Curricular information is subject to change.
By the end of the module students should be have attained considerable mastery in R. They should be able to produce professional quality data analytics and documents via Rstudio which are suitable for use in a business environment.
| Student Effort Type | Hours |
|---|---|
| Specified Learning Activities | 24 |
| Autonomous Student Learning | 72 |
| Online Learning | 24 |
| Total | 120 |
Students must have completed one of the modules Data Programming or Data Programming with R
| Description | Timing | Component Scale | % of Final Grade | ||
|---|---|---|---|---|---|
| Assignment(Including Essay): Assignment due in Week 4 | Week 1, Week 2, Week 3, Week 4 | Alternative linear conversion grade scale 40% | No | 10 |
No |
| Quizzes/Short Exercises: Short exercises | Week 1, Week 2, Week 3, Week 4, Week 5, Week 6, Week 7, Week 8, Week 9, Week 10 | Alternative linear conversion grade scale 40% | No | 10 |
No |
| Assignment(Including Essay): Assignment due in week 7 | Week 1, Week 2, Week 3, Week 4, Week 5, Week 6, Week 7 | Alternative linear conversion grade scale 40% | No | 35 |
No |
| Assignment(Including Essay): Final Assignment due in week 10 | Week 1, Week 2, Week 3, Week 4, Week 5, Week 6, Week 7, Week 8, Week 9, Week 10 | Alternative linear conversion grade scale 40% | No | 45 |
No |
| Resit In | Terminal Exam |
|---|---|
| Autumn | No |
• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment
• Online automated feedback
Not yet recorded.
| Name | Role |
|---|---|
| John O'Sullivan | Lecturer / Co-Lecturer |
| Mr Brian Buckley | Tutor |