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Curricular information is subject to change
At the end of the course, students should have attained an intermediate-level knowledge of R. They should be able to use R to:
- Manipulate data sets of any size and structure.
- Program and implement efficient algorithms.
- Create professional quality graphical summaries of data.
- Perform statistical analyses.
- Produce data analysis documents.
Student Effort Type | Hours |
---|---|
Lectures | 12 |
Computer Aided Lab | 12 |
Specified Learning Activities | 26 |
Autonomous Student Learning | 100 |
Total | 150 |
Not applicable to this module.
Description | Timing | Component Scale | % of Final Grade | ||
---|---|---|---|---|---|
Project: Coursework project | Coursework (End of Trimester) | n/a | Alternative linear conversion grade scale 40% | No | 50 |
Continuous Assessment: Computer labs, multiple choice questionnaires, assignments | Varies over the Trimester | n/a | Alternative linear conversion grade scale 40% | No | 50 |
Resit In | Terminal Exam |
---|---|
Spring | No |
• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment
• Online automated feedback
Not yet recorded.
Name | Role |
---|---|
Manisha Ganguly | Tutor |
Ms Claire Mullen | Tutor |
Thais Pacheco Menezes | Tutor |
Lecture | Offering 1 | Week(s) - Autumn: All Weeks | Thurs 10:00 - 10:50 |
Laboratory | Offering 1 | Week(s) - Autumn: Weeks 2-12 | Thurs 09:00 - 09:50 |
Laboratory | Offering 2 | Week(s) - Autumn: Weeks 2-12 | Thurs 11:00 - 11:50 |
Laboratory | Offering 3 | Week(s) - Autumn: Weeks 2-12 | Tues 16:00 - 16:50 |
Laboratory | Offering 4 | Week(s) - Autumn: Weeks 2-12 | Mon 09:00 - 09:50 |