You're reading from Python and SQL Bible

Product type Book

Published in Jun 2024

Publisher Packt

ISBN-13 9781836206279

Pages 519 pages

Edition 1st Edition

Languages

SQL Python

Concepts

Programming Language

Author (1):

Cuantum Technologies LLC

Table of Contents (29) Chapters

1. Who we are

2. Introduction

3. Chapter 1: Python: An Introduction

4. Chapter 2: Python Building Blocks

5. Chapter 3: Controlling the Flow

6. Chapter 4: Functions, Modules, and Packages

7. Chapter 5: Deep Dive into Data Structures

8. Chapter 6: Object-Oriented Programming in Python

9. Chapter 7: File I/O and Resource Management

10. Chapter 8: Exceptional Python

11. Chapter 9: Python Standard Library

12. Chapter 10: Python for Scientific Computing and Data Analysis

13. Chapter 11: Testing in Python

14. Chapter 12: Introduction to SQL

15. Chapter 13: SQL Basics

16. Chapter 14: Deep Dive into SQL Queries

17. Chapter 15: Advanced SQL

18. Chapter 16: SQL for Database Administration

19. Chapter 17: Python Meets SQL

20. Chapter 18: Data Analysis with Python and SQL

21. Chapter 19: Advanced Database Operations with SQLAlchemy

22. References

23. Conclusion

24. Where to continue?

25. Know more about us

Appendix A: Python Interview Questions

1. Appendix B: SQL Interview Questions

2. Appendix C: Python Cheat Sheet

3. Appendix D: SQL Cheat Sheet

CRUD Operations

18.4 Statistical Analysis in Python and SQL

Statistical analysis is a crucial step in the process of transforming raw data into meaningful insights. Without statistical analysis, the data can be meaningless and difficult to interpret. Luckily, with the use of Python and SQL, you can perform a wide array of statistical analyses on your data, including but not limited to hypothesis testing, regression analysis, and clustering.

Hypothesis testing allows you to determine whether a certain hypothesis about your data is true or false, while regression analysis helps you identify the relationship between different variables in your data. Clustering, on the other hand, groups similar observations together, allowing you to identify patterns in your data.

By combining Python and SQL, you have access to a powerful set of tools that can help you unlock the insights hidden within your data.