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Curricular information is subject to change
By the end of the module, students should be able to:
- Manipulate and analyse large data sets using the Pandas library
- Create and interpret graphical representations of data
- Use the scikit-learn library to preform machine-learning tasks
- Obtain data over the web from APIs and analyse it using simple statistical methods
Student Effort Type | Hours |
---|---|
Specified Learning Activities | 24 |
Autonomous Student Learning | 75 |
Online Learning | 24 |
Total | 123 |
Students should have completed an introductory level statistics course and have a general understanding of calculus.
Description | Timing | Component Scale | % of Final Grade | ||
---|---|---|---|---|---|
Continuous Assessment: Weekly programming exercises | Throughout the Trimester | n/a | Standard conversion grade scale 40% | No | 50 |
Project: Data analysis project | Throughout the Trimester | n/a | Standard conversion grade scale 40% | No | 50 |
Resit In | Terminal Exam |
---|---|
Spring | Yes - 2 Hour |
• Group/class feedback, post-assessment
• Online automated feedback
Not yet recorded.