|Module Name||Statistical Analysis III|
|ECTS Weighting||5 ECTS|
|Semester taught||Semester 1|
|Module Coordinator/s||Susan Connolly|
Module Learning Outcomes
On successful completion of this module, students will be able to:
LO1. Understand and put into practice merging and cleaning of datasets
LO2. Understand and put into practice use of inbuilt and user written
LO3. Understand the graphics capabilities of R and use these methods to
visualise data and create reports
LO4. Understand the use of clustering methods and their application to
different data types
LO5. Understand the use of Generalized linear models and their application to
different data types
LO6. Understand methods of Classification and their application to different data types
LO7. Principles of effective report writing and how to present research and analysis.
This module aims to provide an opportunity for students to develop their hands on
skills in data analysis. Specific methods will be explored to illustrate these
approaches. Students will become very familiar with the R statistical computing
language. After this course, students will have a toolbox of skills for data analysis. In
particular, students should be able to apply their statistical knowledge to a
real scenario, do analysis and make recommendations.
Teaching and learning Methods
1 hour lecture and 2 hours of lab per week
Lectures will introduce theory, methods, and examples. Labs will put these methods
into practice in R Studio.
|Assessment Component||Brief Description||Learning Outcomes Addressed||% of total||Week set||Week Due|
|Lab Work||Lab assessments||all||40||n/a|||
Reassessment is by an assigned project.
Contact Hours and Indicative Student Workload
|Contact Hours (scheduled hours per student over full module), broken down by:||31 hours|
|Tutorial or seminar||0 hours|
|Independent study (outside scheduled contact hours), broken down by:||70 hours|
|Preparation for classes and review of material (including preparation for examination, if applicable||30 hours|
|completion of assessments (including examination, if applicable)||50 hours|
|Total Hours||101 hours|
Recommended Reading List
Stuart, M. An Introduction to Statistical Analysis for Business and Industry A
problem Solving approach. London: Hodder Arnold, 2003
Moore, D.S, McCabe G.P & Craig, B.A. An Introduction to the practice of
Statistics 6th ed. New York: W. H. Freeman, 2009
R for Data Science (available free online at https://r4ds.had.co.nz/)
Prerequisite modules: None.
Other/alternative non-module prerequisites: None.