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

Summary

Well, that certainly was a lot of information in this chapter, but now you have the tools to really dig in and get started on data science. Much like a cook cannot do much without the proper tools, such as sharp knives and specialized utensils, we cannot do proper data science without having the proper tools. Our tools consist of programming languages (mainly Python for data science), code editors and IDEs (such as VS Code), and ways to develop, test, and run our code (such as terminals, IPython, and Jupyter Notebooks).

Although we got started on the basics of Python, there is a lot more to learn, and continuous practice is key to becoming a Python master. There are many other good resources out there for learning Python in more depth, such as Learning Python and Learn Python Programming, by Fabrizio Romano, from Packt. Remember that if you get stuck with errors in your code or don't know how to do something, internet search engines, Stack Overflow, and the documentation...