STAT40180 Data Programming with R

Academic Year 2019/2020

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
Lectures

12
Laboratories

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
Learning Requirements:

Students must have had previous experience of using computers, including web searching and creating spreadsheets. Some familiarity with basic statistical methods (mean and variance, correlation, linear regression) is expected.

Learning Recommendations:

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


Module Requisites and Incompatibles
Incompatibles:
STAT40620 - Data Programming with R, STAT40730 - Data Prog with R (Online)


 
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Examination: End of semester Lab Exam 2 hour End of Trimester Exam Yes Standard conversion grade scale 40% No

70
Continuous Assessment: Computer labs Throughout the Trimester n/a Standard conversion grade scale 40% No

30

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
Dr Vasiliki Dimitrakopoulou Lecturer / Co-Lecturer
Professor Nial Friel Lecturer / Co-Lecturer
Professor Brendan Murphy Lecturer / Co-Lecturer