Python Programming: Best Practices for Data Science
Students learn clean-code principles, collaborative development with GitHub, effective project structures, and methods for testing and debugging machine learning code. The module also introduces cloud computing services and the practical considerations involved in deploying and maintaining models.
Delivery
Live online lectures and tutorials, conducted at scheduled times and recorded for review.
Study Hours
Approximately 100 hours of study, of which 15 are contact hours and 85 are private study.
Software Used
Python, GitHub
Credits
10 CATS credits
Module Lead
Programme Faculty
Key Outcomes
- —Write clean and maintainable Python code
- —Collaborate through GitHub-based workflows
- —Structure professional data science projects
- —Test and debug machine learning code
- —Deploy and maintain models in cloud environments
Desired Skills
- —Write scripts that are easy to understand and maintain
- —Follow professional coding practices consistently
- —Structure code confidently for readability and reuse
Lecture Programme
- WK 01
Principles of clean code in Python
- ·Readable and maintainable scripts
- ·Professional coding standards
- WK 02
Version control and collaboration
- ·Git and GitHub workflows
- ·Collaborative development
- WK 03
Structuring data science projects
- ·Organising code and data
- ·Reusable project structures
- WK 04
Testing and debugging machine learning code
- ·Testing strategies
- ·Debugging and maintaining models
- WK 05
Cloud deployment
- ·Cloud computing services
- ·Deploying machine learning models
Assessment
Online coursework submission at the end of the module.
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