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Curricular information is subject to change
On completion of this module, students should have acquired the following skills:
- Have an understanding of the theory regarding all the statistical learning methods introduced
- Being able to use the different techniques according to the context and the purpose of analysis
- Being able to evaluate the performance of the statistical learning methods introduced
- Use the statistical software R to implement these methods and being able to interpret the relevant output
Student Effort Type | Hours |
---|---|
Lectures | 24 |
Computer Aided Lab | 11 |
Specified Learning Activities | 25 |
Autonomous Student Learning | 60 |
Total | 120 |
A working knowledge of statistical methods including regression analysis. Familiarity with the R software for statistical computing and data programming.
Description | Timing | Component Scale | % of Final Grade | ||
---|---|---|---|---|---|
Continuous Assessment: Assignments | Varies over the Trimester | n/a | Other | No | 40 |
Project: Final project and assessment | Varies over the Trimester | n/a | Other | No | 60 |
Resit In | Terminal Exam |
---|---|
Autumn | Yes - 2 Hour |
• Group/class feedback, post-assessment
Not yet recorded.