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# STAT30340

#### Data Programming with R (STAT30340)

Subject:
Statistics & Actuarial Science
College:
Science
School:
Mathematics & Statistics
Level:
3 (Degree)
Credits:
5
Module Coordinator:
Dr Isabella Gollini
Trimester:
Autumn
Mode of Delivery:
On Campus
Internship Module:
No

Curricular information is subject to change.

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.

###### 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
Specified Learning Activities

26

Autonomous Student Learning

100

Lectures

12

Computer Aided Lab

12

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

50

No
Project: Coursework project Coursework (End of Trimester) n/a Alternative linear conversion grade scale 40% No

50

No

Carry forward of passed components
No

Resit In Terminal Exam
Spring No
###### 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