Data Analytics MSc
Data Mining and Machine Learning STATS5099
- Academic Session: 2026-27
- School: School of Mathematics and Statistics
- Credits: 10
- Level: Level 5 (SCQF level 11)
- Typically Offered: Either Semester 1 or Semester 2
- Available to Visiting Students: No
- Collaborative Online International Learning: No
- Curriculum For Life: No
Short Description
This course introduces students to modern data mining and machine learning techniques, with an emphasis on practical issues and applications.
Timetable
Mainly consists of asynchronous learning material and drop in tutorial help rooms
Excluded Courses
Data Mining and Machine Learning 1 (ODL)
Machine Learning
Machine Learning (Level M)
Assessment
End-of-course examination (80%); coursework (20%)
Main Assessment In: April/May
Are reassessment opportunities available for all summative assessments? No
It is the default expectation that all courses will offer opportunities for reassessment or deferred assessment. Where it is not possible to offer this in some assessment components, the grade achieved at the first attempt will be counted towards the final course grade, and any exceptions for this course are described below.
[No exceptions]
Course Aims
The aims of this course are:
■ to introduce students to different methods for dimension reduction and clustering (unsupervised learning);
■ to introduce students to a range of classification methods;
■ to equip students to apply machine learning methods to solve applied problems;
■ to train students to communicate the results of their analyses in clear non-technical language.
Intended Learning Outcomes of Course
By the end of this course students will be able to:
■ apply and interpret methods of dimension reduction such as principal component analysis;
■ apply and interpret classical methods for cluster analysis;
■ apply and interpret a wide range of methods for classification;
■ select appropriate machine learning methods to solve real-world problems of moderate complexity.