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Essential Statistics for Non-STEM Data Analysts

You're reading from   Essential Statistics for Non-STEM Data Analysts Get to grips with the statistics and math knowledge needed to enter the world of data science with Python

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Product type Paperback
Published in Nov 2020
Publisher Packt
ISBN-13 9781838984847
Length 392 pages
Edition 1st Edition
Languages
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Author (1):
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Rongpeng Li Rongpeng Li
Author Profile Icon Rongpeng Li
Rongpeng Li
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Table of Contents (19) Chapters Close

Preface 1. Section 1: Getting Started with Statistics for Data Science
2. Chapter 1: Fundamentals of Data Collection, Cleaning, and Preprocessing FREE CHAPTER 3. Chapter 2: Essential Statistics for Data Assessment 4. Chapter 3: Visualization with Statistical Graphs 5. Section 2: Essentials of Statistical Analysis
6. Chapter 4: Sampling and Inferential Statistics 7. Chapter 5: Common Probability Distributions 8. Chapter 6: Parametric Estimation 9. Chapter 7: Statistical Hypothesis Testing 10. Section 3: Statistics for Machine Learning
11. Chapter 8: Statistics for Regression 12. Chapter 9: Statistics for Classification 13. Chapter 10: Statistics for Tree-Based Methods 14. Chapter 11: Statistics for Ensemble Methods 15. Section 4: Appendix
16. Chapter 12: A Collection of Best Practices 17. Chapter 13: Exercises and Projects 18. Other Books You May Enjoy

Further reading

With that, you have reached the last part of this book. In this section, I am going to recommend some of the best books on data science, statistics, and machine learning I've found, all of which can act as companions to this book. I have grouped them into categories and shared my personal thoughts on them.

Textbooks

Books that fall into this category are read like textbooks and are often used as textbooks or at least reference books in universities. Their quality has been proven and their value is timeless.

The first one is Statistical Inference by George Casella, 2nd Edition, which book covers the first several chapters of this book. It contains a multitude of useful exercises and practices, all of which are explained in detail. It is hard to get lost when reading this book.

The second book is The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani, and Jerome Friedman, 2nd Edition. This book is the bible of traditional statistical...

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