Computing for Data Analytics&Finance L6 (G5219)
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Computing for Data Analytics and Finance (L6)
Module G5219
Module details for 2025/26.
15 credits
FHEQ Level 6
Module Outline
The aim of this module is to provide basic programming skills with applications in MATLAB/Octave, Python or R. While no previous programming experience is assumed we build up quite quickly to an operational level including (tax/loan/investment/portfolio) ledger book programming, graphing and charting financial data, stochastic simulations involving random number generators, importing/exporting to databases and websites, and more advanced topics in financial computing and financial data analysis.
Module learning outcomes
Have a systematic understanding of computer packages such as MATLAB or Python
Understand how to plan file structure
Access and manipulate data from external data files
Apply the methods and techniques they have learnt to solve finance related problems
| Type | Timing | Weighting |
|---|---|---|
| Coursework | 10.00% | |
| Coursework components. Weighted as shown below. | ||
| Portfolio | T1 Week 11 | 100.00% |
| Computer Based Exam | Semester 1 Assessment | 60.00% |
| Coursework | 30.00% | |
| Coursework components. Weighted as shown below. | ||
| Problem Set | T1 Week 4 | 25.00% |
| Problem Set | T1 Week 6 | 25.00% |
| Problem Set | T1 Week 9 | 25.00% |
| Problem Set | T1 Week 11 | 25.00% |
Timing
Submission deadlines may vary for different types of assignment/groups of students.
Weighting
Coursework components (if listed) total 100% of the overall coursework weighting value.
| Term | Method | Duration | Week pattern |
|---|---|---|---|
| Autumn Semester | Practical | 1 hour | 11111111111 |
| Autumn Semester | Workshop | 1 hour | 01111100111 |
| Autumn Semester | Lecture | 1 hour | 11111111111 |
How to read the week pattern
The numbers indicate the weeks of the term and how many events take place each week.
Dr Philip Herbert
Assess convenor, Convenor
/profiles/616541
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