MEIN40310 Python for the Life Sciences

Academic Year 2021/2022

Python is essential in data science and this module provides an interactive, step-by-step and easy-to -follow introduction to "how to program" with Python. The objective of this module is to provide students (with little or no prior experience in programming) the foundations of programming using Python.

The module provides overview of the Python programming language emphasising on key and engaging interdisciplinary applications using examples and exercises drawn from various aspect of life science research. The module will provide a step-by-step implementation of these examples using Python -- which is one of the most popular programming languages for scientific computing.

The module is structured to provide students with the essential practical components of Python for scientific tasks that will enable them to start writing code immediately (after the first few sessions). The example-driven focus of the module will then enhance the programming skillset of the students allowing them to independently develop code for their own research questions.

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Curricular information is subject to change

Learning Outcomes:

At the end of the module, students should be able to:
- Configure and setup python IDE for life science projects
- Write basic functional python scripts
- Identify packages and libraries essential to scientific computing
- Write python codes for the following indicative applications including motif identification in genomic data, pattern identification in interaction network data, and dynamic modelling of biochemical switches.

Indicative Module Content:

Python basics - getting started with "how-to-program" using Python.
Advanced Python - covering object-oriented, regular expressions, calling libraries and packages for life science application

Students will work on specific examples covering simple biochemical calculations and sequence analysis, to modelling the dynamic interactions of genes and protein in cells and evolutionary properties of system biology. How-to implementation using projects in Python.

Student Effort Hours: 
Student Effort Type Hours


Specified Learning Activities


Autonomous Student Learning




Approaches to Teaching and Learning:
Active/task-based learning;
Peer and group work;
Reflective learning; 
Requirements, Exclusions and Recommendations

Not applicable to this module.

Module Requisites and Incompatibles
Not applicable to this module.
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Continuous Assessment: Two programming challenges to evaluate the programming competency: Basic Python covering Week 1- 4 (25%) and Advanced Python covering Week 5- 8 (25%).
Throughout the Trimester n/a Standard conversion grade scale 40% No


Project: Python scripting for life science application (50%) -- students will complete a skeleton Python project Throughout the Trimester n/a Standard conversion grade scale 40% No


Carry forward of passed components
Resit In Terminal Exam
Spring No
Please see Student Jargon Buster for more information about remediation types and timing. 
Feedback Strategy/Strategies

• Feedback individually to students, on an activity or draft prior to summative assessment
• Feedback individually to students, post-assessment

How will my Feedback be Delivered?

Not yet recorded.

Name Role
Professor Brendan Loftus Lecturer / Co-Lecturer