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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 | ||
---|---|---|---|---|---|
Assignment(Including Essay): There will be one small assignment worth 2% due in week 3 and two main 2 assignments, each worth 19% due in week 6 and 10. | n/a | Alternative linear conversion grade scale 40% | No | 40 |
|
Quizzes/Short Exercises: There will be 4 small tests, each worth 2.5%, which will be available on Brightspace. | n/a | Alternative linear conversion grade scale 40% | No | 10 |
|
Assignment(Including Essay): The project will involve using the R programming tools covered in the course. | 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 |