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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 |
Practical | 24 |
Autonomous Student Learning | 72 |
Total | 120 |
Prior experience with programming in Python and working with the object-oriented programming paradigm.
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.
Name | Role |
---|---|
Dr Timilehin Aderinola | Tutor |
Dr Thach Le Nguyen | Tutor |
Jin Kuan Moo | Tutor |
Davide Italo Serramazza | Tutor |
Lecture | Offering 1 | Week(s) - 20, 21, 23, 24, 25, 26, 29, 31, 32, 33 | Mon 14:00 - 14:50 |
Lecture | Offering 1 | Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 | Thurs 12:00 - 12:50 |
Practical | Offering 1 | Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 | Thurs 14:00 - 15:50 |