MSc Data Science and Analytics

Programme overview

Entry requirements

BSc (or equivalent) in a subject containing a substantial numerate component, usually at level 2.1 or above (or equivalent).

Programme specification

The programme will equip students with the necessary knowledge and skills in data science. Students on this programme will be taught by experts from different academic units: the School of Mathematics (SoM), the School of Computing (SoC), the School of Geography (SoG), and the School of Business (LUBS). In addition to that, three new modules in total are proposed in the SoM for students who are not from a mathematics/statistics background, while modules in the SoC will be suitable for students on this programme who are not from a computer science background. The programme will therefore expose students to different perspectives on data science, including the mathematical and computational underpinnings of the subject and practical understanding of application in a specific context. In particular, we anticipate many projects for the dissertation will span at least two units with joint supervision. As well as emphasizing the application nature of the programme, the dissertation will feature strongly data elucidation, analysis, and interpretation of real-world problems.

Year 1

[Learning Outcomes, Transferable (Key) Skills, Assessment]
View Timetable

Compulsory Modules

Candidates will be required to study the following compulsory modules:

Code	Title	Credits	Semester	Pass for Progression
COMP5122M	Data Science	15	Semester 1 (Sep to Jan)
MATH5720M	Project Skills in Data Science	15	Semester 2 (Jan to Jun)
MATH5751M	Project in Data Science and Analytics	60	1 Jun to 30 Sep	PFP

Optional Modules

Candidates are required to select 1 module from each of lists A,B, C and D. For the remaining 30 credits, students can only select 1 module from lists A and B, and one module from lists C and D, this is to keep the split of credits to 60 in each semester. Students without significant components of mathematics and statistics in previous studies should select MATH5741M and MATH5743M from list A and C respectively.

List A:

Code	Title	Credits	Semester	Pass for Progression
MATH5702M	Statistical Computing	15	Semester 1 (Sep to Jan)
MATH5705M	Multivariate Data Analysis	15	Semester 1 (Sep to Jan)
MATH5741M	Statistical Theory and Methods	15	Semester 1 (Sep to Jan)

List B:

Code	Title	Credits	Semester	Pass for Progression
COMP5450M	Knowledge Representation and Reasoning	15	Semester 1 (Sep to Jan)
COMP5712M	Programming for Data Science	15	Semester 1 (Sep to Jan)
GEOG5042M	Geographic Data Visualisation & Analysis	15	Semester 1 (Sep to Jan)
LUBS5308M	Business Analytics and Decision Science	15	Semester 1 (Sep to Jan)

List C:

Code	Title	Credits	Semester	Pass for Progression
MATH5704M	Time Series Data Analysis	15	Semester 2 (Jan to Jun)
MATH5706M	Further Models for Data Analysis	15	Semester 2 (Jan to Jun)
MATH5743M	Statistical Learning	15	Semester 2 (Jan to Jun)

List D:

Code	Title	Credits	Semester	Pass for Progression
COMP5611M	Machine Learning	15	Semester 2 (Jan to Jun)
COMP5625M	Deep Learning	15	Semester 2 (Jan to Jun)
GEOG5917M	Big Data and Consumer Analytics	15	Semester 2 (Jan to Jun)
LUBS5309M	Forecasting and Advanced Business Analytics	15	Semester 2 (Jan to Jun)
LUBS5990M	Machine Learning in Practice	15	Semester 2 (Jan to Jun)

Last updated: 12/05/2026 16:42:23

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