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

Exploratory Data Analysis and Visualization

We already briefly touched on exploratory data analysis (EDA) and visualization in the previous chapter, and now we will go deeper. EDA is a crucial step in any data science project because we need to understand our data to properly use it. EDA is iterative and happens continually throughout a project. As we learn more about how our data looks from analysis to modeling, we also need to incorporate more EDA to deepen our understanding.

Visualization goes hand in hand with EDA, and other books often show solely visual EDA. In this chapter, our EDA will focus on visualizations as well, since we already touched on numerical EDA in the previous chapter with pandas. However, visualization also involves a lot more – there are loads of best practices for making good visualizations. We will cover the key best practices for visualizations here, so you can make impactful and professional visualizations with Python.

In this chapter,...