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

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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

Statistical testing basics and sample comparison tests

Almost all statistical tests center on hypothesis testing. A hypothesis in this case is an expected outcome of a situation. For example, we might be measuring efficiencies of solar cells coming off a manufacturing line as we did in the previous chapter. We can choose our hypothesis; for example, we can hypothesize that the average efficiency of the solar cells is 14%. Then, we can use a statistical test to check if the data supports or rejects our hypothesis.

In the parlance of statistics, the base case, or no-effect case, is called the null hypothesis. In the example of the solar cell efficiencies, this null hypothesis is that the average efficiency is no different from 14%. The other case is called the alternative hypothesis; in our example, this is the case when the average efficiency is significantly different from 14%. "Significant" is quantified with calculations from mathematical/statistical equations. We...