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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

Product type Paperback

Published in Nov 2020

Publisher Packt

ISBN-13 9781838984847

Length 392 pages

Edition 1st Edition

Languages

Python

Concepts

Data Science

Author (1):

Rongpeng Li

View More author details

Table of Contents (19) Chapters

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

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Summary

Congratulations on reaching the end of this book! In this chapter, we introduced exercises and projects of varying difficulty. You were also provided with a list of additional books that will help you as you progress through the exercises and projects mentioned in this chapter. I hope these additional materials will boost your statistical learning progress and make you an even better data scientist.

If you have any questions about the content of this book, please feel free to light up any issues on the official GitHub repository for this book: https://github.com/PacktPublishing/Essential-Statistics-for-Non-STEM-Data-Analysts. We are always happy to answer your questions there.

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Authors (1)

Li

Li

Sherry Li is an Analytic Consultant who works for a major financial organization with responsibilities in implementing data warehousing, Business Intelligence, and business reporting solutions. She specializes in automation and optimization of data gathering, storing, analyzing and providing data access for business to gain data-driven insights. She especially enjoys sharing her experience and knowledge in data ETL process, database design, dimensional modeling, and reporting in T-SQL and MDX. She has co-authored two books, the MDX with SSAS 2012 Cookbook and MDX with Microsoft SQL Server 2016 Analysis Services Cookbook, which have helped many data professionals advanced their MDX skill in a very short time.

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