Please note: there may be some adjustments to the teaching arrangements published in the course catalogue for 2020-21. Given current circumstances related to the Covid-19 pandemic it is anticipated that some usual arrangements for teaching on campus will be modified to ensure the safety and wellbeing of students and staff on campus; further adjustments may also be necessary, or beneficial, during the course of the academic year as national requirements relating to management of the pandemic are revised.

Statistical Computing (ODL) STATS5097

  • Academic Session: 2021-22
  • 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

Short Description

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 -/-Data Analytics for Government.

Excluded Courses

Introduction to R Programming

Introduction to R Programming (Level M)

Statistics 3R: Introduction to R Programming

R Programming (ODL)




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.

Course Aims

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.