This module consists of lecturers and associated practical sessions. The first part will focus on basic statistical programming in R. The second part will provide an introduction to some modern computational statistical methods and their implementation in R. The module includes 18 lectures and 18 computer practical sessions for students to gain hands-on experience of statistical programming and computation.
Having successfully completed this module you will be able to:
18 computer labs + 18 lectures (this may all be delivered online)
| Type | Hours |
|---|---|
| Teaching | 36 |
| Independent Study | 114 |
| Total study time | 150 |
Grolemund, G. and Wickham, H.. R for Data Science.
Hastie, T., Tibshirani, R. and Friedman, J.. The Elements of Statistical Learning: Data Mining, Inference, and Prediction.
James, G., Witten, D., Hastie, T. and Tibshirani, R.. An Introduction to Statistical Learning with Applications in R.
This is how we’ll formally assess what you have learned in this module.
| Method | Percentage contribution |
|---|---|
| Coursework | 50% |
| Coursework | 50% |
This is how we’ll assess you if you don’t meet the criteria to pass this module.
| Method | Percentage contribution |
|---|---|
| Coursework | 50% |
| Coursework | 50% |
An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.
| Method | Percentage contribution |
|---|---|
| Coursework | 50% |
| Coursework | 50% |
Repeat type: Internal & External