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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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Section 2: Essentials of Statistical Analysis

Section 2 covers the most fundamental and classical contents of statistical analysis at the undergraduate level. However, the statistical analysis we'll get into is applied to messy real-word datasets. This section will give you a taste of statistical analysis as well as sharpening your math skills for further chapters.

This section consists of the following chapters:

Chapter 4, Sampling and Inferential Statistics
Chapter 5, Common Probability Distributions
Chapter 6, Parametric Estimation
Chapter 7, Statistical Hypothesis Testing

The rest of the chapter is locked

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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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