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COMP10290

Academic Year 2024/2025

Computation for Scientists (COMP10290)

Subject:
Computer Science
College:
Science
School:
Computer Science
Level:
1 (Introductory)
Credits:
5
Module Coordinator:
Assoc Professor Neil Hurley
Trimester:
Autumn
Mode of Delivery:
Blended
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

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.

About this Module

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

24
Autonomous Student Learning

52
Online Learning

24
Total

100

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 Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Assignment(Including Essay): Completion of a programming assignment in Python. Week 4, Week 5, Week 6, Week 7, Week 8, Week 9 Alternative linear conversion grade scale 40% No
30
 No
Practical Skills Assessment: In-lab (in-person) practical programming exam Week 12 Alternative linear conversion grade scale 40% No
35
 No
Exam (In-person): Examination covering computational and scientific aspects of the module. End of trimester
Duration:
2 hr(s)
 Alternative linear conversion grade scale 40% No
35
 No

Carry forward of passed components
Yes
 

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?

Online via Brightspace and individually in lab.

Name Role
Eddie Antonio Santos Tutor

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Autumn Practical Offering 1 Week(s) - Autumn: All Weeks Thurs 12:00 - 13:50
Autumn Practical Offering 2 Week(s) - Autumn: All Weeks Fri 14:00 - 15:50
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