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

Test your knowledge

To practice what you've learned, complete the following challenge:

You landed a job as a financial analyst at a hedge fund. Your first task is to gain insights into government-sponsored loans by analyzing the Paycheck Protection Program (PPP) loan data, included in the file PPP Data 150k plus 080820.csv in this book's GitHub repository. Perform EDA on the data (using any necessary data wrangling/preparation steps) and create some professional visualizations to share with your team highlighting your key findings. Be sure to add a written analysis with your visualizations, explaining what they mean.

The full dataset can be found here: https://data.sba.gov/dataset/ppp-foia (although this may change over time).

However, we will only be working with the $150k+ loans data, which are large loans. For an extra challenge, you can combine the state-by-state data with the $150k+ loans data to get the full dataset.