top of page

Python Programming: Best
Practices for Data Science

Please note to take this course you must first have completed Advanced Machine Learning & Programming in Python

This course covers best practices in Python programming. It focuses on structuring code, commenting code, using versioning tools like Github, and setting up data science projects in Python which allow for effective collaboration and maintenance of code while working with other coders in large projects.

This module can be taken as part of a PG Certificate, PG Diploma or Full Masters Program. 

Key Skills

By the end of this course, participants should have knowledge and understanding of: 

Cloud Computing Proficiency: Ability to leverage cloud computing services for data science projects.

Practical Application: Application of cloud computing and collaborative coding skills to real-world projects.

Collaborative Coding with GitHub: Skills in using collaborative coding environments, particularly GitHub.

Tools Utilisation: Effective use of tools such as GitHub for collaborative coding.

Machine Learning Deployment: Proficiency in deploying, testing, and maintaining machine learning models.

Testing and Maintenance of Models: Rigorous testing and ongoing maintenance of machine learning models.

Cloud Service Integration: Integrating cloud computing services into data science workflows.

Desired Skills

By the end of this course, students should be able to: 

Write high quality and easy-to-maintain Python scripts.


Know how to make use of the Python programming language in the best possible way for data science projects..

Adhere to best coding practices.

Understand how to best structure code for clarity, readability, and easy maintenance

Be confident in sharing code with others.

Be confident in using Python in data science projects

Be familiar with using versioning systems such as Github.


Know how to use Github for collaborative coding projects



Python Programming is an optional 10 credit course and therefore students are expected to input approximately 100 hours of study into the course.


The total number of contact hours is 15 hours. This leaves 85  hours for private study.


This module consists of 2 - hour lectures per day for 5 days, plus a 1 - hour tutorial per day. 

 There will be optional clinics on the last day of the course.   

The dates of each lecture are confirmed closer to the start of each term. If you have any questions about dates, please contact

bottom of page