Module overview
Aims and Objectives
Learning Outcomes
Learning Outcomes
Having successfully completed this module you will be able to:
- Demonstrate effective programme development using the Python programming language.
- Use Jupyter notebooks for recording code development and use.
- Demonstrate effective data handling and visualisation strategies for large data sets.
- Demonstrate effective algorithm formulation through use of loops and logic structures.
- Apply computational language and structures to solve problems in a chemical context.
Syllabus
1. Introduction to Python – interface structure and layout. (Inc. read/write files etc)
2. Jupyter notebooks
3. Introduction to LINUX
4. Variables, data structures and operators.
5. Arrays, vectors and matrices.
6. Visualisation
7. Logic structures and loops
8. Functions
9. BASH Scripting
10. Errors, debugging and problem solving
Learning and Teaching
Teaching and learning methods
Lecture material, Worksheets and computational workshops.
Type | Hours |
---|---|
Lecture | 10 |
Completion of assessment task | 30 |
Workshops | 30 |
Guided independent study | 40 |
Wider reading or practice | 40 |
Total study time | 150 |
Assessment
Summative
This is how we’ll formally assess what you have learned in this module.
Method | Percentage contribution |
---|---|
Workshop activities | 100% |
Repeat Information
Repeat type: Internal & External