Data Science in Practice
1st Edition
Published on 22. September 2023
318 pages
978-1-000-95564-4 (ISBN)
Description
Data Science in Practice is the ideal introduction to data science. With or without math skills, here, you get the all-round view that you need for your projects. This book describes how to properly question data, in order to unearth the treasure that data can be. You will get to know the relevant analysis methods, and will be introduced to the programming language R, which is ideally suited for data analysis. Associated tools like notebooks that make data science programming easily accessible are included in this introduction. Because technology alone is not enough, this book also deals with problems in project implementation, illuminates various fields of application, and does not forget to address ethical aspects. Data Science in Practice includes many examples, notes on errors, decision-making aids, and other practical tips. This book is ideal as a complementary text for university students, and is a useful learning tool for those moving into more data-related roles.
Key Features:
Success factors and tools for all project phases
Includes application examples for various subject areas
Introduces many aspects of Data Science, from requirements analysis to data acquisition and visualization
Key Features:
Success factors and tools for all project phases
Includes application examples for various subject areas
Introduces many aspects of Data Science, from requirements analysis to data acquisition and visualization
Reviews / Votes
"This book is a comprehensive introduction to data science, with a focus on how it is used in practice. It promises to be suitable for a variety of audiences: data science stakeholders without advanced mathematical backgrounds and who might not be planning careers as Data Scientists, those wanting to go deeper into data science, and for decision makers who might be interested in starting data science projects or teams. It is described as a complementary text for university students and for those moving into more data-related roles. I believe the book suits these particular audiences. It provides a high-level "tour" of important data science concepts, and provides resources for those interested in learning more. My opinion is that it is best suited for those managing data science teams, or for more business-oriented individuals working with data scientists." - Xiao Hui Tai, Journal of the American Statistical Association, April 9, 2025."...in my opinion the greatest value of this book is in providing a thoughtful curation of topics and methods that are most relevant to data science as it is currently practiced. It provides a suitable introduction to these selected topics, as well as resources for those interested in learning more. It is a valuable introduction to those curious about the field of data science and how it is used in practice."
- Xiao Hui Tai, "Data Science in Practice: Tom Alby, Boca Raton, FL: Chapman & Hall/CRC Press, 2024, Xvi + 301 Pp., $200.00(H), ISBN: 978-1-032-50524-4." The American Statistician 79 (3)
More details
Series
Edition
1. Auflage
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
Professional and scholarly
Illustrations
57 Line drawings, color; 24 Line drawings, black and white; 63 Halftones, color; 2 Halftones, black and white; 120 Illustrations, color; 26 Illustrations, black and white
ISBN-13
978-1-000-95564-4 (9781000955644)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Tom Alby
Data Science in Practice
Book
09/2023
1st Edition
Chapman & Hall/CRC
€255.00
Shipment within 10-20 days
Tom Alby
Data Science in Practice
Book
09/2023
1st Edition
Chapman & Hall/CRC
€112.80
Shipment within 10-20 days
Person
Tom Alby has been working in the digital world since 1994, including nearly 20 years for search engines such as Lycos, Ask.com and Google. His focus is on data-driven applications for everyday business and the development of data literacy. He is the author of several books, lecturer for Data Science and Digital Analytics at various universities and certified project manager (PMP) of the Project Management Institute since 2004.
Content
1. Introduction 2. Machine Learning, Data Science and Artificial Intelligence 3. The Anatomy of a Data Science Project 4. Introduction to R 5. Exploratory Data Analysis 6. Forecasting 7. Clustering 8. Classification 9. Other use cases 10. Workflows and Tools 11. Ethical handling of data and algorithms 12. Next Steps after this book? 13. Appendix: Troubleshooting 14. Glossary
