COMP10290 Computation for Scientists

Academic Year 2022/2023

To develop skills in the use of computational techniques for scientific enquiry:

- performing calculations; manipulating, visualising and presenting data;
- implementing simple computational models of scientific problems.

To learn the foundations of programming in Python through solving scientific problems.

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Curricular information is subject to change

Learning Outcomes:

1. Understand the role of computation in scientific enquiry.
2. Learn good programming practice through solving problems in science.
3. Programming
3.1 Perform scientific calculations in Python (using variables, math library, formatting of results etc)
3.2 Functions: be able to use and write functions
3.3 Understand fundamental concepts of programming including variables, conditions, repetition/loops
3.4 Storing and manipulating data: floats, integers, lists, strings, dictionaries, containers
3.5 Use libraries for file input/output, manipulating data in arrays (e.g. numpy, pandas libraries)
4. Scientific visualisation
4.1 Loading, manipulating, extracting summary statistics from data.
4.2 Plotting of data with proper labelling, titles.

5. Application of programming concepts to range of problems in science

Indicative Module Content:

Introduction to the role and techniques of computational modelling, as used in scientific enquiry.
Introduction to the basic concepts of programming using Python as the programming language: variables, conditional statements, loops, functions, .
Data visualisation techniques using visulisation libraries in Python.
Case studies of scientific problems from the disciplines of physics, chemistry, biology and, applied mathematics.

Student Effort Hours: 
Student Effort Type Hours
Computer Aided Lab


Autonomous Student Learning


Online Learning




Approaches to Teaching and Learning:
Blended approach to content delivery. Lectures on basic programming concepts delivered online.
Active task-based and problem-based learning, carried out in a lab environment. Peer learning and group work, driven by case studies to explore the different uses of computational modelling in the scientific disciplines.
Critical thinking, report writing. 
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
Examination: End of term examination, probably by MCQ. Week 12 Yes Alternative linear conversion grade scale 40% No


Continuous Assessment: Programming and computational science problems and case studies submitted as Python notebooks. Throughout the Trimester n/a Alternative linear 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, post-assessment

How will my Feedback be Delivered?

Not yet recorded.

Name Role
Aayush Roy Tutor
Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Practical Offering 1 Week(s) - 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 Thurs 12:00 - 13:50
Practical Offering 2 Week(s) - 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 Fri 14:00 - 15:50