STAT40730 Data Programming with R (Online)

Academic Year 2021/2022

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.

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Curricular information is subject to change

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

50

Continuous Assessment: Computer Laboratories Throughout the Trimester n/a Standard conversion grade scale 40% No

40

Multiple Choice Questionnaire: Multiple choice assessments Varies over the Trimester n/a Standard conversion grade scale 40% No

10


Carry forward of passed components
No
 
Resit In Terminal Exam
Spring Yes - 2 Hour
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 Nial Friel Lecturer / Co-Lecturer
Professor Claire Gormley Lecturer / Co-Lecturer
Professor Brendan Murphy Lecturer / Co-Lecturer
Mr John O'Sullivan Tutor
Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
 

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