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Curricular information is subject to change
On completion of this module, students will be able to:
1) Program competently using Python and be familiar with a range of Python packages for data science;
2) Collect, pre-process and filter datasets;
3) Apply and evaluate machine learning algorithms in Python;
4) Visualise and interpret the results of data analysis procedures.
Student Effort Type | Hours |
---|---|
Autonomous Student Learning | 80 |
Lectures | 12 |
Practical | 12 |
Total | 104 |
Prior programming experience in a high level language (but not necessarily in Python).
Description | Timing | Component Scale | % of Final Grade | ||
---|---|---|---|---|---|
Assignment(Including Essay): Practical Assignment 1 | Week 8 | Alternative linear conversion grade scale 40% | No | 25 |
No |
Assignment(Including Essay): Practical Assignment 2 | Week 12 | Alternative linear conversion grade scale 40% | No | 25 |
No |
Exam (Open Book): Two hour End of Trimester practical exam. Scheduled in Exam Period. | End of trimester Duration: 2 hr(s) |
Alternative linear conversion grade scale 40% | No | 50 |
No |
Remediation Type | Remediation Timing |
---|---|
Repeat | Within Two Trimesters |
• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment
Not yet recorded.