The 9 Pitfalls Of Data Science
DOWNLOAD
Download The 9 Pitfalls Of Data Science PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get The 9 Pitfalls Of Data Science book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
The 9 Pitfalls Of Data Science
DOWNLOAD
Author : Gary Smith
language : en
Publisher:
Release Date : 2019
The 9 Pitfalls Of Data Science written by Gary Smith and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with Computers categories.
The 9 Pitfalls of Data Science is loaded with entertaining tales of both successful and misguided approaches to interpreting data, both grand successes and epic failures.
The 9 Pitfalls Of Data Science
DOWNLOAD
Author : Gary Smith
language : en
Publisher: Oxford University Press
Release Date : 2019-07-08
The 9 Pitfalls Of Data Science written by Gary Smith and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-08 with Computers categories.
Data science has never had more influence on the world. Large companies are now seeing the benefit of employing data scientists to interpret the vast amounts of data that now exists. However, the field is so new and is evolving so rapidly that the analysis produced can be haphazard at best. The 9 Pitfalls of Data Science shows us real-world examples of what can go wrong. Written to be an entertaining read, this invaluable guide investigates the all too common mistakes of data scientists - who can be plagued by lazy thinking, whims, hunches, and prejudices - and indicates how they have been at the root of many disasters, including the Great Recession. Gary Smith and Jay Cordes emphasise how scientific rigor and critical thinking skills are indispensable in this age of Big Data, as machines often find meaningless patterns that can lead to dangerous false conclusions. The ^9 Pitfalls of Data Science is loaded with entertaining tales of both successful and misguided approaches to interpreting data, both grand successes and epic failures. These cautionary tales will not only help data scientists be more effective, but also help the public distinguish between good and bad data science.
Data Science Without Makeup
DOWNLOAD
Author : Mikhail Zhilkin
language : en
Publisher: CRC Press
Release Date : 2021-11-01
Data Science Without Makeup written by Mikhail Zhilkin and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-01 with Computers categories.
Mikhail Zhilkin, a data scientist who has worked on projects ranging from Candy Crush games to Premier League football players’ physical performance, shares his strong views on some of the best and, more importantly, worst practices in data analytics and business intelligence. Why data science is hard, what pitfalls analysts and decision-makers fall into, and what everyone involved can do to give themselves a fighting chance—the book examines these and other questions with the skepticism of someone who has seen the sausage being made. Honest and direct, full of examples from real life, Data Science Without Makeup: A Guidebook for End-Users, Analysts and Managers will be of great interest to people who aspire to work with data, people who already work with data, and people who work with people who work with data—from students to professional researchers and from early-career to seasoned professionals. Mikhail Zhilkin is a data scientist at Arsenal FC. He has previously worked on the popular Candy Crush mobile games and in sports betting.
Human Centric Integration Of Next Generation Data Science And Blockchain Technology
DOWNLOAD
Author : Amit Kumar Tyagi
language : en
Publisher: Academic Press
Release Date : 2025-03-17
Human Centric Integration Of Next Generation Data Science And Blockchain Technology written by Amit Kumar Tyagi and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-17 with Science categories.
Human- Centric Integration of Next Generation Data Science and Blockchain Technology: Advancing Society 5.0 Paradigms focuses on the current technological landscape, addressing the evolving integration of data science and blockchain within the context of Society 5.0. This comprehensive resource explains the convergences between data science, blockchain, and the human-centric vision of Society 5.0, while also filling the gap in understanding and navigating this transformative intersection with recent shifts towards more decentralized and data-driven paradigms.The book introduces the concept of Society 5.0, examining the historical context, and outlines the evolving technological landscape shaping our interconnected future. It discusses the fundamental principles of data science, from data collection and preprocessing to exploratory data analysis and explains the transformative impact of data science and blockchain across industries such as healthcare, finance, education, and transportation. This book is essential to understanding and shaping the future of technology and society from decentralized solutions to predictive analytics/ emerging technologies. - Addresses the evolving integration of data science and blockchain within the context of Society 5.0 - Introduces the basic architecture and taxonomy of blockchain technology - Explores the future urban lives under the concept of "Society 5.0", characterized by the key phrases of data-driven society and knowledge-intensive society - Offers a firm foundation and understanding of recent advancements in various domains such as data analytics, neural networks, computer vision, and robotics, along with practical solutions to existing problems in fields such as healthcare, manufacturing industries, security, and infrastructure management
The 9 Pitfalls Of Data Science
DOWNLOAD
Author : Gary Smith
language : en
Publisher:
Release Date : 2019
The 9 Pitfalls Of Data Science written by Gary Smith and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with Big data categories.
Data science has never had more influence on the world. Large companies are now seeing the benefit of employing data scientists to interpret the vast amounts of data that now exists. However, the field is so new and is evolving so rapidly that the analysis produced can be haphazard at best. 'The 9 Pitfalls of Data Science' shows us real-world examples of what can go wrong. Written to be an entertaining read, this invaluable guide investigates the all too common mistakes of data scientists - who can be plagued by lazy thinking, whims, hunches, and prejudices - and indicates how they have been at the root of many disasters, including the Great Recession.
An Introduction To Spatial Data Science With Geoda
DOWNLOAD
Author : Luc Anselin
language : en
Publisher: CRC Press
Release Date : 2024-04-26
An Introduction To Spatial Data Science With Geoda written by Luc Anselin and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-26 with Science categories.
This book is the first in a two-volume series that introduces the field of spatial data science. It offers an accessible overview of the methodology of exploratory spatial data analysis. It also constitutes the definitive user’s guide for the widely adopted GeoDa open-source software for spatial analysis. Leveraging a large number of real-world empirical illustrations, readers will gain an understanding of the main concepts and techniques, using dynamic graphics for thematic mapping, statistical graphing, and, most centrally, the analysis of spatial autocorrelation. Key to this analysis is the concept of local indicators of spatial association, pioneered by the author and recently extended to the analysis of multivariate data. The focus of the book is on intuitive methods to discover interesting patterns in spatial data. It offers a progression from basic data manipulation through description and exploration to the identification of clusters and outliers by means of local spatial autocorrelation analysis. A distinctive approach is to spatialize intrinsically non-spatial methods by means of linking and brushing with a range of map representations, including several that are unique to the GeoDa software. The book also represents the most in-depth treatment of local spatial autocorrelation and its visualization and interpretation by means of GeoDa. The book is intended for readers interested in going beyond simple mapping of geographical data to gain insight into interesting patterns. Some basic familiarity with statistical concepts is assumed, but no previous knowledge of GIS or mapping is required. Key Features: • Includes spatial perspectives on cluster analysis • Focuses on exploring spatial data • Supplemented by extensive support with sample data sets and examples on the GeoDaCenter website This book is both useful as a reference for the software and as a text for students and researchers of spatial data science. Luc Anselin is the Founding Director of the Center for Spatial Data Science at the University of Chicago, where he is also the Stein-Freiler Distinguished Service Professor of Sociology and the College, as well as a member of the Committee on Data Science. He is the creator of the GeoDa software and an active contributor to the PySAL Python open-source software library for spatial analysis. He has written widely on topics dealing with the methodology of spatial data analysis, including his classic 1988 text on Spatial Econometrics. His work has been recognized by many awards, such as his election to the U.S. National Academy of Science and the American Academy of Arts and Science.
Managing Data Science
DOWNLOAD
Author : Kirill Dubovikov
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-11-12
Managing Data Science written by Kirill Dubovikov and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-12 with Computers categories.
Understand data science concepts and methodologies to manage and deliver top-notch solutions for your organization Key FeaturesLearn the basics of data science and explore its possibilities and limitationsManage data science projects and assemble teams effectively even in the most challenging situationsUnderstand management principles and approaches for data science projects to streamline the innovation processBook Description Data science and machine learning can transform any organization and unlock new opportunities. However, employing the right management strategies is crucial to guide the solution from prototype to production. Traditional approaches often fail as they don't entirely meet the conditions and requirements necessary for current data science projects. In this book, you'll explore the right approach to data science project management, along with useful tips and best practices to guide you along the way. After understanding the practical applications of data science and artificial intelligence, you'll see how to incorporate them into your solutions. Next, you will go through the data science project life cycle, explore the common pitfalls encountered at each step, and learn how to avoid them. Any data science project requires a skilled team, and this book will offer the right advice for hiring and growing a data science team for your organization. Later, you'll be shown how to efficiently manage and improve your data science projects through the use of DevOps and ModelOps. By the end of this book, you will be well versed with various data science solutions and have gained practical insights into tackling the different challenges that you'll encounter on a daily basis. What you will learnUnderstand the underlying problems of building a strong data science pipelineExplore the different tools for building and deploying data science solutionsHire, grow, and sustain a data science teamManage data science projects through all stages, from prototype to productionLearn how to use ModelOps to improve your data science pipelinesGet up to speed with the model testing techniques used in both development and production stagesWho this book is for This book is for data scientists, analysts, and program managers who want to use data science for business productivity by incorporating data science workflows efficiently. Some understanding of basic data science concepts will be useful to get the most out of this book.
The Decision Maker S Handbook To Data Science
DOWNLOAD
Author : Stylianos Kampakis
language : en
Publisher: Apress
Release Date : 2019-11-26
The Decision Maker S Handbook To Data Science written by Stylianos Kampakis and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-26 with Computers categories.
Data science is expanding across industries at a rapid pace, and the companies first to adopt best practices will gain a significant advantage. To reap the benefits, decision makers need to have a confident understanding of data science and its application in their organization. It is easy for novices to the subject to feel paralyzed by intimidating buzzwords, but what many don’t realize is that data science is in fact quite multidisciplinary—useful in the hands of business analysts, communications strategists, designers, and more. With the second edition of The Decision Maker’s Handbook to Data Science, you will learn how to think like a veteran data scientist and approach solutions to business problems in an entirely new way. Author Stylianos Kampakis provides you with the expertise and tools required to develop a solid data strategy that is continuously effective. Ethics and legal issues surrounding data collection and algorithmic bias are some common pitfalls that Kampakis helps you avoid, while guiding you on the path to build a thriving data science culture at your organization. This updated and revised second edition, includes plenty of case studies, tools for project assessment, and expanded content for hiring and managing data scientists Data science is a language that everyone at a modern company should understand across departments. Friction in communication arises most often when management does not connect with what a data scientist is doing or how impactful data collection and storage can be for their organization. The Decision Maker’s Handbook to Data Science bridges this gap and readies you for both the present and future of your workplace in this engaging, comprehensive guide. What You Will Learn Understand how data science can be used within your business. Recognize the differences between AI, machine learning, and statistics. Become skilled at thinking like a data scientist, without being one. Discover how to hire and manage data scientists. Comprehend how to build the right environment in order to make your organization data-driven. Who This Book Is For Startup founders, product managers, higher level managers, and any other non-technical decision makers who are thinking to implement data science in their organization and hire data scientists. A secondary audience includes people looking for a soft introduction into the subject of data science.
The Phantom Pattern Problem
DOWNLOAD
Author : Gary Smith
language : en
Publisher: Oxford University Press
Release Date : 2020-09-25
The Phantom Pattern Problem written by Gary Smith and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-25 with Science categories.
Pattern-recognition prowess served our ancestors well, but today we are confronted by a deluge of data that is far more abstract, complicated, and difficult to interpret. The number of possible patterns that can be identified relative to the number that are genuinely useful has grown exponentially - which means that the chances that a discovered pattern is useful is rapidly approaching zero. Patterns in data are often used as evidence, but how can you tell if that evidence is worth believing? We are hard-wired to notice patterns and to think that the patterns we notice are meaningful. Streaks, clusters, and correlations are the norm, not the exception. Our challenge is to overcome our inherited inclination to think that all patterns are significant, as in this age of Big Data patterns are inevitable and usually coincidental. Through countless examples, The Phantom Pattern Problem is an engaging read that helps us avoid being duped by data, tricked into worthless investing strategies, or scared out of getting vaccinations.
Cambridge Handbook Of Qualitative Digital Research
DOWNLOAD
Author : Boyka Simeonova
language : en
Publisher: Cambridge University Press
Release Date : 2023-06-22
Cambridge Handbook Of Qualitative Digital Research written by Boyka Simeonova and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-22 with Social Science categories.
Big data and algorithmic decision-making have been touted as game-changing developments in management research, but they have their limitations. Qualitative approaches should not be cast aside in the age of digitalisation, since they facilitate understanding of quantitative data and the questioning of assumptions and conclusions that may otherwise lead to faulty implications being drawn, and - crucially - inaccurate strategies, decisions and actions. This handbook comprises three parts: Part I highlights many of the issues associated with 'unthinking digitalisation', particularly concerning the overreliance on algorithmic decision-making and the consequent need for qualitative research. Part II provides examples of the various qualitative methods that can be usefully employed in researching various digital phenomena and issues. Part III introduces a range of emergent issues concerning practice, knowing, datafication, technology design and implementation, data reliance and algorithms, digitalisation.