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STAT40830

Academic Year 2024/2025

Adv Data Prog with R (online) (STAT40830)

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

Curricular information is subject to change.

In this module we cover advanced use of R and Rstudio, following on from the Data Programming with R module. Topics are subject to change each year as the software progresses but are likely to include:
- Professional graphics through ggplot2
- Advanced function behaviour
- Advanced data processing with R through tidyverse packages
- Creation of interactive documents and presentations with Rstudio, markdown and knitr
- Creation of interactive applications through shiny
- Step-by-step guidance on creating R packages

About this Module

Learning Outcomes:

By the end of the module students should be have attained considerable mastery in R. They should be able to produce professional quality data analytics and documents via Rstudio which are suitable for use in a business environment.

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 lectures, weekly computer labs, homework assignments.

Requirements, Exclusions and Recommendations
Learning Requirements:

Students must have completed one of the modules Data Programming or Data Programming with R


Module Requisites and Incompatibles
Not applicable to this module.
 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Assignment(Including Essay): Assignment due in Week 4 Week 1, Week 2, Week 3, Week 4 Alternative linear conversion grade scale 40% No
10
No
Quizzes/Short Exercises: Short exercises Week 1, Week 2, Week 3, Week 4, Week 5, Week 6, Week 7, Week 8, Week 9, Week 10 Alternative linear conversion grade scale 40% No
10
No
Assignment(Including Essay): Assignment due in week 7 Week 1, Week 2, Week 3, Week 4, Week 5, Week 6, Week 7 Alternative linear conversion grade scale 40% No
35
No
Assignment(Including Essay): Final Assignment due in week 10 Week 1, Week 2, Week 3, Week 4, Week 5, Week 6, Week 7, Week 8, Week 9, Week 10 Alternative linear conversion grade scale 40% No
45
No

Carry forward of passed components
No
 

Resit In Terminal Exam
Autumn 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
John O'Sullivan Lecturer / Co-Lecturer
Mr Brian Buckley Tutor