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

We've learned how to retrieve and store data in Python, and it's a good idea to practice what you learned to lock in the knowledge. In the GitHub repository for this book, the Chapter 3 folder contains a folder called test_your_knowledge. Create a Jupyter notebook in this folder, and complete the following:

Load the bitcoin_price.json data file by opening the file using the built-in json module.
Save this data to a SQLite database.
Query the database using sqlite3 for the following:
- The earliest and latest dates (hint: the MAX and MIN aggregate functions will help)
- The maximum price for each year (grouped by year) and sorted by year
Connect to the chinook.db sqlite3 database with SQLAlchemy.
Find the genre names with the longest average song length. (Hint: join the tables with the genre name and song length and use the SQLite aggregate function for the average along with a GROUP BY...