Python Data Analytics: From Notebooks to Production

59mAdvanced2024-08-26

Authors

Miki Tebeka

CEO at 353Solutions

Course details

Your code needs to run in production, which has its own set of challenges and restrictions. In this course, instructor Miki Tebeka shows you how to convert your research notebook to production ready code. Learn about the needs of production, where notebooks can really help you, and where they might fall short. Explore why you should organize your code and how to write clean API for your modules, as well as how to structure your code with sub modules. Find out why testing your code is important and go over useful techniques and best practices. Plus, dive into dependency management, logging and metrics, performance tuning, securing your code, and more.

Skills covered

Data Science FoundationsPythonData AnalysisProgramming LanguagesData ScienceBusiness Analysis and StrategyBusiness Software and ToolsOpen SourceSoftware DevelopmentOne-Off

Concepts

0. Introduction

01 - Notebooks to production
02 - What you should know
03 - Using GitHub Codespaces

1. Notebooks to Production

04 - Understanding production
05 - Where notebooks excel
06 - Where notebooks come short

2. Organizing Your Code

07 - Why you should organize your code
08 - Module API
09 - Sub modules
10 - Main
11 - Challenge - Convert logs notebook
12 - Solution - Convert logs notebook

3. Testing Your Code

13 - Why testing is important
14 - Running notebooks
15 - Parametrized tests
16 - Test fixtures
17 - Continuous integration
18 - Challenge - Test tags
19 - Solution - Test tags

4. Dependency Management

20 - The problems with dependencies
21 - Specifying and installing dependencies
22 - Separating test dependencies
23 - Distributing your package
24 - Challenge - Create an environment
25 - Solution - Create an environment

5. Running in Production

26 - Logging and metrics
27 - Configuration
28 - Performance tuning
29 - Securing your code
30 - Challenge - Make the monthly report production ready
31 - Solution - Make the monthly report production ready

Conclusion

32 - What's next