UV9918V6 – Introduction to R: a free software environment for statistical computing and graphics
Schedule, syllabus and examination date
Course content
R is a free software environment (https://www.r-project.org/) for statistical computing and graphics that has gained much popularity in the recent years and is widely used in both academia and industry. The course introduces the essential concepts and syntax for getting started with R. Although some programming basics will be covered, problem-based scenarios including daily routine data management tasks will take center piece.
Learning outcome
After completing the course, you
- know basic programming concepts and are familiar with the R operating system
- can run, modify, and write R syntax to perform routine data management tasks
have the necessary knowledge and skills to start learning R beyond the introductory level
Admission
PhD candidates at the Faculty of Educational Sciences will be given priority, but it is also possible for others to apply for the course. Deadline for registration is February 15th.
Candidates admitted to a PhD-program at the Faculty of Educational Sciences (UV): Apply by Studentweb.
Other applicants: apply through Nettskjema.
Maximum number of participants is 25.
Prerequisites
Formal prerequisite knowledge
No prior knowledge is assumed, but do bring your laptop to class for the hands-on computer lab parts.
Teaching
Organizer: CEMO (Centre for Educational Measurement at University of Oslo)
Coordinator/Responsible: Johan Braeken, Stefan Schauber and Björn Andersson
Teaching: 4 sessions, 09:00-15:00, March 1st, 2nd, 12th and 16th
Location: schedule available here
The course will include lectures in combination with in-class computer exercises, and homework assignments in the free statistical software environment R.
Literature
Grolemund, G. & Wickham, H. (2017). R for Data Science: http://r4ds.had.co.nz/. (480 pages)
Optional further reading: Wickham, H. (2014). Advanced R: http://adv-r.had.co.nz/. (450 pages)
Examination
To obtain 1 credit, 80 % attendance in the course is required. To obtain 3 credits, 80 % attendance and a successfully completed assignment is required. A more specific description of the assignment will be given at the course.
Grading scale
Grades are awarded on a pass/fail scale. Read more about the grading system.