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
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.
Arvustused
"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.
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
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.
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