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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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Who this book is for

This book is for people who are looking for materials to fill the gaps in their statistics knowledge. It should also serve experienced data scientists as an enjoyable read. The book assumes minimal mathematics knowledge and it may appear verbose, as it is designed so that novices can use it as a self-contained book and follow the book chapter by chapter smoothly to build a knowledge base on statistics from the ground up.

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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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