R Programming (ODL) STATS5078
- Academic Session: 2019-20
- School: School of Mathematics and Statistics
- Credits: 10
- Level: Level 5 (SCQF level 11)
- Typically Offered: Semester 1
- Available to Visiting Students: No
- Available to Erasmus Students: No
- Taught Wholly by Distance Learning: Yes
The course introduces students to programming in the statistical software environment R.
The course mostly consists of asynchronous teaching material.
Requirements of Entry
The course is only available to online-distance learning students on the PGCert/PGDip/MSc in Data Analytics and Data Analytics for Government.
Introduction to R Programming
Introduction to R Programming (Level M)
Statistics 3R: Introduction to R Programming
100% Continuous Assessment
This will typically be made up of a project (20%), one oral assessment (20%) and three homework exercises (60%).
Full details are provided in the programme handbook.
The aims of this course are:
■ to introduce students to the basic concepts and ideas of a statistical computing environment;
■ to train students in programming tools using the R computing environment
■ to provide computational skills which will support other courses on the programme; and
■ to introduce students to fundamental concepts in (scientific) programming in general.
Intended Learning Outcomes of Course
By the end of this course students will be able to:
■ recognise and make appropriate use of different types of data structures;
■ use R to create figures and graphs;
■ identify and implement appropriate control structures to solve a particular programming problem;
■ design and write functions in R and implement simple iterative algorithms;
■ structure complex programming problems into functional units and implement these;
■ carry out extended programming tasks and produce clearly annotated listing of their code;
■ author reports with embedded code using technologies such as Sweave or knitr; and
■ develop and deploy R Shiny apps.
Minimum Requirement for Award of Credits
Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.