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Curricular information is subject to change
On successful completion of this module the learner will be able to:
1. Understand the principles and the purposes of data analytics.
2. Use Python to retrieve and analyse real-world datasets.
3. Apply the process of data understanding and address data quality issues.
4. Use appropriate machine learning techniques for a given data analytics problem.
5. Design evaluation experiments for selecting the best predictive model for a given analytics problem.
Student Effort Type | Hours |
---|---|
Lectures | 24 |
Seminar (or Webinar) | 2 |
Practical | 24 |
Autonomous Student Learning | 70 |
Total | 120 |
Prior experience with programming in Python and working with the object-oriented programming paradigm.
Description | Timing | Component Scale | % of Final Grade | ||
---|---|---|---|---|---|
Assignment: Assignment 1 | Varies over the Trimester | n/a | Graded | No | 50 |
Assignment: Assignment 2 | Varies over the Trimester | n/a | Graded | No | 50 |
Resit In | Terminal Exam |
---|---|
Summer | No |
• Feedback individually to students, on an activity or draft prior to summative assessment
• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment
Not yet recorded.