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STAT40730

Academic Year 2023/2024

Data Programming with R (Online) (STAT40730)

Subject:
Statistics & Actuarial Science
College:
Science
School:
Mathematics & Statistics
Level:
4 (Masters)
Credits:
5
Module Coordinator:
Dr Isabella Gollini
Trimester:
Autumn
Mode of Delivery:
Online
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

This module introduces students with no previous programming experience to the open-source statistical programming language R. Topics include: manipulating vectors, matrices, arrays and lists; basic programming constructs and programme flow; graphical methods; dealing with large data sets; simple statistical methods.

About this Module

Learning Outcomes:

At the end of the course students should be able to use R to:
- Load in and manipulate data sets of any size and structure
- Find help and use functions which they have not met before
- Create professional quality graphical summaries of data
- Perform simple statistical analyses

Student Effort Hours:
Student Effort Type Hours
Specified Learning Activities

24

Autonomous Student Learning

72

Online Learning

24

Total

120


Approaches to Teaching and Learning:
Weekly video lecture and screencast material, non-assessed lab sheets.

Requirements, Exclusions and Recommendations
Learning Requirements:

Students must have had previous experience of using computers, including web searching and creating spreadsheets.

Learning Recommendations:

Some familiarity with Microsoft Office (or equivalent), programming concepts such as loops and functions.


Module Requisites and Incompatibles
Incompatibles:
STAT30340 - Data Programming with R, STAT40180 - Data Programming with R, STAT40620 - Data Programming with R


 

Assessment Strategy
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Continuous Assessment: Computer Laboratories Throughout the Trimester n/a Standard conversion grade scale 40% No
40
No
Multiple Choice Questionnaire: Multiple choice assessments Varies over the Trimester n/a Standard conversion grade scale 40% No
10
No
Project: Coursework project Coursework (End of Trimester) n/a Standard conversion grade scale 40% No
50
No

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
Professor Brendan Murphy Lecturer / Co-Lecturer
John O'Sullivan Lecturer / Co-Lecturer
Brian O'Sullivan Tutor