Data capture and analysis Books
Kogan Page Ltd ValueDriven Data
Book SynopsisEdosa Odaro is an AI and data transformation leader who has helped countless international organizations deliver significant impact through data analytics, transformation strategy and intelligent interventions. He is Chief Data and Analytics Officer at Tawuniya and is on the board for the UK's National Institute for Health Data Science (HDR UK). Odaro has been named a Financial Times Top 100 Most Influential Leader and one of the UK's 30 Most Influential Black Leaders in FinTech.Trade Review"A masterclass in how to unlock the true value of data for your organization. Value-Driven Data is a must read for all data leaders." * Hartnell Ndungi, Chief Data Officer, Absa Group *"Value-Driven Data is a timely and practical guide to support us all with the challenge of unlocking and measuring the value of data. This thought provoking book is filled with practical examples to support frameworks and theories. A must read for all executives." * Dr Johanna Hutchinson, Chief Data Officer, BAE Systems and Board Member, The Royal Statistics Society *"A powerful reminder that data is not just a valuable asset, but a critical driver of business success and unlocking new value pools sitting at the intersection of technology and sustainable business." * Lamé Verre, Head of Strategy, Innovation & Sustainability, SSE Energy Customer Solutions and Global Future Council Member, World Economic Forum *"Value-Driven Data is an excellent book and a valuable resource for anyone looking to cut through the noise. It provides clarity on how to quantify the financial impact of data initiatives and effectively communicates with senior and non-technical audiences using clear and concise language." * Amy Shi-Nash, Chief Analytics & Data Officer, Tabcorp and Data Board Member, MIT Sloan School of Management *"Edosa has masterfully stitched together a collection of great examples with a set of tangible principles to guide readers on how to enhance their potential with data. The insights that this book provides are unique, the advice practical and the success stories applicable across industry sectors." * Mona Soni, Chief Technology Officer, formerly at S&P Global and Dow Jones *"Value-Driven Data offers a combination of deep knowledge and practical value for leaders guiding organizations through the responsible use of data. Odaro brings together a variety of perspectives from data practitioners and consultants to executive leadership in global businesses. I hope his shared knowledge will reach data professionals around the world and contribute to their success." * Simone Steel, Chief Data and Analytics Officer & CIO for Enterprise Data Platforms, Nationwide Building Society *"Value Driven Data cuts through the rumours and hearsay with real-life, no-nonsense examples of creating a data vision and value in practice. This is a comprehensive guide for both data professionals and business leaders. Once you have read it you won't want to do research without it." * Graeme McDermott, Chief Data Officer, Tempcover *"Provides insightful frameworks and considerations for every organization that wants to get more value out of data and analytics." * Gero Martin Gunkel, Data Science Leader & Chief Operating Officer (ZCAM), Zurich Insurance *"Value-Driven Data provides a comprehensive framework for developing a data vision that aligns with the overall strategy of an organisation. One of the most impressive aspects of the book is how it breaks down complex concepts into easy-to-understand language, making it an enjoyable read for anyone interested in data strategy, regardless of their level of expertise." * Rowland Agidee, Head of Data Management, UK Intellectual Property Office *"Edosa brings his experience and expertise together to remind us all of how expressing data value is fundamental to data driven transformation." * JC Lionti, Managing Director & Chief Data Officer, formerly at BNP Paribas Americas *"Edosa has done terrific work in producing this masterpiece! I like the way he has used data visions as the starting point and has linked all chapters to it by creating a practical and actionable book to help organizations realize their full potential." * Ram Kumar, Chief Data & Analytics Officer, Cigna *"Finally, a book that makes delivering value through data the number one priority. Business Leaders, whilst interested, do not really care how we as data professionals do it. Influencing top line, cost avoidance and bottom line are central to 99.9% of business strategies and so should also be the main focus when creating data strategies. Using real-world and highly relatable examples, Edosa has delivered an essential read for both data and business professionals." * Sam Richmond, Group Head of Data, The Go-Ahead Group *"Value-Driven Data is an incredible resource, full of frameworks and tools to help navigate the elusive topic of data value in an easy to digest format, with stories drawn from Edosa's long professional career. A valuable instrument in an era of cost optimisation, providing knowledge to the reader to aid in directing and articulating vision, value and creating pathways to overcome obstacles." * Stylianos Taxidis, Head of Data Science & AI, Costain Group *Table of Contents Chapter - 00: Introduction Section - ONE: Vision: Discovering and capturing data value opportunities Chapter - 01: What is data vision? Chapter - 02: Capturing data visions Chapter - 03: Why data visions of all size matter Chapter - 04: The destructive impact of data vision misalignment Chapter - 05: Simplifying data vision misalignments Section - TWO: Obstacle: The things that stand between data visions and data value realisation Chapter - 06: Obstacles of the past Chapter - 07: Obstacles of the future Chapter - 08: Obstacles of the present Section - THREE: Value: Identifying, capturing and communicating data value Chapter - 09: Capturing data value propositions Chapter - 10: Measuring data value for business case and operational assurance Chapter - 11: The data value measurement lifecycle Chapter - 12: A data value account for data profits and losses Chapter - 13: Presenting data value to the CXO, EXCO and the board Chapter - 14: Conclusion: Bringing it all together
£85.50
Kogan Page Ltd Data Ethics
Book SynopsisKatherine O'Keefe works with Ireland's national water utility, Uisce Éireann, and is on the teaching faculty of the Law Society of Ireland's Diploma Centre. She has developed international professional accreditation schemes for information management, for which she was awarded the DAMA International Professional Achievement Award in 2017. Daragh O Brien is the founder and managing director of Castlebridge, a leading data strategyconsultancy. He lectures in Data Protection and Data Governance in the Sutherland School of Law at University College Dublin. Daragh is a founding member of the Strategic Advisory Board to the School of Business in Maynooth University.Trade Review"Ethics play an increasingly important role when considering how to collect and use personal data. This updated edition of Data Ethics clearly explains how to take ethics seriously and make it an integral part of business information management and governance. The combination of sound and up to date legal theories with practical tips and case studies makes it a useful handbook for anyone working with data on a regular basis." * Paul Breitbarth, Senior Visiting Fellow, European Centre on Privacy and Cybersecurity, Maastricht University *"In a world where AI is creating a growing wave of often dubious information, O Brien and O'Keefe's book should be mandatory reading for everybody in IT, media, regulatory bodies and beyond. This new edition of Data Ethics focuses on emerging topics of vital importance in a world where ethical decisions by IT may literally be, in the extreme, matters of life and death." * Dr Barry Devlin, Founder, 9 Sight Consulting and author of Business unIntelligence *"I can't think of a subject more relevant than data ethics. Given that we live in a data-dependent world, the most important question is not "Can I do something with data?" but "Should I do something with data?". These questions should be considered by teens learning to code, businesspeople gathering and exploiting customer data, scientists developing and releasing Artificial Intelligence (AI) applications, and anyone creating and using data. Daragh and Katherine provide an excellent groundwork for addressing these questions and give us the tools to think and act with our data in a responsible way. Read their book, share it and apply it!" * Danette McGilvray, President and Principal, Granite Falls Consulting, Inc. and author of Executing Data Quality Projects *"Reading Data Ethics gave me goosebumps. Impeccably researched, it is the definitive work on the topic. Simultaneously confronting and enlightening, it challenged my own ethical framework and validated the principles I hold dear in my practice as a Data Governance Executive. The foreword by John Ladley is delightful and sets the scene perfectly for what is to follow. I look forward to our DAMA community here in Australia, and internationally, having the opportunity to share their experiences after reading this outstanding book on data ethics." * Andrew Andrews, Data Governance Manager, ANZ Banking Group and Vice President of Marketing, DAMA International *Table of Contents Chapter - 00: Introduction - Why write a book on data ethics?; Chapter - 01: Ethics in the context of data management; Chapter - 02: Introduction to ethical concepts and frameworks; Chapter - 03: Ethical principles, standards and practice; Chapter - 04: Ethics, privacy and analytics; Chapter - 05: Ethics and data management (including AI); Chapter - 06: Developing an ethical architecture for information management; Chapter - 07: Introducing the Ethical Enterprise Information Management (E2IM) framework; Chapter - 08: Information ethics as an information quality system; Chapter - 09: Information ethics and data governance; Chapter - 10: Information ethics and risk - Tools and methods for identifying and managing ethical risk; Chapter - 11: Data ethics - the bigger picture; Chapter - 12: And in conclusion...;
£42.74
Kogan Page Ltd Data Ethics
Book SynopsisKatherine O'Keefe works with Ireland's national water utility, Uisce Éireann, and is on the teaching faculty of the Law Society of Ireland's Diploma Centre. She has developed international professional accreditation schemes for information management, for which she was awarded the DAMA International Professional Achievement Award in 2017. Daragh O Brien is the founder and managing director of Castlebridge, a leading data strategyconsultancy. He lectures in Data Protection and Data Governance in the Sutherland School of Law at University College Dublin. Daragh is a founding member of the Strategic Advisory Board to the School of Business in Maynooth University.Trade Review"Ethics play an increasingly important role when considering how to collect and use personal data. This updated edition of Data Ethics clearly explains how to take ethics seriously and make it an integral part of business information management and governance. The combination of sound and up to date legal theories with practical tips and case studies makes it a useful handbook for anyone working with data on a regular basis." * Paul Breitbarth, Senior Visiting Fellow, European Centre on Privacy and Cybersecurity, Maastricht University *"In a world where AI is creating a growing wave of often dubious information, O Brien and O'Keefe's book should be mandatory reading for everybody in IT, media, regulatory bodies and beyond. This new edition of Data Ethics focuses on emerging topics of vital importance in a world where ethical decisions by IT may literally be, in the extreme, matters of life and death." * Dr Barry Devlin, Founder, 9 Sight Consulting and author of Business unIntelligence *"I can't think of a subject more relevant than data ethics. Given that we live in a data-dependent world, the most important question is not "Can I do something with data?" but "Should I do something with data?". These questions should be considered by teens learning to code, businesspeople gathering and exploiting customer data, scientists developing and releasing Artificial Intelligence (AI) applications, and anyone creating and using data. Daragh and Katherine provide an excellent groundwork for addressing these questions and give us the tools to think and act with our data in a responsible way. Read their book, share it and apply it!" * Danette McGilvray, President and Principal, Granite Falls Consulting, Inc. and author of Executing Data Quality Projects *"Reading Data Ethics gave me goosebumps. Impeccably researched, it is the definitive work on the topic. Simultaneously confronting and enlightening, it challenged my own ethical framework and validated the principles I hold dear in my practice as a Data Governance Executive. The foreword by John Ladley is delightful and sets the scene perfectly for what is to follow. I look forward to our DAMA community here in Australia, and internationally, having the opportunity to share their experiences after reading this outstanding book on data ethics." * Andrew Andrews, Data Governance Manager, ANZ Banking Group and Vice President of Marketing, DAMA International *Table of Contents Chapter - 00: Introduction - Why write a book on data ethics?; Chapter - 01: Ethics in the context of data management; Chapter - 02: Introduction to ethical concepts and frameworks; Chapter - 03: Ethical principles, standards and practice; Chapter - 04: Ethics, privacy and analytics; Chapter - 05: Ethics and data management (including AI); Chapter - 06: Developing an ethical architecture for information management; Chapter - 07: Introducing the Ethical Enterprise Information Management (E2IM) framework; Chapter - 08: Information ethics as an information quality system; Chapter - 09: Information ethics and data governance; Chapter - 10: Information ethics and risk - Tools and methods for identifying and managing ethical risk; Chapter - 11: Data ethics - the bigger picture; Chapter - 12: And in conclusion...;
£130.50
Kogan Page Ltd DataDriven HR
Book SynopsisBernard Marr is one of the leading voices in Technology and Innovation. A futurist and strategic performance consultant, he has advised many of the world's best-known organizations on their business and data strategies. A frequent keynote speaker, he also writes on the topic of data and analytics for various publications including Forbes and the Huffington Post. Bernard Marr is also the author of Data Strategy (2021) and The Intelligence Revolution (2020) published by Kogan Page.Trade Review"Without a doubt human capability (talent + leadership + organization + HR) increasingly delivers value to all stakeholders. This excellent book provides business and HR leaders the information required to improve decision making. Bernard's insights on analytics and AI will be the keys for progress." * Dave Ulrich, Rensis Likert Professor, Ross School of Business, University of Michigan Partner, The RBL Group *"If anyone was going to publish a book about the impact of the latest technology developments such as AI on the field of HR and People Analytics my bets were on Bernard Marr. And you won't be disappointed. The book offers a deep dive into the world of data of every kind, every possible use case, honest overview of technology and important considerations. It has never been more critical to educate ourselves about it." * Maja Luckos, VP, Employee Success, Salesforce *"This book propelled me into a world of possibilities for HR leaders in embracing the 'intelligence revolution' to shape people strategies that add value to their organizations and their people. It's enlightened me to the power of AI-enabled HR and how I might use it, and it's made me want to learn more. This is a must read for all HR leaders." * Linda Sleath, Group HR Director, Topps Tiles Plc. *"Data-Driven HR strikes a nice balance between exploring emerging trends in people analytics while primarily serving as a practical guide to HR professionals at any stage of their data journey. The second edition seamlessly weaves AI into a narrative that's easy to engage with and is packed full of examples that bring the theories to life." * Mark Ferrie, People Analytics Director, Meta *"Data-Driven HR is a terrific overview of the enormous world of people analytics and AI. For people trying to understand this important space, this book shows you the way." * Josh Bersin, Global Industry Analyst and CEO of The Josh Bersin Company *"Data, analytics and AI provides to elevate HR from its traditional role as a support function to one of a strategic partner creating value for the enterprise, its customers and its employees. There's a well-thumbed copy of the first edition of Data Driven HR on my bookshelf, and in this timely update Marr, one of the most knowledgeable people on the topic, explains how data and AI can enable HR to drive better decision making about people, deliver an enhanced service to employees; and make HR processes more efficient." * David Green, Managing Partner at Insight222, co-author of Excellence in People Analytics, and host of the Digital HR Leaders podcast. *"Bernard Marr has once again delivered an indispensable guide to harnessing the power of data, analytics and AI in HR. This updated edition thoroughly captures the latest innovations shaping human resources while still being accessible for HR professionals at any level. Through compelling examples and clear frameworks, Marr demonstrates how to drive business value through evidence-based talent practices. This is a must-read playbook for any HR leader looking to build capabilities in data-driven decision-making." * Professor Max Blumberg, PhD, University of Leeds *"This is a great guide for HR professionals who are grappling with the transition to becoming data led. It's easy to read, and with real examples and case studies across the employee lifecycle, it's also a pragmatic resource to have in your HR toolkit." * Ashish Sinha Korn Ferry Head of People Analytics, AI & Strategy EMEA Practice Leader *"AI is transforming the world of work and our personal lives. With a people-centric approach, Bernard Marr demystifies data driven AI enabled HR with context, thought provoking insights and examples of AI at the time this book was written. We all have a role to play when it comes to this rapidly evolving space as the output of AI will be a reflection of our culture and values. Staying on top of leading practices, lessons learned, emerging regulations and standards is critical so we can unlock AI's potential and value add to the business, our customers and employees while minimizing risk. This book sets the foundation so we can do just that!" * Terilyn Juarez Monroe, Terilyn Juarez Monroe, Chief People Officer *"Data-Driven HR is an indispensable resource for Career Services professionals looking to equip their students with cutting-edge strategies in today's competitive job market. This comprehensive book offers invaluable insights into recruitment and candidate selection, employer branding, pinpointing the most effective recruitment channels, and harnessing AI-enhanced automation to identify and assess the best candidates for businesses. It's a game-changer for career advisors committed to empowering their students with the knowledge and skills needed to excel in the evolving world of talent acquisition and HR." * Dr. Amber Wigmore Álvarez, Associate Professor, IE Business School and IE University *Table of Contents Chapter - 00: Preface; Section - ONE: Data, Analytics and AI in HR; Chapter - 01: How data and AI are transforming HR; Chapter - 02: How data and AI have come to revolutionise HR; Chapter - 03: The Data, Analytics and AI tools available to HR; Section - TWO: Data-Driven and AI-enabled HR in Practice; Chapter - 04: Better HR insights and decision-making; Chapter - 05: Recruitment and candidate selection; Chapter - 06: Employee Onboarding; Chapter - 07: Performance Monitoring and Management; Chapter - 08: Employee Training and Development; Chapter - 09: Performance monitoring and management; Chapter - 10: Identify the use cases; Chapter - 11: Building skills and aligning culture; Section - THREE: Making data-driven and AI enabled HR happen; Chapter - 12: Identifying the use cases for your organization; Chapter - 13: The future of HR
£87.30
Kogan Page Artificial Intelligence for Business
Book SynopsisKamales Lardi is a global thought leader on digital transformation and an expert on emerging technology solutions including AI, blockchain and IoT. She is CEO of Lardi & Partner Consulting and has advised many multinational companies across various industries in Europe, Asia and Africa. Kamales is Chair of the FORBES Business Council Women Executives, a Teaching Fellow at Durham University Business School and has been listed in the "Top 10 Global Influencers & Thought Leaders in Digital Transformation" (Thinkers360). She is based near Schwyz, Switzerland.
£60.30
Bristol University Press Data in Society
Book SynopsisThis book analyses societal trends and controversies related to developments in data ownership, access, construction, dissemination and interpretation, looking at the ways that society interacts with and uses statistical data.Table of ContentsBook Introduction ~ Humphrey Southall, Jeff Evans and Sally Ruane; 1: How Data are Changing; Introduction ~ Humphrey Southall and Jeff Evans; Statistical work: the changing occupational landscape ~ Kevin McConway; The creation and use of big administrative data ~ Harvey Goldstein and Ruth Gilbert Data Analytics ~ Ifan Shepherd and Gary Hearne; Social Media Data ~ Adrian Tear and Humphrey Southall; 2: Counting in a Globalised world; Introduction ~ Sally Ruane and Jeff Evans; Adult Skills Surveys and Transnational Organisations: Globalising Educational Policy ~ Jeff Evans; Poverty and health care surveys in the Global South: Towards making valid estimates ~ Roy Carr-Hill; Counting the Population in Need of International Protection Globally ~ Brad Blitz, Alessio D’Angelo and Eleonore Kofman; Tax justice and the challenges of measuring illicit financial flows ~ Richard Murphy; 3: The Changing Role of the State; Introduction ~ Sally Ruane and Humphrey Southall; The control and ‘fitness for purpose’ of UK official statistics ~ David Rhind; The Statistics of Devolution ~ David Byrne; Welfare reform: national policies with local impacts ~ Christina Beatty and Steve Fothergill; Social insecurity and the changing role of the (welfare) state: Public perceptions, social attitudes and political action ~ Christopher Deeming and Ron Johnston; Access to data and NHS privatisation: reducing public accountability ~ Sally Ruane; 4: Economic Life; Introduction ~ Humphrey Southall, Sally Ruane and Jeff Evans; The ‘distribution question’: the role of statistical analysis in measuring and evaluating trends in inequality ~ Stewart Lansley; Labour market statistics ~ Paul Bivand; The financial system ~ Rebecca Boden; The difficulty of building comprehensive tax avoidance data ~ Prem Sikka; Tax and spend decisions: did austerity improve financial numeracy and literacy? ~ David Walker; 5: Inequalities in Health and Well-being; Introduction ~ Sally Ruane and Humphrey Southall; Health Divides ~ Anonymous; Measuring Social Wellbeing ~ Roy Carr-Hill; Re-engineering health policy research to measure equity impacts ~ Tim Doran and Richard Cookson; The Generation Game: Ending the phony information war between young and old ~ Jay Ginn and Neil Duncan-Jordan; 6: Advancing social progress through critical statistical literacy; Introduction ~ Jeff Evans, Humphrey Southall and Sally Ruane; The Radical Statistics Group: Using Statistics for Progressive Social Change ~ Jeff Evans and Ludi Simpson; Lyme disease politics and evidence-based policy-making in the UK ~ Kate Bloor; Counting the uncounted: contestations over casualisation data in Australian universities ~ Nour Dados, James Goodman and Keiko Yasukawa; The quantitative crisis in UK Sociology ~ Malcolm Williams, Luke Sloan and Charlotte Brookfield; Critical Statistical Literacy and Interactive Data Visualisations ~ Jim Ridgway, James Nicholson, Sinclair Sutherland and Spencer Hedger; Full Fact ~ Amy Sippitt; What a difference a dataset makes? Data journalism and/as data activism ~ Jonathan Gray and Liliana Bounegru; Book Epilogue .
£81.59
Bristol University Press Data in Society
Book SynopsisThis book analyses societal trends and controversies related to developments in data ownership, access, construction, dissemination and interpretation, looking at the ways that society interacts with and uses statistical data.Table of ContentsBook Introduction ~ Humphrey Southall, Jeff Evans and Sally Ruane; 1: How Data are Changing; Introduction ~ Humphrey Southall and Jeff Evans; Statistical work: the changing occupational landscape ~ Kevin McConway; The creation and use of big administrative data ~ Harvey Goldstein and Ruth Gilbert Data Analytics ~ Ifan Shepherd and Gary Hearne; Social Media Data ~ Adrian Tear and Humphrey Southall; 2: Counting in a Globalised world; Introduction ~ Sally Ruane and Jeff Evans; Adult Skills Surveys and Transnational Organisations: Globalising Educational Policy ~ Jeff Evans; Poverty and health care surveys in the Global South: Towards making valid estimates ~ Roy Carr-Hill; Counting the Population in Need of International Protection Globally ~ Brad Blitz, Alessio D’Angelo and Eleonore Kofman; Tax justice and the challenges of measuring illicit financial flows ~ Richard Murphy; 3: The Changing Role of the State; Introduction ~ Sally Ruane and Humphrey Southall; The control and ‘fitness for purpose’ of UK official statistics ~ David Rhind; The Statistics of Devolution ~ David Byrne; Welfare reform: national policies with local impacts ~ Christina Beatty and Steve Fothergill; Social insecurity and the changing role of the (welfare) state: Public perceptions, social attitudes and political action ~ Christopher Deeming and Ron Johnston; Access to data and NHS privatisation: reducing public accountability ~ Sally Ruane; 4: Economic Life; Introduction ~ Humphrey Southall, Sally Ruane and Jeff Evans; The ‘distribution question’: the role of statistical analysis in measuring and evaluating trends in inequality ~ Stewart Lansley; Labour market statistics ~ Paul Bivand; The financial system ~ Rebecca Boden; The difficulty of building comprehensive tax avoidance data ~ Prem Sikka; Tax and spend decisions: did austerity improve financial numeracy and literacy? ~ David Walker; 5: Inequalities in Health and Well-being; Introduction ~ Sally Ruane and Humphrey Southall; Health Divides ~ Anonymous; Measuring Social Wellbeing ~ Roy Carr-Hill; Re-engineering health policy research to measure equity impacts ~ Tim Doran and Richard Cookson; The Generation Game: Ending the phony information war between young and old ~ Jay Ginn and Neil Duncan-Jordan; 6: Advancing social progress through critical statistical literacy; Introduction ~ Jeff Evans, Humphrey Southall and Sally Ruane; The Radical Statistics Group: Using Statistics for Progressive Social Change ~ Jeff Evans and Ludi Simpson; Lyme disease politics and evidence-based policy-making in the UK ~ Kate Bloor; Counting the uncounted: contestations over casualisation data in Australian universities ~ Nour Dados, James Goodman and Keiko Yasukawa; The quantitative crisis in UK Sociology ~ Malcolm Williams, Luke Sloan and Charlotte Brookfield; Critical Statistical Literacy and Interactive Data Visualisations ~ Jim Ridgway, James Nicholson, Sinclair Sutherland and Spencer Hedger; Full Fact ~ Amy Sippitt; What a difference a dataset makes? Data journalism and/as data activism ~ Jonathan Gray and Liliana Bounegru; Book Epilogue .
£30.39
O'Reilly Media Ethics of Big Data
Book SynopsisThis book contains a framework for productive discussion and thinking about ethics and Big Data in business environments. A framework provides you with a set of conceptual terms and tools that help decision-markers to engage difficult questions the expanding role Big Data plays in an increasing variety of products and services.
£13.59
O'Reilly Media Analyzing the Analyzers
Book Synopsis
£7.59
£9.45
Taylor & Francis Inc Data Clustering
Book SynopsisResearch on the problem of clustering tends to be fragmented across the pattern recognition, database, data mining, and machine learning communities. Addressing this problem in a unified way, Data Clustering: Algorithms and Applications provides complete coverage of the entire area of clustering, from basic methods to more refined and complex data clustering approaches. It pays special attention to recent issues in graphs, social networks, and other domains.The book focuses on three primary aspects of data clustering: Methods, describing key techniques commonly used for clustering, such as feature selection, agglomerative clustering, partitional clustering, density-based clustering, probabilistic clustering, grid-based clustering, spectral clustering, and nonnegative matrix factorization Domains, covering methods used for different domains of data, such as categorical data, text data, multimedia data, graph data, biologicTable of ContentsAn Introduction to Cluster Analysis. Feature Selection for Clustering: A Review. Probabilistic Models for Clustering. A Survey of Partitional and Hierarchical Clustering Algorithms. Density-Based Clustering. Grid-Based Clustering. Non-Negative Matrix Factorizations for Clustering: A Survey. Spectral Clustering. Clustering High-Dimensional Data. A Survey of Stream Clustering Algorithms. Big Data Clustering. Clustering Categorical Data. Document Clustering: The Next Frontier. Clustering Multimedia Data. Time Series Data Clustering. Clustering Biological Data. Network Clustering. A Survey of Uncertain Data Clustering Algorithms. Concepts of Visual and Interactive Clustering. Semi-Supervised Clustering. Alternative Clustering Analysis: A Review. Cluster Ensembles: Theory and Applications. Clustering Validation Measures. Educational and Software Resources for Data Clustering. Index.
£114.00
Edinburgh University Press Corpus Stylistics
Book SynopsisA beginner's guide to the corpus analysis of style in texts
£26.59
O'Reilly Media Data Analytics with Hadoop
Book SynopsisReady to use statistical and machine-learning techniques across large data sets? This practical guide shows you why the Hadoop ecosystem is perfect for the job.
£20.99
O'Reilly Media RealWorld Hadoop
Book SynopsisUsing real-world stories and situations, the authors show how NoSQL databases and Hadoop can solve a variety of business and research issues. They help you to learn about early decisions and pre-planning that can make the process easier and more productive.
£15.99
O'Reilly Media Practical Machine Learning with H20
Book SynopsisThis hands-on guide teaches you how to use H20 with only minimal math and theory behind the learning algorithms.
£29.99
O'Reilly Media The Practitioners Guide to Graph Data
Book SynopsisGraph data closes the gap between the way humans and computers view the world. While computers rely on static rows and columns of data, people navigate and reason about life through relationships. This practical guide demonstrates how graph data brings these two approaches together.
£47.99
O'Reilly Media Cloud Native DataCenter Networking
Book SynopsisWhether you're pursuing a multitenant private cloud, a network for running machine learning, or an enterprise data center, author Dinesh Dutt takes you through the steps necessary to design a data center that's affordable, high capacity, easy to manage, agile, and reliable.
£42.39
O'Reilly Media Mastering Shiny
Book SynopsisHadley Wickham from RStudio shows data scientists, data analysts, statisticians, and scientific researchers with no knowledge of HTML, CSS, or JavaScript how to create rich web apps from R.
£51.19
O'Reilly Media Machine Learning Pocket Reference
Book SynopsisWith detailed notes, tables, and examples, this handy reference will help you navigate the basics of structured machine learning. Author Matt Harrison delivers a valuable guide that you can use for additional support during training and as a convenient resource when you dive into your next machine learning project.
£20.39
O'Reilly Media The SelfService Data Roadmap
Book SynopsisData-driven insights are a key competitive advantage for any industry today, but deriving insights from raw data can still take days or weeks. With this practical book, data engineers, data scientists, and team managers will learn how to build a self-service data science platform that helps anyone in your organization extract insights from data.
£42.39
O'Reilly Media Tableau Prep Up and Running
Book SynopsisCarl Allchin from The Information Lab in London gets you up to speed on Tableau Prep through a series of practical lessons that include methods for preparing, cleaning, automating, organizing, and outputting your datasets.
£51.19
O'Reilly Media HandsOn Data Visualization
Book SynopsisTell your story and show it with data, using free and easy-to-learn tools on the web. This introductory book teaches you how to design interactive charts and customized maps for your website, beginning with simple drag-and-drop tools such as Google Sheets, Datawrapper, and Tableau Public.
£42.39
O'Reilly Media Colorwise
Book SynopsisWith this book, author and DATAcated founder Kate Strachnyi provides the ultimate guide to the correct use of color for representing data in graphs, charts, tables, and infographics.
£21.74
O'Reilly Media CostEffective Data Pipelines
Book SynopsisWith this practical guide, author Sev Leonard provides a holistic approach to designing scalable data pipelines in the cloud. Intermediate data engineers, software developers, and architects will learn how to navigate cost/performance trade-offs and how to choose and configure compute and storage.
£42.39
University of Nebraska Press When Big Data Was Small
Book SynopsisRichard D. Cramer started analysing baseball statistics as a hobby in the mid-1960s, not long after graduating from Harvard and MIT. In When Big Data Was Small Cramer recounts his life and remarkable contributions to baseball knowledge.Trade Review"When Big Data Was Small is one of the most consequential books on baseball history and the evolution of thinking on the game."—Jason Schott, Brooklyn Digest“Dick was one of a handful of people back in the 1970s who started the statistical revolution in baseball . . . in his spare time. He was also a respected scientist with a distinguished career, and he played a little jazz on the side. This book chronicles his life, with its ups and downs, both professional and personal, in an honest and unassuming way. It is an interesting journey, with the last chapter yet to be written.”—Pete Palmer, coauthor of The Hidden Game of Baseball: A Revolutionary Approach to Baseball and Its StatisticsTable of ContentsContentsForeword by John ThornAcknowledgmentsIntroduction1. Setting the Stage2. Baseball and Science Surface3. College4. Graduate School and the 1960s Computer5. Industrial Synthetic Chemist6. Harvard’s Research Computer7. Computer-Aided Drug Discovery8. Sabermetrics’ Infancy9. Scientific Recognition10. Twists of Fate11. Birth of STATS Inc.12. White Sox and Yankees13. Scientific Career Transition14. Rebirth of STATS Inc.15. Comparative Molecular Field Analysis16. STATS Soars17. Cheerlessness and Lyme Disease19. The Rise and Fall of TRPS19. Repudiated by STATS20. Tidying Up21. In My Humble Opinion22. Summing UpAppendix: Bamberg Mathematical Analysis of BaseballNotesBibliographyIndex
£21.84
SAGE Publications Inc Qualitative and Mixed Methods Data Analysis Using
Book SynopsisQualitative and Mixed Methods Data Analysis Using Dedoose®: A Practical Approach for Research Across the Social Sciences provides both new and experienced researchers with a guided introduction to dealing with the methodological complexity of mixed methods and qualitative inquiry using Dedoose® software. The authors use their depth of experience designing and updating Dedoose® as well as their published research to give the reader practical strategies for using Dedoose® from a wide range of research studies. Case study contributions by outside researchers provide readers with rich examples of how to use Dedoose® in practical, applied social science and health settings. Trade Review"Extremely helpful information that will inspire and educate both those who are just learning and those who have been using Dedoose® for years." -- Julie Kugel"Great overview of Dedoose® tools and mixed methods functionality make this a great book for beginners." -- Shaunna SmithTable of ContentsForeword by Lyn Richards Preface Acknowledgments Glossary: Dedoose Common Terms About the Authors PART I FOUNDATIONS OF MIXED METHODS RESEARCH Chapter 1 Using Mixed Methods and Dedoose 1.1 About This Book 1.2 Mixed Methods and Mixed Paradigms 1.3 Using Cloud Technology to Support Mixed Methods Research 1.4 What Is Dedoose? 1.5 Dedoose: A Historical Journey Chapter 2 Adopting Dedoose 2.1 Successful Adoption of Digital Tools 2.2 Framing the Purpose and Focus 2.3 Dedoose: Starting Your Project 2.4 Case Study: Using the Five-Level QDA® Method With Dedoose Chapter 3 Bringing Data Into Dedoose 3.1 Gathering Mixed Data 3.2 Numbers as Data 3.3 Memos as Data 3.4 Case Study: Incorporating Mixed Analysis Into Your Study 3.5 Conclusion Appendix: Types of Interview Data PART II DATA INTERACTION AND ANALYSIS Chapter 4 Teamwork Analysis Techniques 4.1 Team Management 4.2 Collaborative Interpretations 4.3 Coding in Teams 4.4 Bringing Procedures Into the Dedoose Environment 4.5 Team Conduct Rules 4.6 Case Study: Large-Scale, Multilanguage, Cross-Cultural Analysis With Dedoose 4.7 Conclusion Chapter 5 Qualitative Analysis 5.1 Qualitative Analysis: Looking for Quality 5.2 Working With Codes 5.3 Case Study: Using Dedoose for a Multisite Study 5.4 Conclusion Chapter 6 Designing Mixed Methods Analysis 6.1 Identifying Analysis Strategies 6.2 Using Descriptors 6.3 Topic Modeling 6.4 Case Study: Integrating Mixed Data in a Longitudinal Study 6.5 Conclusion Chapter 7 Managing Complex Mixed Methods Analysis 7.1 Recognizing and Managing Complexity in Analysis 7.2 Data Complexity in Your Project 7.3 Using Visualization Tools for Analysis 7.4 Moving Through and Filtering Your Data 7.5 Case Study: Complex Yet Manageable—the Organizational Genius of Dedoose 7.6 Conclusion Chapter 8 Working With Numbers in Dedoose: Statistics, Tabling, and Charting for Numbers, Weights, and Option List Field Data 8.1. Background/Introduction 8.2 Charts, Tables, and Plots for Individual Fields or Code Weights 8.3 Charts, Tables, Plots, and Analyses for Pairs of Fields/Code Weights 8.4 Summary PART III REPORTING CREDIBLE RESULTS AND SHARING FINDINGS Chapter 9 Reporting Your Findings 9.1 Reaching Your Audience 9.2 Mixed Methods Procedural Checklist 9.3 Case Study: Reporting to Multiple Audiences Chapter 10 Sharing Data With a Larger Audience 10.1 Reaching a Larger Audience 10.2 Case Study: Sharing Qualitative Social Science Data 10.3 Changing Reporting Practices: Open Access 10.4 Final Word Closing Remarks by Thomas S. Weisner References Index
£50.00
De Gruyter Quick Start Guide to Azure Data Factory, Azure
Book SynopsisWith constantly expanding options such as Azure Data Lake Server (ADLS) and Azure SQL Data Warehouse (ADW), how can developers learn the process and components required to successfully move this data? Quick Start Guide to Azure Data Factory, Azure Data Lake Server, and Azure Data Warehouse teaches you the basics of moving data between Azure SQL solutions using Azure Data Factory. Discover how to build and deploy each of the components needed to integrate data in the cloud with local SQL databases. Mark Beckner's step by step instructions on how to build each component, how to test processes and debug, and how to track and audit the movement of data, will help you to build your own solutions instantly and efficiently. This book includes information on configuration, development, and administration of a fully functional solution and outlines all of the components required for moving data from a local SQL instance through to a fully functional data warehouse with facts and dimensions.
£15.99
De Gruyter Big Data Analytics Methods: Analytics Techniques
Book SynopsisBig Data Analytics Methods unveils secrets to advanced analytics techniques ranging from machine learning, random forest classifiers, predictive modeling, cluster analysis, natural language processing (NLP), Kalman filtering and ensembles of models for optimal accuracy of analysis and prediction. More than 100 analytics techniques and methods provide big data professionals, business intelligence professionals and citizen data scientists insight on how to overcome challenges and avoid common pitfalls and traps in data analytics. The book offers solutions and tips on handling missing data, noisy and dirty data, error reduction and boosting signal to reduce noise. It discusses data visualization, prediction, optimization, artificial intelligence, regression analysis, the Cox hazard model and many analytics using case examples with applications in the healthcare, transportation, retail, telecommunication, consulting, manufacturing, energy and financial services industries. This book's state of the art treatment of advanced data analytics methods and important best practices will help readers succeed in data analytics.Table of ContentsIntroduction PART I: Big Data Analytics Chapter 1. Data Analytics Overview Chapter 2. Basic Data Analysis Chapter 3. Data Visualization Tools PART II: Advanced Analytics Methods Chapter 4. Natural Language Processing Chapter 5. Quantitative Analysis - Prediction and Prognostics Chapter 6. Advanced Analytics & Predictive Modeling Chapter 7. Ensemble of Models Chapter 8. Machine Learning, Deep Learning – Artificial Neural Networks Chapter 9. Model Accuracy & Optimization PART III: Case Study – Prediction & Advanced Analytics in Practice Chapter 10: Ensemble of Models – Medical Prediction Case Study Appendix A: Prognostics Methods Appendix B: A Neural Network Example Appendix C: Back Propagation Algorithm Derivation Appendix D: NeuroSolutions Software Description Appendix E: The Oracle Program References
£48.38
Nova Science Publishers Inc Cognitive Sciences Research Progress
Book SynopsisThis book presents new research on cognitive science which is most simply defined as the scientific study either of mind or of intelligence. It is an interdisciplinary study drawing from relevant fields including psychology, philosophy, neuroscience, linguistics, anthropology, computer science, biology, and physics. There are several approaches to the study of cognitive science. These approaches may be classified broadly as symbolic, connectionist, and dynamic systems. Symbolic holds that cognition can be explained using operations on symbols, by means of explicit computational theories and models of mental (but not brain) processes analogous to the workings of a digital computer. Connectionist (subsymbolic) holds that cognition can only be modelled and explained by using artificial neural networks on the level of physical brain properties. Hybrid systems hold that cognition is best modelled using both connectionist and symbolic models, and possibly other computational techniques. Dynamic Systems hold that cognition can be explained by means of a continuous dynamical system in which all the elements are interrelated, like the Watt Governor. The essential questions of cognitive science seem to be: What is intelligence? and How is it possible to model it computationally?
£63.74
Manning Publications Storm Applied:Strategies for real-time event
Book Synopsis It's a lot harder to make sense out of data when it's coming at full speed. Apache Storm’s efficient stream processing capabilities are relied upon by giants like Twitter and Yahoo for swiftly extracting intelligence from their Big Data streams. Fault tolerant guarantees of Storm make it an invaluable and versatile platform in the Big Data landscape. It integrates seamlessly with battle-tested message queuing systems (like Kafka) and NoSQL databases (like Cassandra). Storm is built to run on the JVM but provides straightforward extensions for working with non-JVM languages like Ruby and Python. Storm Applied is a practical guide to using Apache Storm for the real-world tasks associated with processing and analyzing real-time data streams. The book starts by building a solid foundation of the Storm essentials. Then, it quickly dives into real-world case studies that will bring the novice up to speed with productionizing Storm: the knowledge needed to scale a high throughput stream processor and ensure smooth operation within a production cluster. It moves on to teach readers how to use Trident to treat streams as batches for solving a different class of problems, and covers the tools available within the Storm open source community that are crucial for any seasoned Storm developer. RETAIL SELLING POINTS Immediately useful practical guide Applies Storm to real-world use cases Takes Storm from development to a fully tuned and optimized production setup AUDIENCE While prior experience with Storm is not necessary, acquaintance with related Big Data problem solving is helpful. Basic understanding of Java or similar JVM language and concurrency is assumed. DESCRIBE THE TECHNOLOGY Storm is a tool that can be used for processing "big data" in real-time. Think performing real-time analysis of all the tweets going through Twitter.
£39.99
Manning Publications Real-World Machine Learning
Book SynopsisDESCRIPTION In a world where big data is the norm and near-real-time decisions are crucial, machine learning (ML) is a critical component of the data workflow. Machine learning systems can quickly crunch massive amounts of information to offer insights and make decisions in a way that matches or even surpasses human cognitive abilities. These systems use sophisticated computational and statistical tools to build models that can recognize and visualize patterns, predict outcomes, forecast values, and make recommendations. Real-World Machine Learning is a practical guide designed to teach developers the art of ML project execution. The book introduces the day-to-day practice of machine learning and prepares readers to successfully build and deploy powerful ML systems. Using the Python language and the R statistical package, it starts with core concepts like data acquisition and modeling, classification, and regression. Then it moves through the most important ML tasks, like model validation, optimization and feature engineering. It uses real-world examples that help readers anticipate and overcome common pitfalls. Along the way, they will discover scalable and online algorithms for large and streaming data sets. Advanced readers will appreciate the in-depth discussion of enhanced ML systems through advanced data exploration and pre-processing methods. KEY FEATURES Accessible and practical introduction to machine learning Contains big-picture ideas and real-world examples Prepares reader to build and deploy powerful predictive systems Offers tips & tricks and highlights common pitfalls AUDIENCE Code examples are in Python and R. No prior machine learning experience required. ABOUT THE TECHNOLOGY Machine learning has gained prominence due to the overwhelming successes of Google, Microsoft, Amazon, LinkedIn, Facebook, and others in their use of ML. The Gartner report predicts that big data analytics will be a $25 billion market by 2017, and financial firms, marketing organizations, scientific facilities, and Silicon Valley startups are all demanding machine learning skills from their developers.
£35.99
Manning Publications Hadoop in Practice
Book Synopsis For developers working with big data, it's not enough to have a theoretical understanding of Hadoop. They need to solve real challenges like analyzing real-time streams, moving data securely between storage systems, and managing large-scale clusters. The Hadoop ecosystem is constantly growing, and it's important they keep up with the new technologies and practices to stay productive and future-proof data systems. Hadoop in Practice, Second Edition provides over 100 tested, instantly-useful techniques that will help conquer big data, using Hadoop. This revised new edition covers changes and new features in the Hadoop core architecture, including MapReduce 2. Brand new chapters cover YARN, real-time use cases, and integrating Kafka, Storm, and Spark with Hadoop. There’s also a new and updated techniques for Flume, Sqoop, and Mahout, all of which have seen major new versions recently. In short, this is the most practical, up-to-date coverage of Hadoop available anywhere. RETAIL SELLING POINTS Practical up-to-date coverage Over 100 practical, battle-tested Hadoop techniques Major updates to key technologies AUDIENCE Readers should be familiar with Hadoop and have experience programming in Java or another OOP language. ABOUT THE TECHNOLOGY Hadoop is an open source MapReduce platform designed to query and analyze data distributed across large clusters. Especially effective for big data systems, Hadoop powers mission-critical software at Apple, eBay, LinkedIn, Yahoo, and Facebook. It offers organizations efficient ways to store, manage, and analyze data.
£39.99
Manning Publications Data Munging with R
Book SynopsisData Munging with R shows readers how to take raw data and transform it for use in computations, tables, graphs, and more. Whether they already have some programming experience or they’re just a spreadsheet whiz looking for a more powerful data manipulation tool, this book will help programmers get started. Readers will discover the ins and outs of using the data-oriented R programming language and its many task-specific packages. By the end, readers will be master mungers, with a robust, reproducible workflow and the skills to use data to strengthen their conclusions! Key Features • Practical examples • Step-by-step guide • Introduction to R Audience If you have beginner programming skills or you're comfortable with writing spreadsheet formulas, you have everything you need to get the most out of this book. About the technology R is a statistical programming language in that it was made for the purpose of performing statistics calculations, but it has grown to be so much more through community contributions. As a general purpose language, it is flexible enough to work with almost any data you can interact with; stored or streaming, images, text, or numbers.
£39.99
Manning Publications Data Wrangling with JavaScript
Book SynopsisWith a growing ecosystem of tools and libraries available, and the flexibility to run on many platforms (web, desktop and mobile), JavaScript is a terrific all-round environment for all data wrangling needs! Data Wrangling with JavaScript teaches readers core data munging techniques in JavaScript, along with many libraries and tools that will make their data tasks even easier. Key Features · How to handle unusual data sets · Cleaning and preparing raw data · Visualizing your results Audience Written for developers with experience using JavaScript. No prior knowledge of data analytics is needed. Author Bio Ashley Davis is a software developer, entrepreneur, writer, and a stock trader. He is the creator of Data-Forge, a data transformation and analysis toolkit for JavaScript inspired by Pandas and Microsoft LINQ.
£39.99
Manning Publications Modern Fortran:Building Efficient Parallel
Book SynopsisModern Fortran is natively parallel, so it's uniquely suited for efficiently handling problems like complex simulations, long-range predictions, and ultra-precise designs. If you're working on tasks where speed, accuracy, and efficiency matter, it's time to discover—or rediscover— Fortran. Modern Fortran: Building efficient parallel applications teaches you how to develop fast, efficient parallel applications with Fortran, an amazingly powerful and flexible programming language that forms the foundation of high performance computing for research, science and industry. Key Features · Parallel programming from the ground up · Working with variables and types · Module development in Fortran · Fortran with C, C++, and Python · Object-oriented and functional programming styles Written for developers, engineers, and computational scientists. No prior experience with Fortran in needed. About the technology Fortran is a general-purpose programming language that is dominant in engineering and scientific computing applications. It’s the leading language used in high performance (HPC) and parallel programming. Milan Curcic is a meteorologist and oceanographer. A Fortran programmer since 2006, he has worked with teams from United States Navy and NASA on developing and improving Earth system prediction models. Milan has authored two general-purpose Fortran libraries and is currently working on a startup porting Fortran to the cloud for weather and ocean prediction.
£47.99
Manning Publications Algorithms and Data Structures for Massive
Book SynopsisMassive modern datasets make traditional data structures and algorithms grind to a halt. This fun and practical guide introduces cutting-edge techniques that can reliably handle even the largest distributed datasets.In Algorithms and Data Structures for Massive Datasets you will learn: Probabilistic sketching data structures for practical problems Choosing the right database engine for your application Evaluating and designing efficient on-disk data structures and algorithms Understanding the algorithmic trade-offs involved in massive-scale systems Deriving basic statistics from streaming data Correctly sampling streaming data Computing percentiles with limited space resources Algorithms and Data Structures for Massive Datasets reveals a toolbox of new methods that are perfect for handling modern big data applications. You’ll explore the novel data structures and algorithms that underpin Google, Facebook, and other enterprise applications that work with truly massive amounts of data. These effective techniques can be applied to any discipline, from finance to text analysis. Graphics, illustrations, and hands-on industry examples make complex ideas practical to implement in your projects—and there’s no mathematical proofs to puzzle over. Work through this one-of-a-kind guide, and you’ll find the sweet spot of saving space without sacrificing your data’s accuracy. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Standard algorithms and data structures may become slow—or fail altogether—when applied to large distributed datasets. Choosing algorithms designed for big data saves time, increases accuracy, and reduces processing cost. This unique book distills cutting-edge research papers into practical techniques for sketching, streaming, and organizing massive datasets on-disk and in the cloud. About the book Algorithms and Data Structures for Massive Datasets introduces processing and analytics techniques for large distributed data. Packed with industry stories and entertaining illustrations, this friendly guide makes even complex concepts easy to understand. You’ll explore real-world examples as you learn to map powerful algorithms like Bloom filters, Count-min sketch, HyperLogLog, and LSM-trees to your own use cases. What's inside Probabilistic sketching data structures Choosing the right database engine Designing efficient on-disk data structures and algorithms Algorithmic tradeoffs in massive-scale systems Computing percentiles with limited space resources About the reader Examples in Python, R, and pseudocode. About the author Dzejla Medjedovic earned her PhD in the Applied Algorithms Lab at Stony Brook University, New York. Emin Tahirovic earned his PhD in biostatistics from University of Pennsylvania. Illustrator Ines Dedovic earned her PhD at the Institute for Imaging and Computer Vision at RWTH Aachen University, Germany. Table of Contents 1 Introduction PART 1 HASH-BASED SKETCHES 2 Review of hash tables and modern hashing 3 Approximate membership: Bloom and quotient filters 4 Frequency estimation and count-min sketch 5 Cardinality estimation and HyperLogLog PART 2 REAL-TIME ANALYTICS 6 Streaming data: Bringing everything together 7 Sampling from data streams 8 Approximate quantiles on data streams PART 3 DATA STRUCTURES FOR DATABASES AND EXTERNAL MEMORY ALGORITHMS 9 Introducing the external memory model 10 Data structures for databases: B-trees, Bε-trees, and LSM-trees 11 External memory sorting
£45.39
Manning Publications How to Lead in Data Science
Book SynopsisTo lead a data science team, you need to expertly articulate technology roadmaps, support a data-driven culture, and plan a data strategy that drives a competitive business plan. In this practical guide, you'll learn leadership techniques the authors have developed building multiple high-performance data teams. In How to Lead in Data Science you'll master techniques for leading data science at every seniority level, from heading up a single project to overseeing a whole company's data strategy. You'll find advice on plotting your long-term career advancement, as well as quick wins you can put into practice right away. Throughout, carefully crafted assessments and interview scenarios encourage introspection, reveal personal blind spots, and show development areas to help advance your career. Leading a data science team takes more than the typical set of business management skills. You need specific know-how to articulate technology roadmaps, support a data-driven culture, and plan a data strategy that drives a competitive business plan. Whether you're looking to manage your team better or work towards a seat at your company's top leadership table, this book will show you how. Trade Review“Improveleadership skills, irrespective of the domain you are in.” Vishwesh RaviShrimali “Whether you are new to managing, new to data science, or just want tobe a better advocate for your data team there are a lot of tips to improve yourpractice.” MichaelPetrey “This is a book that surpasses the boundaries of mining data and coding,but warns you about not forgetting them in the effort to successfully lead datascience teams.” JesúsJuárez-Guerrero “Excellent book. Covers a large complex topic in a clear and understandableway.” GaryBake “Excellent and ambitious book that provides actionable insight on how tolead in data science. Filled with insightful vignettes, anecdotes, and casestudies to bring life and relevance to the frameworks and discussion.” MarcParadis
£37.99
Manning Publications Time Series Forecasting in Python
Book SynopsisBuild predictive models from time-based patterns in your data. Master statistical models including new deep learning approaches for time series forecasting. In Time Series Forecasting in Python you will learn how to: Recognize a time series forecasting problem and build a performant predictive model Create univariate forecasting models that account for seasonal effects and external variables Build multivariate forecasting models to predict many time series at once Leverage large datasets by using deep learning for forecasting time series Automate the forecasting process DESCRIPTION Time Series Forecasting in Python teaches you to build powerful predictive models from time-based data. Every model you create is relevant, useful, and easy to implement with Python. You'll explore interesting real-world datasets like Google's daily stock price and economic data for the USA, quickly progressing from the basics to developing large-scale models that use deep learning tools like TensorFlow.Time Series Forecasting in Python teaches you to apply time series forecasting and get immediate, meaningful predictions. You'll learn both traditional statistical and new deep learning models for time series forecasting, all fully illustrated with Python source code. Time Series Forecasting in Python teaches you to build powerful predictive models from time-based data. Every model you create is relevant, useful, and easy to implement with Python. You'll explore interesting real-world datasets like Google's daily stock price and economic data for the USA, quickly progressing from the basics to developing large-scale models that use deep learning tools like TensorFlow. about the technology Time series forecasting reveals hidden trends and makes predictions about the future from your data. This powerful technique has proven incredibly valuable across multiple fields—from tracking business metrics, to healthcare and the sciences. Modern Python libraries and powerful deep learning tools have opened up new methods and utilities for making practical time series forecasts. about the book Time Series Forecasting in Python teaches you to apply time series forecasting and get immediate, meaningful predictions. You'll learn both traditional statistical and new deep learning models for time series forecasting, all fully illustrated with Python source code. Test your skills with hands-on projects for forecasting air travel, volume of drug prescriptions, and the earnings of Johnson & Johnson. By the time you're done, you'll be ready to build accurate and insightful forecasting models with tools from the Python ecosystem.Table of Contentstable of contents detailed TOC PART 1: TIME WAITS FOR NO ONE READ IN LIVEBOOK 1UNDERSTANDING TIME SERIES FORECASTING READ IN LIVEBOOK 2A NAÏVE PREDICTION OF THE FUTURE READ IN LIVEBOOK 3GOING ON A RANDOM WALK PART 2: FORECASTING WITH STATISTICAL MODELS READ IN LIVEBOOK 4MODELING A MOVING AVERAGE PROCESS READ IN LIVEBOOK 5MODELING AN AUTOREGRESSIVE PROCESS READ IN LIVEBOOK 6MODELING COMPLEX TIME SERIES READ IN LIVEBOOK 7FORECASTING NON-STATIONARY TIME SERIES READ IN LIVEBOOK 8ACCOUNTING FOR SEASONALITY READ IN LIVEBOOK 9ADDING EXTERNAL VARIABLES TO OUR MODEL READ IN LIVEBOOK 10FORECASTING MULTIPLE TIME SERIES READ IN LIVEBOOK 11CAPSTONE: FORECASTING THE NUMBER OF ANTIDIABETIC DRUG PRESCRIPTIONS IN AUSTRALIA PART 3: LARGE-SCALE FORECASTING WITH DEEP LEARNING READ IN LIVEBOOK 12INTRODUCING DEEP LEARNING FOR TIME SERIES FORECASTING READ IN LIVEBOOK 13DATA WINDOWING AND CREATING BASELINES FOR DEEP LEARNING READ IN LIVEBOOK 14BABY STEPS WITH DEEP LEARNING READ IN LIVEBOOK 15REMEMBERING THE PAST WITH LSTM READ IN LIVEBOOK 16FILTERING OUR TIME SERIES WITH CNN READ IN LIVEBOOK 17USING PREDICTIONS TO MAKE MORE PREDICTIONS READ IN LIVEBOOK 18CAPSTONE: FORECASTING THE ELECTRIC POWER CONSUMPTION OF A HOUSEHOLD PART 4: AUTOMATING FORECASTING AT SCALE READ IN LIVEBOOK 19AUTOMATING TIME SERIES FORECASTING WITH PROPHET READ IN LIVEBOOK 20CAPSTONE: FORECASTING THE MONTHLY AVERAGE RETAIL PRICE OF STEAK IN CANADA 21 GOING ABOVE AND BEYOND APPENDIX APPENDIX A: INSTALLATION INSTRUCTIONS
£41.39
Manning Publications DataDriven Cybersecurity
£43.95
Manning Publications The WellGrounded Data Analyst
Book Synopsis
£45.05
Manning Publications Statistics Playbook
Learn statistics by analysing professional basketball data! Statistics Slam Dunk is an action-packed book that will help you build your skills in exploratory data analysis by digging into the fascinating world of NBA games and player stats using the R language. This textbook will upgrade your R data science skills by taking on practical analysis challenges based on NBA game and player data. You will take on the challenge of wrangling messy data to drill on the skills that will make you the star player on any data team. And just like in the real world, you will get no clean pre-packaged datasets in this book. You will develop a toolbox of R data skills including: Reading and writing data Installing and loading packages Transforming, tidying, and wrangling data Applying best-in-class exploratory data analysis techniques Creating compelling visualizations Developing supervised and unsupervised machine learning algorithms Execute hypothesis tests, including t-tests and chi-square tests for independence Compute expected values, Gini coefficients, and z-scores Is losing games on purpose a rational strategy? Which hustle statistics have an impact on wins and losses? Each chapter in this one-of-a-kind guide uses new data science techniques to reveal interesting insights like these. About the technology Amazing insights are hiding in raw data, and statistical analysis with R can help reveal them! R was built for data, and it supports modelling and statistical techniques including regression and classification models, time series forecasts, and clustering algorithms. And when you want to see your results, R's visualisations are stunning, with best-in-class plots and charts.
£42.39
States Academic Press Data Analytics: Techniques and Applications
Book Synopsis
£106.72
Information Age Publishing Advances in Latent Class Analysis: A Festschrift
Book SynopsisWhat is latent class analysis? If you asked that question thirty or forty years ago you would have gotten a different answer than you would today. Closer to its time of inception, latent class analysis was viewed primarily as a categorical data analysis technique, often framed as a factor analysis model where both the measured variable indicators and underlying latent variables are categorical. Today, however, it rests within much broader mixture and diagnostic modeling framework, integrating measured and latent variables that may be categorical and/or continuous, and where latent classes serve to define the subpopulations for whom many aspects of the focal measured and latent variable model may differ.For latent class analysis to take these developmental leaps required contributions that were methodological, certainly, as well as didactic. Among the leaders on both fronts was C. Mitchell “Chan” Dayton, at the University of Maryland, whose work in latent class analysis spanning several decades helped the method to expand and reach its current potential. The current volume in the Center for Integrated Latent Variable Research (CILVR) series reflects the diversity that is latent class analysis today, celebrating work related to, made possible by, and inspired by Chan’s noted contributions, and signaling the even more exciting future yet to come.
£47.45
Information Age Publishing Advances in Latent Class Analysis: A Festschrift
Book SynopsisWhat is latent class analysis? If you asked that question thirty or forty years ago you would have gotten a different answer than you would today. Closer to its time of inception, latent class analysis was viewed primarily as a categorical data analysis technique, often framed as a factor analysis model where both the measured variable indicators and underlying latent variables are categorical. Today, however, it rests within much broader mixture and diagnostic modeling framework, integrating measured and latent variables that may be categorical and/or continuous, and where latent classes serve to define the subpopulations for whom many aspects of the focal measured and latent variable model may differ.For latent class analysis to take these developmental leaps required contributions that were methodological, certainly, as well as didactic. Among the leaders on both fronts was C. Mitchell “Chan” Dayton, at the University of Maryland, whose work in latent class analysis spanning several decades helped the method to expand and reach its current potential. The current volume in the Center for Integrated Latent Variable Research (CILVR) series reflects the diversity that is latent class analysis today, celebrating work related to, made possible by, and inspired by Chan’s noted contributions, and signaling the even more exciting future yet to come.
£82.80
NY Research Press Intelligent Data Analysis: Challenges and
Book Synopsis
£111.54
Clanrye International Introduction to Data Mining
Book Synopsis
£107.38
Willford Press Intelligent Data Analysis: From Data Gathering to
Book Synopsis
£117.37
Information Age Publishing Innovative Psychometric Modeling and Methods
Book SynopsisThe general theme of this book is to present innovative psychometric modeling and methods. In particular, this book includes research and successful examples of modeling techniques for new data sources from digital assessments, such as eye-tracking data, hint uses, and process data from game-based assessments. In addition, innovative psychometric modeling approaches, such as graphical models, item tree models, network analysis, and cognitive diagnostic models, are included. Chapters 1, 2, 4 and 6 are about psychometric models and methods for learning analytics. The first two chapters focus on advanced cognitive diagnostic models for tracking learning and the improvement of attribute classification accuracy. Chapter 4 demonstrates the use of network analysis for learning analytics. Chapter 6 introduces the conjunctive root causes model for the understanding of prerequisite skills in learning. Chapters 3, 5, 8, 9 are about innovative psychometric techniques to model process data. Specifically, Chapters 3 and 5 illustrate the usage of generalized linear mixed effect models and item tree models to analyze eye-tracking data. Chapter 8 discusses the modeling approach of hint uses and response accuracy in learning environment. Chapter 9 demonstrates the identification of observable outcomes in the game-based assessments. Chapters 7 and 10 introduce innovative latent variable modeling approaches, including the graphical and generalized linear model approach and the dynamic modeling approach. In summary, the book includes theoretical, methodological, and applied research and practices that serve as the foundation for future development. These chapters provide illustrations of efforts to model and analyze multiple data sources from digital assessments. When computer-based assessments are emerging and evolving, it is important that researchers can expand and improve the methods for modeling and analyzing new data sources. This book provides a useful resource to researchers who are interested in the development of psychometric methods to solve issues in this digital assessment age.Table of Contents Advances in Psychometric Methods for Uncovering Latent Structure and Cognitive Processes Improving Attribute Classification Accuracy in High Dimensional Data: A Four-Step Latent Regression Approach A Dynamic Generalized Mixed Effect Model for Intensive Binary Temporal-Spatio Data From an Eye-Tracking Technique Application of Network Analysis in Understanding Collaborative Problem Solving Processes and Skills IRTree Modeling of Cognitive Processes Based on Outcome and Intermediate Data Prerequisite Structure Finding Using the Conjunctive Root Causes Model A Graphical and Generalized Linear Model Approach to Latent Variable Modeling Modeling Hint Requests, Response Times, and Response Accuracy in Adaptive Learning Systems Identifying Observable Outcomes in Game-Based Assessments A Regime-Switching (RS) Framework for Formulating Multi-Phase Linear and Nonlinear Growth Curve Models About the Editors.
£69.00
