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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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Toc

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

What this book covers

Chapter 1, Fundamentals of Data Collection, Cleaning, and Preprocessing, introduces basic concepts in data collection, cleaning, and simple preprocessing.

Chapter 2, Essential Statistics for Data Assessment, talks about descriptive statistics, which are handy for the assessment of data quality and exploratory data analysis (EDA).

Chapter 3, Visualization with Statistical Graphs, introduces common graphs that suit different visualization scenarios.

Chapter 4, Sampling and Inferential Statistics, introduces the fundamental concepts and methodologies in sampling and the inference techniques associated with it.

Chapter 5, Common Probability Distributions, goes through the most common discrete and continuous distributions, which are the building blocks for more sophisticated real-life empirical distributions.

Chapter 6, Parametric Estimation, covers a classic and rich topic that solidifies your knowledge of statistics and probability by having you estimate parameters from accessible datasets.

Chapter 7, Statistical Hypothesis Testing, looks at a must-have skill for any data scientist or data analyst. We will cover the full life cycle of hypothesis testing, from assumptions to interpretation.

Chapter 8, Statistics for Regression, discusses statistics for regression problems, starting with simple linear regression.

Chapter 9, Statistics for Classification, explores statistics for classification problems, starting with logistic regression.

Chapter 10, Statistics for Tree-Based Methods, delves into statistics for tree-based methods, with a detailed walk through of building a decision tree from first principles.

Chapter 11, Statistics for Ensemble Methods, moves on to ensemble methods, which are meta-algorithms built on top of basic machine learning or statistical algorithms. This chapter is dedicated to methods such as bagging and boosting.

Chapter 12, Best Practice Collection, introduces several important practice tips based on the author's data science mentoring and practicing experience.

Chapter 13, Exercises and Projects, includes exercises and project suggestions grouped by chapter.

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