STAT30340 Data Programming with R

Academic Year 2023/2024

R is a free, open-source programming language and software environment designed for data analysis, graphics, and statistical computing. It is continually updated by contributors from all over the world who add new packages that implement a range of statistical and machine learning techniques. R is widely used by academic researchers and businesses globally.
This module provides an introduction to R, covering essential topics such as manipulating vectors, matrices, arrays, and lists, programming constructs, flow control, graphical and visualisation methods, working with large datasets, basic data analysis and statistical methods, as well as document and report creation.

Show/hide contentOpenClose All

Curricular information is subject to change

Learning Outcomes:

At the end of the course, students should have attained an intermediate-level knowledge of R. They should be able to use R to:
- Manipulate data sets of any size and structure.
- Program and implement efficient algorithms.
- Create professional quality graphical summaries of data.
- Perform statistical analyses.
- Produce data analysis documents.

Student Effort Hours: 
Student Effort Type Hours
Lectures

12
Computer Aided Lab

12
Specified Learning Activities

26
Autonomous Student Learning

100
Total

150
Approaches to Teaching and Learning:
Lectures and lab practical sessions. 
Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Incompatibles:
STAT40730 - Data Prog with R (Online)


 
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Project: Coursework project Coursework (End of Trimester) n/a Alternative linear conversion grade scale 40% No

50
Continuous Assessment: Computer labs, multiple choice questionnaires, assignments Varies over the Trimester n/a Alternative linear conversion grade scale 40% No

50

Carry forward of passed components
No
 
Resit In Terminal Exam
Spring No
Please see Student Jargon Buster for more information about remediation types and timing. 
Feedback Strategy/Strategies

• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment
• Online automated feedback

How will my Feedback be Delivered?

Not yet recorded.

Name Role
Manisha Ganguly Tutor
Ms Claire Mullen Tutor
Thais Pacheco Menezes Tutor
Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
 
Autumn
      
Lecture Offering 1 Week(s) - Autumn: All Weeks Thurs 10:00 - 10:50
Laboratory Offering 1 Week(s) - Autumn: Weeks 2-12 Thurs 09:00 - 09:50
Laboratory Offering 2 Week(s) - Autumn: Weeks 2-12 Thurs 11:00 - 11:50
Laboratory Offering 3 Week(s) - Autumn: Weeks 2-12 Tues 16:00 - 16:50
Laboratory Offering 4 Week(s) - Autumn: Weeks 2-12 Mon 09:00 - 09:50
Autumn