of Contents On Integrating Psychometrics and Learning Analytics in Complex Assessments, Robert J. Mislevy. Exploring Process Data in Problem-Solving Items in Computer-Based Large-Scale Assessments: Case Studies in PISA and PIAAC, Qiwei He, Matthias von Davier, and Zhuangzhuang Han. The Use of Data Mining Techniques to Detect Cheating, Sarah L. Thomas and Dennis D. Maynes. Selected Applications of Data Science in Cyber Security, Yue (Richard) Xie. Assessing Learner -Driven Constructs in Informal Learning Environments: Synergies Created by the Nexus of Psychometrics, Learning Analytics, and Educational Data Mining, Lori C. Bland. Measuring Rater Effectiveness: New Uses of Value-Added Modeling in Competency-Based Education, B. Brian Kuhlman. Ranking Documents in Online Enterprise Social Network, Alex H. Wang and Umeshwar Dayal. Methods for Measuring Learning Evaluation in the Context of E-Learning, Matthew Pietrowski, Roopa Sanwardeker, and David Witkowski. High Level Strategic Approaches for Conducting Big Data Studies in Assessment, Manfred M. Straehle, Liberty J. Munson, Austin Fossey, and Emily Kim. Integrating Survey and Learning Analytics Data for a Better Understanding of Engagement in MOOCs, Evgenia Samoilova, Florian Keusch, and Frauke Kreuter.

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Book SynopsisThe general theme of this book is to encourage the use of relevant methodology in data mining which is or could be applied to the interplay of education, statistics and computer science to solve psychometric issues and challenges in the new generation of assessments. In addition to item response data, other data collected in the process of assessment and learning will be utilized to help solve psychometric challenges and facilitate learning and other educational applications. Process data include those collected or available for collection during the process of assessment and instructional phase such as responding sequence data, log files, the use of help features, the content of web searches, etc. Some book chapters present the general exploration of process data in large -scale assessment. Further, other chapters also address how to integrate psychometrics and learning analytics in assessment and survey, how to use data mining techniques for security and cheating detection, how to use more assessment results to facilitate student’s learning and guide teacher’s instructional efforts. The book includes both theoretical and methodological presentations that might guide the future in this area, as well as illustrations of efforts to implement big data analytics that might be instructive to those in the field of learning and psychometrics. The context of the effort is diverse, including K-12, higher education, financial planning, and survey utilization. It is hoped that readers can learn from different disciplines, especially those who are specialized in assessment, would be critical to expand the ideas of what we can do with data analytics for informing assessment practices.Table of Contents On Integrating Psychometrics and Learning Analytics in Complex Assessments, Robert J. Mislevy. Exploring Process Data in Problem-Solving Items in Computer-Based Large-Scale Assessments: Case Studies in PISA and PIAAC, Qiwei He, Matthias von Davier, and Zhuangzhuang Han. The Use of Data Mining Techniques to Detect Cheating, Sarah L. Thomas and Dennis D. Maynes. Selected Applications of Data Science in Cyber Security, Yue (Richard) Xie. Assessing Learner -Driven Constructs in Informal Learning Environments: Synergies Created by the Nexus of Psychometrics, Learning Analytics, and Educational Data Mining, Lori C. Bland. Measuring Rater Effectiveness: New Uses of Value-Added Modeling in Competency-Based Education, B. Brian Kuhlman. Ranking Documents in Online Enterprise Social Network, Alex H. Wang and Umeshwar Dayal. Methods for Measuring Learning Evaluation in the Context of E-Learning, Matthew Pietrowski, Roopa Sanwardeker, and David Witkowski. High Level Strategic Approaches for Conducting Big Data Studies in Assessment, Manfred M. Straehle, Liberty J. Munson, Austin Fossey, and Emily Kim. Integrating Survey and Learning Analytics Data for a Better Understanding of Engagement in MOOCs, Evgenia Samoilova, Florian Keusch, and Frauke Kreuter.

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Book SynopsisVisual displays play a crucial role in knowledge generation and communication. The purpose of the volume is to provide researchers with a framework that helps them use visual displays to organize and interpret data; and to communicate their findings in a comprehensible way within different research (e.g., quantitative, mixed methods) and testing traditions that improves the presentation and understanding of findings. Further, this book includes contributions from leading scholars in testing and quantitative, qualitative, and mixed methods research, and results reporting. The volume’s focal question is: What are the best principles and practices for the use of visual displays in the research and testing process, which broadly includes the analysis, organization, interpretation, and communication of data?The volume is organized into four sections. Section I provides a rationale for this volume; namely, that including visual displays in research and testing can enhance comprehension and processing efficiency. Section II includes addresses theoretical frameworks and universal design principles for visual displays. Section III examines the use of visual displays in quantitative, qualitative, and mixed methods research. Section IV focuses on using visual displays to report testing and assessment data.

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Book SynopsisVisual displays play a crucial role in knowledge generation and communication. The purpose of the volume is to provide researchers with a framework that helps them use visual displays to organize and interpret data; and to communicate their findings in a comprehensible way within different research (e.g., quantitative, mixed methods) and testing traditions that improves the presentation and understanding of findings. Further, this book includes contributions from leading scholars in testing and quantitative, qualitative, and mixed methods research, and results reporting. The volume’s focal question is: What are the best principles and practices for the use of visual displays in the research and testing process, which broadly includes the analysis, organization, interpretation, and communication of data?The volume is organized into four sections. Section I provides a rationale for this volume; namely, that including visual displays in research and testing can enhance comprehension and processing efficiency. Section II includes addresses theoretical frameworks and universal design principles for visual displays. Section III examines the use of visual displays in quantitative, qualitative, and mixed methods research. Section IV focuses on using visual displays to report testing and assessment data.

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Book SynopsisThis text provides an overview of the principles and practices involved in managing and operating data centers. It covers topics such as data center design, infrastructure management, virtualization, cloud computing, and security. The book is intended for IT professionals and data center managers who are responsible for the operation and maintenance of data centers. It provides valuable insights and best practices for optimizing data center performance, reliability, and efficiency.Table of Contents Chapter 1 Introduction to Data Center Management Chapter 2 Data Center Topologies and Network Architecture Chapter 3 Security and Compliance in Data Protection Chapter 4 Monitoring and Management Tools Chapter 5 Virtualization and Cloud Computing Chapter 6 Importance of Power and Cooling Management Chapter 7 Challenges in Data Center Management Chapter 8 Future Trends in Data Center Management

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Book SynopsisQualitative Comparative Analysis (QCA) is an emerging research method that is highly suitable for evaluation studies. Clear and concise, this book explains how researchers and evaluators can use QCA effectively for the systematic and thorough analysis of large infrastructure projects, while also acknowledging their complexity.Lasse Gerrits and Stefan Verweij present the key steps of this methodology to identify patterns across real-life cases. From collecting and interpreting data to sharing their knowledge and presenting the results, the authors use examples of megaprojects to emphasize how QCA can be used successfully for both single infrastructure ventures as well as more extensive projects. In addition to discussing the best practices and pitfalls of the methodology, further examples from current research are given in order to illustrate how QCA works effectively in both theory and practice.Being written with researchers and evaluators in mind, this book will be of great benefit for students and scholars of evaluation studies, public administration, transport studies, policy analysis and project management. The book is also highly applicable for those working in public or private organizations involved in infrastructure projects looking for an effective, detailed and systematic method of evaluation.Trade Review'Disentangling within-case complexity is a challenging task; even more so if one examines multiple cases. Gerrits and Verweij brilliantly demonstrate, using the latest conceptual and technical innovations, and through the concrete example of infrastructure projects, that QCA can produce qualitative leaps in taking on this challenge. This book is a must-read for researchers, evaluators and practitioners who take both complexity and comparison seriously.' --Benoit Rihoux, Universite catholique de Louvain, BelgiumTable of ContentsContents: 1. Not a Straightforward Path: Developing and Evaluating Infrastructure Projects 2. The Case 3. Calibration 4. Comparison 5. Complexity and Evaluation Revisited References Index

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Book SynopsisThis book asks the important question; Can the by-products of research activity be treated as data and of research interest in themselves? This groundbreaking interdisciplinary volume considers the analytic value of a range of 'by-products' of social research and reading. These include electronically captured paradata on survey administration, notes written in the margins of research documents and literary texts, and fieldnotes and ephemera produced by social researchers. Revealing the relational nature of paradata, marginalia and fieldnotes, contributions examine how the craft of studying and analyzing these by-products offers insight into the intellectual, social and ethical processes underpinning the activities of research and reading. Unique and engaging, this book is a must read for social researchers and sociologists, narrative analysts, literary scholars and historians. Bridging methodological boundaries, it will also prove of great value to quantitative and qualitative methodologists alike.Contributors include: K. Bell, J. Boddy, R.G. Burgess, G.B. Durrant, R. Edwards, H. Elliott, E. Fahmy, J. Goodwin, H.J. Jackson, D. Kilburn, O. Maslovskaya, H. O'Connor, A. Phoenix, W.H. ShermanTrade Review'This is an extremely important book that brings to the attention of social researchers and methodologists the fascinating potential and intrinsic interest of three kinds of by-product of the research process - field notes, paradata, and marginalia. Many of us are unfamiliar with all or some of these sources. The book is full of worked examples of their use which greatly enhances the book's utility for all of us. The editors and authors have done us all a great service in bringing to our attention research sources that can no longer be ignored.' --Alan Bryman, University of Leicester, UK'Paradata will become increasingly important to researchers, both as an insight into the complexity and richness of participants and contexts, but also it has great potential to improve the quality of our research. Ros Edwards and her colleagues have provided us with a wonderfully comprehensive set of essays that are both insightful and valuable. This is a book which will have great appeal to students and professional researchers from both the quantitative and qualitative traditions.' --Malcolm Williams, Cardiff University, UK'Taking an expansive and inclusive approach to its topic, Working with Paradata, Marginalia and Fieldnotes offers a stimulating tour of a neglected domain of methodology. Readers who customarily regard paradata as a ''dry and dull'' element of data archiving will be delighted to read of the hidden corners of the research enterprise that this book's understanding of paradata and marginalia illuminates. Launching what is effectively a new field of inquiry, the book shows how these materials contribute to the field's renewed process of self-discovery.' --Nigel Fielding, University of Surrey, UKTable of ContentsContents: Marginalia - A Poem by Billy Collins Preface Robert G. Burgess 1. Introduction: Working with Paradata, Marginalia and Fieldnotes John Goodwin, Henrietta O’Connor, Ann Phoenix and Rosalind Edwards 2. Paradata for Non-response Investigations in Social Surveys Gabrielle B. Durrant and Olga Maslovskaya 3. Using Paradata to Evaluate Survey Quality: Behaviour Coding the 2012 PSE UK Survey Eldin Fahmy and Karen Bell 4. ‘Another Long and Involved Story’: Narrative Themes in the Marginalia of the Poverty in the UK Survey Ann Phoenix, Janet Boddy, Rosalind Edwards and Heather Elliott 5 ‘The House Seemed to be Falling Down Around Their Ears’: Contesting and Amplifying Observations of Housing Through Qualitative Survey Paradata Daniel Kilburn 6. The Secondary Analysis of Fieldnotes, Marginalia and Paradata from Past Studies of Young People Henrietta O’Connor and John Goodwin 7. John Adam’s Marginalia: Then and Now H.J. Jackson 8. ‘Soiled by Use’ or ‘Enlivened by Association’? Attitudes Towards Marginalia William H. Sherman 9. Afterword: The Craft of Paradata, Marginalia and Fieldnotes Rosalind Edwards, Ann Phoenix, John Goodwin and Henrietta O’Connor Index

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Book SynopsisOne of the most challenging tasks in the research design process is choosing the most appropriate data collection and analysis technique. This Handbook provides a detailed introduction to five qualitative data collection and analysis techniques pertinent to exploring entrepreneurial phenomena.Techniques for collecting and analyzing data are rarely addressed in detail in published articles. In addition, the constant development of new tools and refinement of existing ones has meant that researchers often face a confusing range from which to choose. The experienced and expert group of contributors to this book provide detailed, practical accounts of how to conduct research employing focus groups, critical incident technique, repertory grids, metaphors, the constant comparative method and grounded theory. This Handbook will become the starting point for any research project.Scholars new to entrepreneurship and doctoral students as well as established academics keen to extend their research scope will find this book an invaluable and timely resource.Contributors: A.R. Anderson, C. Bjursell, A. Bøllingtoft, E. Chell, E. Díaz de León, C. Dima, S. Drakopoulou Dodd, P. Guild, A. Hagedorn, R.T. Harrison, F.M. Hill, S.L. Jack, R.G. Klapper, A. de Koning, C.M. Leitch, E. McKeever, S. Moult, H. Neergaard, R. Newby, R. Smith, S.M. Smith, G. Soutar, J. WatsonTrade Review'This is a much-needed addition to research methods in entrepreneurship. This book champions valuable practices for studying entrepreneurial phenomena in rigorous ways. Five qualitative interview methods (constant comparative technique, metaphor methodologies, critical incident technique, focus groups and repertory grids) are grounded in prior theory and research, and demonstrated in specific research situations in ways that offer scholars insightful and important approaches to exploring entrepreneurship. This is a ''must buy'' for scholars who want to utilize better and more insightful methods for exploring the ideas, context and praxis of entrepreneurship.' --William B. Gartner, Copenhagen Business School, Denmark and California Lutheran University, US'This book will appeal to all researchers interested in qualitative research within the entrepreneurship field. The editors, Neergaard and Leitch, have put together a great group of experts who provide a fantastic overview on a wide range of known and lesser-known techniques. There is much to be discovered even for the experienced researcher. A great ''how to'' guide and a must-read for all qualitative entrepreneurship researchers, be they novices or experienced researchers.' --Friederike Welter, Institut fur Mittelstandsforschung (IfM) Bonn and University of Siegen, GermanyTable of ContentsContents: Introduction PART 1 AN INTRODUCTION TO THE CONSTANT COMPARATIVE TECHNIQUE Alistair R. Anderson and Sarah L. Jack 1. Using the Constant Comparative Technique to Consider Network Change and Evolution Sarah L. Jack, Alistair R. Anderson, Sarah Drakopoulou Dodd and Susan Moult 2. Using Constant Comparison as a Method of Analysis in Entrepreneurship Research Susan M. Smith and Edward McKeever 3. Grounded Theory Analysis in Entrepreneurship Research Anne Bøllingtoft The Future for the Constant Comparative Technique Alistair R. Anderson and Sarah L. Jack PART II METAPHOR METHODOLOGIES: EXPLORING ENTREPRENEURSHIP RESEARCH, PEDAGOGY AND RESEARCHERS Sarah Drakopoulou Dodd and Alice de Koning 4. Enacting, Experimenting and Exploring Metaphor Methodologies in Entrepreneurship Sarah Drakopoulou Dodd and Alice de Koning 5. Con’text’ualising Images of Enterprise: An Examination of ‘Visual Metaphors’ used to Represent Entrepreneurship in Textbooks Robert Smith 6. Metaphors in Communication of Scholarly Work Cecilia Bjursell Metaphor Methodologies in Entrepreneurship Research Sarah Drakopoulou Dodd and Alice de Koning SECTION III THE CRITICAL INCIDENT TECHNIQUE: AN OVERVIEW Claire M. Leitch 7. Researching the Entrepreneurial Process using the Critical Incident Technique Elizabeth Chell 8. The Efficacy of the Qualitative Variant of the Critical Incident Technique (CIT) in Entrepreneurship Research Claire M. Leitch and Frances M. Hill 9. A Critical Incident Technique Approach to Entrepreneurship Research using Phenomenological Explicative Data Collection Richard T. Harrison Critical Incident Technique: Some Conclusions Claire M. Leitch PART IV PROVENANCE AND USE OF FOCUS GROUPS John Watson and Rick Newby 10. Conducting a Traditional Focus Group John Watson, Rick Newby, Helle Neergaard and Robert Smith 11. Conducting a Focus Group using Group Support System (GSS) Software Geoff Soutar, Rick Newby and John Watson 12. Conducting an On-line Focus Group Rick Newby and John Watson Focus Groups: What have we Learned? John Watson and Rick Newby PART V REPERTORY GRIDS IN ENTREPRENEURSHIP: PRACTICAL EXAMPLES FROM RESEARCH Rita G. Klapper 17. Using Repertory Grid Technique to Explore the Relationship between Business Founders and Support Agents Anja Hagedorn 18. Using Repertory Grid to Assess Intangibles: Uncertainty Reduction for Lean Start-ups in Entrepreneurship Enrique Díaz de León and Paul Guild 19. Repertory Grid Technique: An Ideographic and Nomothetic Approach to Knowledge Carmen Dima 20. Concluding Thoughts on Repertory Grids Rita G. Klapper Index

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Book SynopsisEffectively and ethically leveraging people data to deliver real business value is what sets the best HR leaders and teams apart. Excellence in People Analytics provides business and human resources leaders with everything they need to know about creating value from people analytics. Written by two leading experts in the field, this practical guide outlines how to create sustainable business value with people analytics and develop a data-driven culture in HR. Most importantly, it allows HR professionals and business executives to translate their data into tangible actions to improve business performance, whilst navigating the rapidly evolving world of work. Full of practical tools and advice assembled around the Insight222 Nine Dimensions in People Analytics® model, this book demonstrates how to use people data to increase profits, improve staff retention and workplace productivity as well as develop individual employee experience. Featuring case studies from leading companies including Microsoft, HSBC, Syngenta, Capital One, Novartis, Bosch, Uber, Santander Brasil and American Eagle Outfitters®, Excellence in People Analytics is essential reading for all HR professionals needing to unlock the potential in their people data and gain competitive advantage.Trade Review"In this book, cutting-edge practitioners share insights that you can start putting into action right away."" * Adam Grant, #1 New York Times bestselling author of THINK AGAIN and host of the TED podcast, WorkLife *"Exceptional and the standard for people analytics" * Dave Ulrich, Rensis Likert Professor, School of Business, University of Michigan Partner, The RBL Group *"A superb book with practical case studies applicable to every HR professional and business leader in the use of data analytics towards better decision making."" * Low Peck Kem, Chief Human Resources Officer, Public Service Division, Prime Minister’s Office, Singapore *"Filled with topical case studies that can support any people analytics and HR team in their pursuit of creating enterprise value." * Loren I. Shuster, Chief People Officer & Head of Corporate Affairs, The LEGO Group *"HR is closer to the business than ever, and this book shows how people analytics is a business activity that drives substantial value." * Katarina Berg, Chief Human Resources Officer, Spotify *"There is a need for companies to become more human in our increasingly digital age. I have found, as a CHRO, that analytics provides equal benefit to both employees and the business, and Excellence in People Analytics dovetails these two very well. Using analytics is clearly one of the most valuable tools for becoming more human, enabling personalization and consumerisation of the employee experience." * Leena Nair Chief Human Resources Officer, Unilever *"People analytics provides business executives with another lever to improve their strategy and operations. Jonathan and David have a deep understanding of this topic and its impact on people and performance. Their work with companies across the globe is now captured in this book, providing insight with a collection of terrific case studies and practical advice. It is an outstanding guide for executives wishing to create value using people analytics." * John Boudreau, Professor Emeritus, Marshall School of Business, University of Southern California *"Excellence in People Analytics is a delightful journey of discovery through the field of people analytics with 30 vivid case studies and practical models. Businesses have recognized that workforce data can unleash the potential of talent and create value for the company. Yet people analytics is one of the biggest capability gaps for organizations. This book inspires me and is a great guide to implement people analytics beyond the 'buzz' term." * Rosa Lee, Executive Vice President of Bosch China & Corporate HR, Head of Asia-Pacific *"Excellence in People Analytics will equip HR leaders and practitioners with the structures and use cases they need to keep up with technology and learn new skills. I have little doubt that this book will define HR's contribution to the workplace of the future." * Bernard Marr, Bestselling author of Data Strategy and Data-Driven HR, futurist and strategic advisor *"Brilliantly insightful, yet practically impactful. This is a foundational book in the field of people analytics that emphasizes a business-first approach for elevating human performance. The nine dimensions for success are complemented with powerful cases that will empower any practitioner to apply these concepts." * Michael J. Arena, VP Talent & Development, AWS and author of Adaptive Space *Table of Contents Section - PART ONE: The case for people analytics; Chapter - 00: Introduction; Chapter - 000: The business value of people analytics; Section - PART TWO: Nine Dimensions for Excellence in People Analytics; Chapter - 01: Governance; Chapter - 02: Methodology; Chapter - 03: Stakeholder Management; Chapter - 04: Skills; Chapter - 05: Technology; Chapter - 06: Data; Chapter - 07: Workforce Experiences; Chapter - 08: Business Outcomes; Chapter - 09: Culture; Section - PART THREE: The next steps for people analytics; Chapter - 10: Transforming people analytics; Chapter - 11: Epilogue - the future of people analytics; Chapter - 12: Concluding remarks; Chapter - 13: Glossary; Chapter - 14: Index

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

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Book SynopsisLeading tech companies such as Netflix, Amazon and Uber use data science and machine learning at scale in their core business processes, whereas most traditional companies struggle to expand their machine learning projects beyond a small pilot scope. This book enables organizations to truly embrace the benefits of digital transformation by anchoring data and AI products at the core of their business. It provides executives with the essential tools and concepts to establish a data and AI portfolio strategy as well as the organizational setup and agile processes that are required to deliver machine learning products at scale. Key consideration is given to advancing the data architecture and governance, balancing stakeholder needs and breaking organizational silos through new ways of working. Each chapter includes templates, common pitfalls and global case studies covering industries such as insurance, fashion, consumer goods, finance, manufacturing and automotive. Covering a holistic perspective on strategy, technology, product and company culture, Driving Digital Transformation through Data and AI guides the organizational transformation required to get ahead in the age of AI.Trade Review"After years of progress in AI, might the hype be growing faster than the reality? Are we about to enter an 'AI autumn'? Not if Borek and Prill have anything to say about it! Digital transformation is tough - this book improves your odds." * Thomas C Redman, "the Data Doc", Harvard Business Review Blogger and Author *"Clear and to the point in a language that works for executives. A must-read for any leader." * Holger Kömm, Senior Director Advanced Analytics, Adidas *"A great in-depth introduction to how to add value to companies using digitalization, data and AI." * Patrick Glauner, Professor of Artificial Intelligence, Deggendorf Institute of Technology *"Provides great guidance on how to think of data products instead of projects - which is a key factor in mastering the challenges of digitalization." * Carsten Bange, Founder and CEO, BARC (Business Application Research Centre) *"A must-read for everyone involved into turning digitization and AI into real value for your company. Whether you're in the middle of the process looking for some orientation or just about getting started, this book will provide you with the advice you need!" * Alexander Thamm, CEO and Founder of Alexander Thamm GmbH *Table of Contents Chapter - 01: Introduction to delivering data and AI products; Chapter - 02: Developing the data and AI product strategy and goals; Chapter - 03: Setting up the data and AI product delivery organization; Chapter - 04: Identifying and defining data and AI products; Chapter - 05: Delivering high quality data and AI products; Chapter - 06: Designing the data and AI platform and architecture; Chapter - 07: Driving transformative change with data and AI products; Chapter - 08: The future of data and AI products in your organization

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Book SynopsisLeading tech companies such as Netflix, Amazon and Uber use data science and machine learning at scale in their core business processes, whereas most traditional companies struggle to expand their machine learning projects beyond a small pilot scope. This book enables organizations to truly embrace the benefits of digital transformation by anchoring data and AI products at the core of their business. It provides executives with the essential tools and concepts to establish a data and AI portfolio strategy as well as the organizational setup and agile processes that are required to deliver machine learning products at scale. Key consideration is given to advancing the data architecture and governance, balancing stakeholder needs and breaking organizational silos through new ways of working. Each chapter includes templates, common pitfalls and global case studies covering industries such as insurance, fashion, consumer goods, finance, manufacturing and automotive. Covering a holistic perspective on strategy, technology, product and company culture, Driving Digital Transformation through Data and AI guides the organizational transformation required to get ahead in the age of AI.Trade Review"After years of progress in AI, might the hype be growing faster than the reality? Are we about to enter an 'AI autumn'? Not if Borek and Prill have anything to say about it! Digital transformation is tough - this book improves your odds." * Thomas C Redman, "the Data Doc", Harvard Business Review Blogger and Author *"Clear and to the point in a language that works for executives. A must-read for any leader." * Holger Kömm, Senior Director Advanced Analytics, Adidas *"A great in-depth introduction to how to add value to companies using digitalization, data and AI." * Patrick Glauner, Professor of Artificial Intelligence, Deggendorf Institute of Technology *"Provides great guidance on how to think of data products instead of projects - which is a key factor in mastering the challenges of digitalization." * Carsten Bange, Founder and CEO, BARC (Business Application Research Centre) *"A must-read for everyone involved into turning digitization and AI into real value for your company. Whether you're in the middle of the process looking for some orientation or just about getting started, this book will provide you with the advice you need!" * Alexander Thamm, CEO and Founder of Alexander Thamm GmbH *Table of Contents Chapter - 01: Introduction to delivering data and AI products; Chapter - 02: Developing the data and AI product strategy and goals; Chapter - 03: Setting up the data and AI product delivery organization; Chapter - 04: Identifying and defining data and AI products; Chapter - 05: Delivering high quality data and AI products; Chapter - 06: Designing the data and AI platform and architecture; Chapter - 07: Driving transformative change with data and AI products; Chapter - 08: The future of data and AI products in your organization

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Book SynopsisProviding an authoritative assessment of the current landscape of spatial analysis in the social sciences, this cutting-edge Handbook covers the full range of standard and emerging methods across the social science domain areas in which these methods are typically applied. Accessible and comprehensive, it expertly answers the key questions regarding the dynamic intersection of spatial analysis and the social sciences. The chapters are split into insightful sections dedicated to foundational background material, methods, social science applications and the challenges on the horizon, using state-of-the-art coverage of the traditional and novel spatial methods. Leading scholars in the field use a range of applications to illustrate the diverse ways in which spatial analysis methods can inform research in the field of social sciences. Furthermore, the Handbook discusses the key challenges to that research including uncertainty, reproducibility and replicability. This Handbook of Spatial Analysis in the Social Sciences will be an excellent informative resource for scholars in the fields of geography, social sciences and public health. Established and early career researchers of the social sciences alike will appreciate the detailed overview of the methods and applications as well as the ability to expand their methodological knowledge.Trade Review‘Rey and Franklin introduce this new Handbook with an allusion to a rapidly changing landscape. And perhaps because any landscape, but especially a swiftly evolving one needs solid landmarks, this collection is a welcome addition to the literature that should appeal to any researchers in the social sciences working to learn the ropes of spatial analysis.’ -- Antonio Paez, McMaster University, Canada‘A comprehensive collection of chapters, carefully curated, spanning the current state of the art of spatial analysis in the social sciences written by established experts in the field ably accompanied by those creating the expertise of the future.’ -- Danny Dorling, Oxford University, UKTable of ContentsContents: Introduction: Spatial analysis and the social sciences in a rapidly changing landscape xi Sergio J. Rey and Rachel S. Franklin PART 1 THEORY, FRAMEWORKS AND FOUNDATIONS 1 GIScience through the looking glass 2 Barbara P. Buttenfield 2 Locating spatial data in the social sciences 16 Jonathan Reades 3 Analytical environments 36 Roger Bivand 4 Complexity 64 Li An 5 Linking spatial patterns to processes 85 Colin Robertson and Jed Long PART 2 METHODS 6 Spatial econometrics 101 Luc Anselin 7 Local modeling in a regression framework 123 Mehak Sachdeva, Taylor Oshan and A. Stewart Fotheringham 8 Simulating geographical systems using cellular automata and agent-based models 142 Alison Heppenstall, Andrew Crooks, Ed Manley and Nick Malleson 9 Microsimulation 158 Nik Lomax 10 Multilevel models 173 Richard Harris 11 Context-dependent movement analysis 187 Somayeh Dodge 12 Spatial interaction modeling 208 Taylor Oshan 13 Spatial optimization 223 Alan T. Murray 14 Cluster identification 245 Edward Helderop and Tony H. Grubesic 15 Spatial point patterns 262 Stuart Sweeney and Sophia Arabadjis 16 Spatial dynamics 277 Wei Kang 17 GeoAI in social science 291 Wenwen Li 18 Exploratory spatial data analysis 305 Ran Wei 19 Geovisualization and geovisual analysis 322 Alasdair Rae 20 Immersive virtual reality and spatial analysis 336 Trevor M. Harris 21 Spatiotemporal data mining 352 Arun Sharma, Zhe Jiang and Shashi Shekhar PART 3 APPLICATIONS 22 Neighborhood change 370 Elizabeth Delmelle 23 The spatial analysis of gentrification: Formalizing geography in models of a multidimensional urban process 384 Elijah Knaap 24 Social networks in space 400 Clio Andris and Dipto Sarkar 25 Analysing the dynamics of inter-regional inequality: The case of Canada 416 Sébastien Breau 26 Spatial approaches to energy poverty 434 Caitlin Robinson 27 The shape of bias: Understanding the relationship between compactness and bias in U.S. elections 451 Levi John Wolf 28 Space and New Urbanism 470 Emily Talen 29 Space for wellbeing 481 Victoria Houlden 30 Urban analytics: History, trajectory and critique 503 Geoff Boeing, Michael Batty, Shan Jiang and Lisa Schweitzer PART 4 EMERGING CHALLENGES AND ISSUES 31 Reproducibility and replicability in spatial science 518 Michael F. Goodchild 32 An image library: The potential of imagery in (quantitative) social sciences 528 Daniel Arribas-Bel, Francisco Rowe, Meixu Chen and Sam Comber 33 Uncertainty 544 David C. Folch Index 559

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Book SynopsisThis insightful book examines all aspects of the design process and implementation of questionnaire surveys on the activities of business, public sector, and non-profit organizations. Anthony Arundel discusses how different aspects of the survey method and planned statistical analysis can constrain question design, and how these issues can be effectively resolved. Throughout this engaging yet practical book, Arundel promotes good practices for questionnaire design, sample construction, and survey delivery systems including online, postal, and verbal methods, with a focus on obtaining high-quality data in line with ethics and confidentiality requirements. Chapters include constructive advice on questionnaire design and testing, survey implementation, and data processing, analysis, and reporting, with examples of time and financial cost budgets. Considering the recent developments in survey methods, the book explores how to use web probing as a substitute for cognitive testing and examines the use of tablets and smartphones in answering questionnaires. Combining theoretical and practical insights into survey design, implementation, and data processing and analysis, this book will be essential reading for business and management scholars and students, with a particular interest in research methods and organization studies. It will also be useful for practitioners and business managers seeking to understand how to create and use surveys.Trade Review‘This book by Anthony Arundel is a must read for researchers or practitioners that plan to conduct a survey. In a very understandable and insightful way, Arundel takes the reader through the intricacies of each step involved in designing and implementing a high quality survey, from questionnaire testing and design to sampling, data processing, and analysis.’ -- Carter Bloch, CFA, Aarhus University, Denmark‘Anthony Arundel has experience from decades of statistical measurement, survey design, management, and analysis of survey outcomes. He knows what works and what does not, and this guide provides the reader with valuable and accessible information. Anyone who needs to understand survey design, and results, should read this book.’ -- Fred Gault, UNU-MERIT, the Netherlands and Tshwane University of Technology, South Africa‘This is a much-needed book. It provides a complete and detailed overview of all practical steps that are required for setting-up, executing, and analysing a survey of firms and other organizations. The clear and non-technical language makes the book highly accessible also to readers not experienced in survey techniques. Everyone planning to conduct a survey should consult this book.’ -- Christian Rammer, Centre for European Economic Research (ZEW), GermanyTable of ContentsContents: 1. Introduction 2. Survey fundamentals 3. Questionnaire design 4. Questionnaire testing 5. Survey implementation 6. Data processing activities 7. Data analysis and reporting 8. Conclusion References Annexes Index

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Book SynopsisThis practical book explores collaborative inquiry as an approach to research and change in organizations where internal members and external researchers work together as partners to address organizational issues and create knowledge about changing organizations.Taking a research-based approach, Abraham B. (Rami) Shani and David Coghlan analyze the challenges that participants face in building a partnership between researchers and practitioners throughout the phases of collaboration. Chapters explore how collaborative partners assess the organization's current and future capabilities by expressing the present and future in creative imagery and by making relevant changes in the organization to create that future. The book examines the theoretical foundations behind collaborative inquiry in addition to the methodologies of this approach to organization development and change.Mapping both the theory and practice of collaborative inquiry, this book will be a valuable resource for scholars and students of organization studies and research methods, particularly those with a focus on business and management. It will also be beneficial for practitioners interested in collaborative and action research modes.Trade Review‘This book is one of the best exemplars of showing how these two practices--research and helping--can inform each other constructively. This book is a welcome exploration of how these practices have enlarged our understanding of how human systems really work, how they must be studied, and how we can constructively intervene in them.’ -- - Edgar H. Schein, Professor Emeritus, MIT Sloan School of Management, US‘As scientists, we ask ourselves how we can contribute more to the amelioration of the most challenging issues of our time, such as global pandemics, climate change, social justice. Shani and Coghlan help us understand that there is a better way for science to influence decision makers. Collaborative inquiry is built to provide a scientific approach to change and Shani and Coghlan have been the masters of that approach for decades. This new book should be read by any scientist or leader who wants to make progress instead of just bemoaning the current state of affairs.’ -- - William A. (Bill) Pasmore, Professor of Practice, Colombia University, USTable of ContentsContents: Foreword 1 Massimo S. Brunelli Foreword 2 Michael Beer Preface 1. Introducing collaborative inquiry 2. Theoretical foundations 3. Methodology and methods of inquiry 4. Transformation and design 5. Phases, mechanisms and quality 6. The researcher, theorizing and opportunities Epilogue Afterword: Collaborative inquiry: takeaways and applications Philip H. Mirvis References Index

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Book SynopsisDrawing on the theoretical debates, practical applications, and sectoral approaches in the field, this ground-breaking Handbook unpacks the political and regulatory developments in AI and big data governance. Covering the political implications of big data and AI on international relations, as well as emerging initiatives for legal regulation, it provides an accessible overview of ongoing data science discourses in politics, law and governance.With novel insights into existing and emerging debates, this cutting-edge Handbook highlights the mutual effects of big data and AI on society. Amongst other theoretical and sectoral issues, chapters analyse the liability of AI use in autonomous weapons, the role of big data in healthcare and education, the intersections between AI and gender in human rights law, and the ethics of public facial-recognition technology. Addressing the many open questions and future regulatory problems, it uses data science to investigate the dynamics between the technical aspects, societal dynamics and governance implications of big data and AI.Transdisciplinary in scope, this Handbook will be invaluable to students and researchers across the fields of politics, law, governance and data science, alongside policymakers concerned with the regulation and governance of AI and big data in public and private institutions.Trade Review‘Zwitter and Gstrein have astutely brought together an impressive collection of chapters that address key themes in the politics and governance of AI and big data. From social justice and gender to privacy and rights, the Handbook provides a solid introduction to key debates and their implications for societies.’ -- Evelyn Ruppert, Goldsmiths, University of London, UK‘This volume succeeds in bringing together a wide ranging collection of original studies in a field that is as fast developing as it is important to keep track of. The reader who is interested in normative political and governance perspectives on AI and big data will find insightful analyses and well-informed discussions of the key problems of regulation and policy making in a digital age.’ -- Jeroen van den Hoven, Delft University of Technology, the NetherlandsTable of ContentsContents: Foreword xiii PART I INTRODUCTION Introduction to the Handbook on the Politics and Governance of Big Data and Artificial Intelligence 2 Andrej Zwitter and Oskar J. Gstrein PART II CONCEPTUAL PERSPECTIVES 1 Can AI governance be progressive? Group interests, group privacy and abnormal justice 19 Linnet Taylor 2 Big Data and the humanitarian sector: emerging trends and persistent challenges 41 Susanne Schmuck, Andrej Zwitter and Oskar J. Gstrein 3 Digital twins: potentials, ethical issues and limitations 64 Dirk Helbing and Javier Argota Sánchez-Vaquerizo 4 Governing Digital Twin technology for smart and sustainable tourism: a case study in applying a documentation framework for architecture decisions 105 Eko Rahmadian, Daniel Feitosa and Andrej Zwitter PART III PRINCIPLE-BASED APPROACHES TO THE GOVERNANCE OF BIG DATA AND AI 5 Digital transitional justice: unpacking the black box 139 Christopher K. Lamont and Medlir Mema 6 Autonomous weaponry and IR theory: conflict and cooperation in the age of AI 167 Amelia Hadfield and Alex Leveringhaus 7 Understanding emergent technology, instability and power in international political economy 188 Malcolm Campbell-Verduyn 8 Governance of AI and gender: building on International Human Rights Law and relevant regional frameworks 211 Elizabeth Coombs and Halefom Abraha PART IV SECTORAL APPROACHES TO THE GOVERNANCE OF BIG DATA AND AI 9 Better technological security solutions through human-centred design and development 245 Andrew B. Wootton, Caroline L. Davey, Dagmar Heinrich and Maximilian Querbach 10 On the governance of privacy-preserving systems for the web: should Privacy Sandbox be governed? 279 Lukasz Olejnik 11 Experiments with facial recognition technologies in public spaces: in search of an EU governance framework 315 Catherine Jasserand 12 Big Data, AI and health data: between national, European, and international legal frameworks 358 Nikolaus Forgó, Emily Johnson, Iana Kazeeva and Elisabeth Steindl 13 Governing the ‘datafied’ school: bridging the divergence between universal education and student autonomy 395 Theresa Henne and Oskar J. Gstrein PART V AUTONOMOUS SYSTEMS, RIGHTS AND DUTIES 14 Artificial Intelligence and international human rights law: implications for humans and technology in the 21st century and beyond 430 Joshua C. Gellers and David J. Gunkel 15 Challenges posed by autonomous systems to liability regimes: finding a balance 456 Nynke E. Vellinga 16 Autonomous Weapons Systems in warfare: is Meaningful Human Control enough? 476 Taís Fernanda Blauth Index 504

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Book SynopsisThis state-of-the-art Handbook provides an overview of the role of big data analytics in various areas of business and commerce, including accounting, finance, marketing, human resources, operations management, fashion retailing, information systems, and social media. It provides innovative ways of overcoming the challenges of big data research and proposes new directions for further research using descriptive, diagnostic, predictive, and prescriptive analytics.With contributions from leading academics and practitioners, the Handbook analyses how big data analytics can be used in different sectors, including detecting credit fraud in the financial sector, identifying potential diseases in health care, and increasing customer loyalty in the telecommunication sector. Chapters explore the use of artificial intelligence in accounting, the construction of successful data science ecosystems using the public cloud, and transformational models of personal data protection in the digital era. The Handbook also discusses the difficulties of adopting a data science platform and how the public cloud can aid companies in overcoming these challenges.Exploring how industries rely on predictive analytics to improve their decision-making, this Handbook will be essential reading for students and scholars in business analytics, economics, information systems, innovation and technology, and research methods. It will also benefit data analysts, economists, human resource managers, marketers, neuroscientists, and social science researchers.Trade Review‘Big data research methods have gained dramatic momentum in the world. Researchers and practitioners extend this line of research constantly by producing journals, posts, news articles and podcasts. However, there is a paucity of a book that covers descriptive, diagnostic, predictive and prescriptive method-based research papers under one umbrella. This is one of those books which will immerse a reader in the past, present and future of big data analytics methods. It is an exceptional book that is grounded in evidence and meaningful to practice.’ -- Yogesh K. Dwivedi, Swansea University, UKTable of ContentsContents: 1 Introduction to the Handbook of Big Data Research Methods 1 Shahriar Akter, Samuel Fosso Wamba, Shahriar Sajib and Sahadat Hossain 2 Big data research methods in financial prediction 11 Md Lutfur Rahman and Shah Miah 3 Big data, data analytics and artificial intelligence in accounting: an overview 32 Sudipta Bose, Sajal Kumar Dey and Swadip Bhattacharjee 4 The benefits of marketing analytics and challenges 52 Madiha Farooqui 5 How big data analytics will transform the future of fashion retailing 72 Niloofar Ahmadzadeh Kandi 6 Descriptive analytics and data visualization in e-commerce 86 P.S. Varsha and Anjan Karan 7 Application of big data Bayesian interrupted time-series modeling for intervention analysis 105 Neha Chaudhuri and Kevin Carillo 8 How predictive analytics can empower your decision making 117 Nadia Nazir Awan 9 Gaussian process classification for psychophysical detection tasks in multiple populations (wide big data) using transfer learning 128 Hossana Twinomurinzi and Hermanus C. Myburgh 10 Predictive analytics for machine learning and deep learning 148 Tahajjat Begum 11 Building a successful data science ecosystem using public cloud 165 Mohammad Mahmudul Haque 12 How HR analytics can leverage big data to minimise employees’ exploitation and promote their welfare for sustainable competitive advantage 179 Kumar Biswas, Sneh Bhardwaj and Sawlat Zaman 13 Embracing Data-Driven Analytics (DDA) in human resource management to measure the organization performance 195 P.S. Varsha and S. Nithya Shree 14 A process framework for big data research: social network analysis using design science 214 Denis Dennehy, Samrat Gupta and John Oredo 15 Notre-Dame de Paris cathedral is burning: let’s turn to Twitter 233 Serge Nyawa, Dieudonné Tchuente and Samuel Fosso Wamba 16 Does personal data protection matter in data protection law? A transformational model to fit in the digital era 266 Gowri Harinath 17 The future of AI-based CRM 278 Khadija Alnofeli, Shahriar Akter and Venkata Yanamandram 18 Descriptive analytics methods in big data: a systematic literature review 294 Nilupulee Liyanagamage and Mario Fernando Index

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Book SynopsisThis practical book explores collaborative inquiry as an approach to research and change in organizations where internal members and external researchers work together as partners to address organizational issues and create knowledge about changing organizations.Taking a research-based approach, Abraham B. (Rami) Shani and David Coghlan analyze the challenges that participants face in building a partnership between researchers and practitioners throughout the phases of collaboration. Chapters explore how collaborative partners assess the organization's current and future capabilities by expressing the present and future in creative imagery and by making relevant changes in the organization to create that future. The book examines the theoretical foundations behind collaborative inquiry in addition to the methodologies of this approach to organization development and change.Mapping both the theory and practice of collaborative inquiry, this book will be a valuable resource for scholars and students of organization studies and research methods, particularly those with a focus on business and management. It will also be beneficial for practitioners interested in collaborative and action research modes.Trade Review‘This book is one of the best exemplars of showing how these two practices--research and helping--can inform each other constructively. This book is a welcome exploration of how these practices have enlarged our understanding of how human systems really work, how they must be studied, and how we can constructively intervene in them.’ -- - Edgar H. Schein, Professor Emeritus, MIT Sloan School of Management, US‘As scientists, we ask ourselves how we can contribute more to the amelioration of the most challenging issues of our time, such as global pandemics, climate change, social justice. Shani and Coghlan help us understand that there is a better way for science to influence decision makers. Collaborative inquiry is built to provide a scientific approach to change and Shani and Coghlan have been the masters of that approach for decades. This new book should be read by any scientist or leader who wants to make progress instead of just bemoaning the current state of affairs.’ -- - William A. (Bill) Pasmore, Professor of Practice, Colombia University, USTable of ContentsContents: Foreword 1 Massimo S. Brunelli Foreword 2 Michael Beer Preface 1. Introducing collaborative inquiry 2. Theoretical foundations 3. Methodology and methods of inquiry 4. Transformation and design 5. Phases, mechanisms and quality 6. The researcher, theorizing and opportunities Epilogue Afterword: Collaborative inquiry: takeaways and applications Philip H. Mirvis References Index

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Book SynopsisTaking the Fear Out of Data Analysis provides readers with the necessary knowledge and skills to understand, perform, and interpret quantitative data analysis effectively. Acknowledging that people often dislike statistics and quantitative methods, this book illustrates that statistical reasoning can be a fun and intuitive part of our lives.Key Features: Split into three sections covering how to understand data, preparing data for analysis and carrying out the analysis Blends theory with practical examples in a logical and straightforward manner to guide readers in making sense of statistical inference Offers universal knowledge that can be applied to a variety of software applications with limited technical complexity to aid the learning process Short and concise chapters focusing on the essence of the topics covered, such as analytical techniques that are typically used in behavioral and social science research Significantly revised and updated, this textbook is an essential text for both undergraduate and postgraduate students in fields such as information systems, international business and marketing. It will also be beneficial for practitioners involved in data science, data analytics, and market research.Trade Review‘Written with wry wit and incredible clarity, the authors provide the reader with a detailed understanding of seminal issues in data analysis. A masterful work that truly does “take the fear out of data analysis” – this book is a rare treat indeed.’ -- David A. Griffith, Mays Business School, Texas A&M University, US‘Written by a proficient team of authors, Taking the Fear out of Data Analysis is a fascinating … ah, forget the marketing blurb. This is a great text, you should read it! There is no doubt that you will devour this book in no time and learn a lot about statistics on the way.' -- Marko Sarstedt, Ludwig-Maximilians-University (LMU), Germany‘Statistics. I know – you hate it. It’s hard and confusing. Students of all levels find the topic hard. I tell them to get this book. And no! They cannot borrow mine, I don’t want to lose it. Diamantopoulos, Schlegelmilch and Halkias knock another one out of the park with this excellent introduction to a great array of statistical issues. They start right at the beginning – which is always a good place to start if you’re a beginner – and gently, often hilariously, and successfully guide the reader through the various learning moments that need to be negotiated if one is to become fearless in the face of columns of data. Priceless.’ -- John Cadogan, School of Business and Economics, Loughborough University, UK‘The new edition of this book provides excellent guidance to data knowledge and competence using a problem-solving approach. With the digital becoming increasingly important, analytical skills should be key competencies in everybody’s daily life. To achieve this goal, Taking the Fear out of Data Analysis is highly recommended.’ -- Zhongming Wang, Zhejiang University, China‘The significantly extended, new edition is increasingly relevant as the world of quantitative methods has kept on expanding, in part due to an explosion in software programs that scholars can use seemingly without much understanding. Do not let the light-hearted nature of this book fool you. It is a statistics book that carefully leads authors through all the necessary stages of analysis. It effortlessly explains the analysis details and assumptions that PhD examiners, journal reviewers, and conference presentation audience members insist on raising. This excellent new edition is destined to be very well thumbed.’ -- Matthew Robson, Cardiff Business School, UKTable of ContentsContents: Pre-publication reviews from around the world Introduction to Taking the Fear out of Data Analysis PART I UNDERSTANDING DATA 1. What is data (and can you do it in your sleep)? 2. Does sampling have a purpose other than providing employment for statisticians? 3. Why should you be concerned about different types of measurement? PART II PREPARING DATA FOR ANALYSIS 4. Have you cleaned your data and found the mistakes you made? 5. Why do you need to know your objective before you fail to achieve it? PART III CARRYING OUT THE ANALYSIS 6. Why not take it easy initially and describe your data? 7. Can you use few numbers in place of many to summarize your data? 8. What about using estimation to see what the population looks like? 9. How about sitting back and hypothesizing? 10. Simple things first: One variable, one sample 11. Getting experienced: Making comparisons 12. Getting adventurous: Searching for relationships 13. Getting hooked: A look into multivariate analysis 14. Getting obsessed: A further look into multivariate analysis 15 It’s all over … or is it? Index

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Book SynopsisThe Elgar Encyclopedia of Law and Data Science represents a comprehensive mapping of the field. Comprising over 60 entries, it features contributions from eminent global scholars, drawing on expertise from multiple disciplines, including law and data science, economics, computer engineering, physics, biomedical engineering and history, philosophy, neuro-engineering, political science, and geo-informatics.This Encyclopedia brings together jurists, computer scientists, and data analysts to uncover the challenges, opportunities, and fault lines that arise as these groups are increasingly thrown together by expanding attempts to regulate and adapt to a data-driven world. It explains the concepts and tools at the crossroads of the many disciplines involved in data science and law, bridging scientific and applied domains. Entries span algorithmic fairness, consent, data protection, ethics, healthcare, machine learning, patents, surveillance, transparency and vulnerability.Comprehensive yet accessible, this Encyclopedia will be an indispensable resource for scholars of law, data science, artificial intelligence and law and technology. It also contains practical implications for a manifold of users: from domain experts to policy makers, from businesses to practitioners.Key Features: The first Encyclopedic coverage of the field of Law and Data Science Over 60 entries Entries organized alphabetically for ease of reference Full analytical index Interrelated multidisciplinary perspectives Unique accessibility for non-experts. Table of ContentsContents: Introduction to the Encyclopedia of Law and Data Science: ‘Directions for Use’ ix Giovanni Comand. 1 Access 1 Giulia Schneider 2 Accountability 7 Giulia Schneider 3 Algorithm 12 Letizia Milli and Giulio Rossetti 4 Algorithmic Discrimination 17 Bettina Berendt 5 Algorithmic Fairness 32 Salvatore Ruggieri 6 Anonymity 36 Dino Pedreschi, Roberto Pellungrini, Francesca Pratesi 7 Anonymous Data 41 Lorenzo Dalla Corte 8 Argument Mining 48 Vern R. Walker 9 Artificial General Intelligence 53 Bettina Berendt 10 Bias [definition] 60 Antonio Davola 11 Children (in the Digital Environment) 64 Denise Amram 12 Clustering (see Data Mining and Clustering) 70 13 Computer Programs 70 Guido Noto La Diega 14 Confidentiality 73 Arianna Rossi, Itzel Vazquez Sandoval, Gabriele Lenzini 15 Consent 81 Cesare Bartolini 16 Copyright 83 Caterina Sganga 17 Cybersecurity (in Distributed Computing Systems) 92 Tommaso Cucinotta 18 Database Protection 98 Caterina Sganga 19 Data Breach 105 Anna Monreale, Roberto Pellungrini, Francesca Pratesi 20 Data Mining and Clustering 110 Athanasios Kiourtis, Argyro Mavrogiorgou, Dimosthenis Kyriazis 21 Data Protection 122 Cesare Bartolini 22 Data Protection Impact Assessment 125 Anna Monreale, Roberto Pellungrini, Francesca Pratesi 23 Data Quality 130 Michela Natilli, Salvatore Rinzivillo, Franco Turini 24 Data Subject 134 Gloria Gonz.lez Fuster 25 Decision-making 139 Gianclaudio Malgieri 26 Discrimination Data Analysis 142 Salvatore Ruggieri 27 Disparate Impact (from Software-Based Decision-Making Systems) 146 Jeanna Neefe Matthews 28 Erasure 150 Cesare Bartolini 29 Ethics 153 Giorgia Pozzi and Juan M. Dur.n 30 Explainability 160 Riccardo Guidotti, Fosca Giannotti, Dino Pedreschi 31 Fairness 168 Giulia Schneider 32 Forgotten (Right to Be) 175 Paul De Hert and Vagelis Papakonstantinou 33 Freedom of Information (Freedom of Expression – Access to Public Data) 181 Matteo Monti 34 Governance (of Personal Data Flows) 186 Denise Amram 35 Healthcare (Data Science in) 192 Martina Finocchiaro, Tommaso Banfi, Matteo Vissani, Alberto Mazzoni, Gastone Ciuti 36 Informed Consent 199 Danielle da Costa Leite Borges 37 Lawfulness and Necessity (of Possible Limitations on the Fundamental Rights to Privacy and to the Protection of Personal Data) in the EU Legal Order 203 Mario Guglielmetti 38 Legitimate Interest 209 Christopher F. Mondschein and Cosimo Monda 39 Liability 215 Andrea Parziale 40 Machine Learning 223 Luca Pappalardo 41 Mobility Data (Knowledge Discovery from) 227 Agnese Bonavita and Giovanni Comand. 42 Necessity (see Lawfulness and Necessity (of Possible Limitations on the Fundamental Rights to Privacy and to the Protection of Personal Data) in the EU Legal Order) 241 43 Open Data and Public Sector Information 241 Lorenzo Dalla Corte and Bastiaan van Loenen 44 Patents 253 Enrico Bonadio and Hannes Sigurgeirsson 45 Personal Data in the EU Legal System 259 Lorenzo Dalla Corte 46 Portability (of Data) 267 Gianclaudio Malgieri 47 Predictive Analytics 271 Mirco Nanni 48 Privacy 275 Carlotta Rigotti and Alessandra Calvi 49 Privacy by Design 281 Marina Sokolova and Stan Matwin 50 Privacy-Preserving Technologies 291 Josep Domingo-Ferrer 51 Profile/Profiling 300 Salvatore Ruggieri 52 Proportionality 305 Giuseppe Martinico 53 Pseudonymization 310 Anna Monreale, Roberto Pellungrini, Francesca Pratesi 54 Public Interest (Scientific Research and the Legal Grounds) 314 Hanne Elsen, Wessel Damen, Audrey Van Scharen 55 Public Sector Information (see Open Data and Public Sector Information) 318 56 Reasonable Safeguards 318 Andr.s Chomczyk Penedo 57 Scoring 323 Frank Pasquale 58 Software (Computer Programs) 326 Guido Noto La Diega 59 Supervisory Authorities (Powers) 329 Roberto Lattanzi 60 Surveillance 341 Juraj Sajfert 61 Trade Secrets and Data-Driven Innovation in the EU 347 Silvia Scalzini 62 Transparency 354 Giulia Schneider 63 Unfairness [definition] 360 Antonio Davola 64 Vulnerability 363 Gianclaudio Malgieri Analytical index 371 Index

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

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Book SynopsisNext-generation DNA and RNA sequencing has revolutionized biology and medicine. With sequencing costs continuously dropping and our ability to generate large datasets rising, data analysis becomes more important than ever. Next-Generation Sequencing Data Analysis walks readers through next-generation sequencing (NGS) data analysis step by step for a wide range of NGS applications. For each NGS application, this book covers topics from experimental design, sample processing, sequencing strategy formulation, to sequencing read quality control, data preprocessing, read mapping or assembly, and more advanced stages that are specific to each application. Major applications include: RNA-seq: Both bulk and single cell (separate chapters) Genotyping and variant discovery through whole genome/exome sequencing Clinical sequencing and detection of actionable variants De novo genome assembly Table of Contents 1. The Cellular System and The Code of Life. 2. DNA Sequence: the Genome Base. 3. RNA: the Transcribed Sequence. 4. Next-Generation Sequencing (NGS) Technologies: Ins and Outs. 5. Early-Stage Next-Generation Sequencing (NGS) Data Analysis: Common Steps. 6. Computing Needs for Next-Generation Sequencing (NGS) Data Management and Analysis. 7. Transcriptomics by Bulk RNA-Seq. 8. Transcriptomics by Single Cell RNA-Seq. 9. Small RNA Sequencing. 10. Genotyping and Variation Discovery by Whole Genome/Exome Sequencing. 11. Clinical Sequencing and Detection of Actionable Variants. 12. De Novo Genome Assembly with Long and/or Short Reads. 13. Mapping Protein-DNA Interactions with ChIP-Seq. 14. Epigenomics by DNA Methylation Sequencing. 15. Whole Metagenome Sequencing for Microbial Community Analysis. 16. What’s Next for Next-Generation Sequencing (NGS)?.

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Book SynopsisThis book is a step-by-step data story for analyzing ordinal data from start to finish. The book is for researchers, statisticians and scientists who are working with datasets where the response is ordinal. This type of data is common in many disciplines, not just in surveys (as is often thought). For example, in the biological sciences, there is an interest in understanding and predicting the (growth) stage (of a plant or animal) based on a multitude of factors. Likewise, ordinal data is common in environmental sciences (for example, stage of a storm), chemical sciences (for example, type of reaction), physical sciences (for example, stage of damage when force is applied), medical sciences (for example, degree of pain) and social sciences (for example, demographic factors like social status categorized in brackets). There has been no complete text about how to model an ordinal response as a function of multiple numerical and categorical predictors. There has always been a reluctanc

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