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

Python basics

Python is designed to be an easy-to-use and easy-to-read programming language. Consequently, it's also relatively easy to learn, which is part of why it's so popular.

To follow along and run the examples in this and other chapters, I recommend you use one of the following methods:

Type or copy and paste the code into IPython, a .py file, or Jupyter Notebooks.
Run the Jupyter notebook from this book's GitHub repository.

Be careful when copy-pasting code from the book, however, since sometimes lines of code can spill over on multiple lines in the book. This means when copy-pasted, additional newlines may be added that you will need to look out for (and manually remove). We can infer the intended format from syntax highlighting and formatting, or take a look at the code in the Jupyter Notebooks on the book's GitHub repository.

As you are working through examples in this book, I recommend making modifications to the code...