Postgraduate taught 

Advanced Statistics MRes

Introduction to statistical programming in R and Python STATS5103

  • Academic Session: 2023-24
  • School: School of Mathematics and Statistics
  • Credits: 20
  • Level: Level 5 (SCQF level 11)
  • Typically Offered: Either Semester 1 or Semester 2
  • Available to Visiting Students: No

Short Description

The course introduces students to statistical programming, the programming languages R and Python and their use for data programming and analytics.



Excluded Courses

STATS4044 Introduction to R Programming

STATS3017 Statistics 3R: Introduction to R Programming


100% continuous assessment:

This will typically be made up of 4 assessments (e.g. timed class tests or take-home assessments worth 25% each).

Course Aims

To introduce students to the basic concepts and ideas of a statistical computing environment; to train students in programming tools using the R and Python computing environments; to provide computational skills which will support other M-level courses; 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:

■ design and implement functions in R and Python;

■ make efficient use of the data structures built into R and Python;

■ use R and Python to create informative and interpretable figures and graphs;

■ identify and implement appropriate control structures to solve a particular programming problem in R and Python; 

■ carry out extended programming tasks and produce clearly annotated listing of their code

■ describe and exploit features of classes and object-oriented design

■ implement data-analytic tasks (e.g. using external libraries such as scikit-learn, NumPy/SciPy and pandas)

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.