Introduction to statistical programming in R and Python STATS5103
- Academic Session: 2022-23
- 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
The course introduces students to statistical programming, the programming languages R and Python and their use for data programming and analytics.
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).
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.