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Curricular information is subject to change
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
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 Type | Hours |
---|---|
Computer Aided Lab | 24 |
Autonomous Student Learning | 52 |
Online Learning | 24 |
Total | 100 |
Not applicable to this module.
Description | Timing | Component Scale | % of Final Grade | ||
---|---|---|---|---|---|
Examination: End of term examination, probably by MCQ. | Week 12 | Yes | Alternative linear conversion grade scale 40% | No | 20 |
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 | 80 |
Resit In | Terminal Exam |
---|---|
Spring | No |
• Feedback individually to students, post-assessment
Not yet recorded.
Name | Role |
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Aayush Roy | Tutor |