Data Science in Practice
eBook - ePub

Data Science in Practice

  1. 302 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

Data Science in Practice

About this book

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

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Yes, you can access Data Science in Practice by Tom Alby in PDF and/or ePUB format, as well as other popular books in Mathematics & Probability & Statistics. We have over one million books available in our catalogue for you to explore.

Information

Table of contents

  1. Cover
  2. Half-Title
  3. Series
  4. Title
  5. Copyright
  6. Contents
  7. Foreword
  8. Figures
  9. 1 Introduction
  10. 2 Machine Learning, Data Science, and Artificial Intelligence
  11. 3 The Anatomy of a Data Science Project
  12. 4 Introduction to R
  13. 5 Exploratory Data Analysis
  14. 6 Forecasting
  15. 7 Clustering
  16. 8 Classification
  17. 9 Other Use Cases
  18. 10 Workflows and Tools
  19. 11 Ethical Handling of Data and Algorithms
  20. 12 Next Steps after This Book
  21. 13 Appendix: Troubleshooting
  22. 14 Glossary
  23. Bibliography
  24. Index