Postgraduate Course: Intermediate R Programming for Data Science (on campus) (HEIN11095)
Course Outline
| School | Deanery of Molecular, Genetic and Population Health Sciences |
College | College of Medicine and Veterinary Medicine |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | This course builds on the basics of R and introduces students to more complex operations, such as: string manipulation, writing and debugging functions, iteration (loops and the map family of functions). After completing this course, students will be more confident in using R for larger data projects. |
| Course description |
This course is designed to help students develop their skills in R programming - the assumption is that students on the course will already be able to perform basic operations on R, but this course will help them become more confident and more effective programmers.
The course is delivered in a flipped classroom format and is divided into five sessions, each lasting a week. Teaching sessions will be composed of written materials, video presentations, coding exercises, and problem-based learning questions. Throughout the course, students will engage with professional programming practices and tools (version control, code reviewing, debugging), and will have the opportunity to collaborate with peers to develop their skills.
Formative peer and teacher-led feedback will be given throughout the course through the discussion boards, and summative assessment feedback will be provided at the end of the course.
In-person seminars will be scheduled to further reinforce the learning and provide a sense of academic cohort for the students. In these sessions, the students will have the opportunity to learn about how the course instructors apply intermediate R programming techniques in their own practice, and to receive support with their ideas for assessment.
|
Course Delivery Information
|
| Academic year 2025/26, Not available to visiting students (SS1)
|
Quota: None |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 5,
Seminar/Tutorial Hours 1,
Online Activities 35,
Feedback/Feedforward Hours 5,
Formative Assessment Hours 5,
Revision Session Hours 1,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
46 )
|
| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
Written exam 0%, Coursework 100%, Practical Exam 0% |
| Feedback |
Formative feedback will be provided throughout the course, for example, during live question and answer sessions, quizzes, and discussion boards. Summative written feedback will be given on both assessment components. Feedback on the code review will be provided by the instructor as well as peers. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Demonstrate a critical understanding of the data that are used in healthcare and social service organisations, and of the analytical approaches that can be used to process the data.
- Apply a significant range of logical, analytical, and problem-solving skills to effectively use the R programming language to wrangle and analyse data.
- Demonstrate the ability to communicate about the analytical choices made and approaches adopted.
|
Reading List
Grolemund, G. & Wickman, H. (2017). R for Data Science. O'Reilly Media.
Wickman, H. (2019). Advanced R. Chapman and Hall/CRC.
Both are available freely online under a creative commons license (links will be provided on the course page). |
Additional Information
| Graduate Attributes and Skills |
This course will enable the students to develop a wide range of Graduate Attributes and Skills that will contribute to their professional growth as successful data scientists and experts in their field.
Students will use skilled communication to enhance their understanding of the analytical challenges and to engage effectively with others.
They will become innovative, confident, and reflective lifelong learners, developing key analytical skills.
Students will use their personal and intellectual autonomy to critically evaluate the data from an open-minded and reasoned perspective.
Students will become effective and proactive individuals, skilled in the ability to identify and creatively tackle problems, influencing positively and adapting to new situations with sensitivity and integrity.
|
| Keywords | Health and social care data,R programming,data science |
Contacts
| Course organiser | Dr Brittany Blankinship
Tel:
Email: B.Blankinship@ed.ac.uk |
Course secretary | Miss Abbi Thomson
Tel:
Email: athoms6@ed.ac.uk |
|
|
University Homepage