You're reading from Practical Data Science with Python Learn tools and techniques from hands-on examples to extract insights from data

Product type Paperback

Published in Sep 2021

Publisher Packt

ISBN-13 9781801071970

Length 620 pages

Edition 1st Edition

Languages

Python

Tools

Excel

Concepts

Data Science

Author (1):

Nathan George

View More author details

Table of Contents (30) Chapters

Preface

1. Part I - An Introduction and the Basics

2. Introduction to Data Science FREE CHAPTER

3. Getting Started with Python

4. Part II - Dealing with Data

5. SQL and Built-in File Handling Modules in Python

6. Loading and Wrangling Data with Pandas and NumPy

7. Exploratory Data Analysis and Visualization

8. Data Wrangling Documents and Spreadsheets

9. Web Scraping

10. Part III - Statistics for Data Science

11. Probability, Distributions, and Sampling

12. Statistical Testing for Data Science

13. Part IV - Machine Learning

14. Preparing Data for Machine Learning: Feature Selection, Feature Engineering, and Dimensionality Reduction

15. Machine Learning for Classification

16. Evaluating Machine Learning Classification Models and Sampling for Classification

17. Machine Learning with Regression

18. Optimizing Models and Using AutoML

19. Tree-Based Machine Learning Models

20. Support Vector Machine (SVM) Machine Learning Models

21. Part V - Text Analysis and Reporting

22. Clustering with Machine Learning

23. Working with Text

24. Part VI - Wrapping Up

25. Data Storytelling and Automated Reporting/Dashboarding

26. Ethics and Privacy

27. Staying Up to Date and the Future of Data Science

28. Other Books You May Enjoy

29. Index

Other statistical tests

The tests we have covered so far were mainly for testing the difference in means between groups. There are a huge number of other tests, and many of them have specific purposes. One set of tests we will not cover here are non-parametric tests, which are for small sample sizes and non-Gaussian distributions. Those tests also return p-values for a hypothesis test like the t- and z-test. Some of the common non-parametric tests are the sign test, the Wilcoxen signed-rank test, and the Mann-Whitney U test. Here, we will cover tests for checking if data comes from a specific distribution, an outlier test, and tests for relationships between variables.

Testing if data belongs to a distribution

The first set of tests we will examine test if data is from a normal distribution. The first way to test this is to simply plot a histogram and "eyeball" it. There are several other tests for checking if something comes from a normal distribution, however,...