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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: Practical Assignment 2 | Unspecified | n/a | Alternative linear conversion grade scale 40% | No | 45 |
Assignment: Practical Assignment 1 | Unspecified | n/a | Alternative linear conversion grade scale 40% | No | 30 |
Class Test: Practical Test | Week 12 | n/a | Alternative linear conversion grade scale 40% | No | 25 |
Remediation Type | Remediation Timing |
---|---|
In-Module Resit | Prior to relevant Programme Exam Board |
• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment
Not yet recorded.
Name | Role |
---|---|
Laura Dunne | Tutor |
Practical | Offering 1 | Week(s) - 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12 | Mon 16:00 - 16:50 |
Online Learning | Offering 1 | Week(s) - 20, 21, 23, 24, 25, 26, 29, 30, 31, 32, 33 | Mon 17:00 - 17:50 |