R Programming (online) STATS5078

  • Academic Session: 2018-19
  • 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.

Timetable

The course consists of short online lessons (each usually of at most 30 minutes length), totalling around 15-20 hours. Embedded in these lessons are formative quizzes and assessment tasks (not included in the above duration). These are flexible and can be taken (and re-taken) at any time. There also are 6-10 hours of programming labs

Requirements of Entry

The course is only available to students on the online MSc in Data Analytics.

Excluded Courses

Introduction to R Programming

Introduction to R Programming (Level M)

Statistics 3R: Introduction to R Programming

Co-requisites

-/-

Assessment

100% Continuous Assessment

Project work is assessed by a report as well as an oral assessment and/or video presentation.

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.