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2023/24 Taught Postgraduate Programme Catalogue

MSc Data Science and Analytics

Programme code:MSC-DS&AUCAS code:
Duration:12 Months Method of Attendance: Full Time
Programme manager:Dr Luisa Cutillo Contact

Total credits: 180

Entry requirements:

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

School/Unit responsible for the parenting of students and programme:

School of Mathematics

Examination board through which the programme will be considered:

School of Mathematics

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.

Year1 - View timetable

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

Candidates must enroll on exactly 180 or 185 credits overall, with at least 135 credits at level 5M. Please note that in order to obtain the MSc award students need to pass 150 credits with at least 135 at level 5 with a minimum classification average of 5.0. Please refer to the 'rules of award' document for further details, with particular attention to section 16.

Students will be awarded the PGCert if they exit with 60 credits (including 45 at Level 5M), or the PGDip if they exit with 90 credits (including 75 at Level 5M).

Compulsory modules:

Candidates will be required to study the following compulsory modules:

COMP5122MData Science15 creditsSemester 1 (Sep to Jan)
MATH5747MLearning Skills through Case Studies15 creditsSemester 2 (Jan to Jun)
MATH5872MDissertation in Data Science and Analytics60 credits1 Jun to 30 Sep

Optional modules:

Remaining credits need to be chosen from the following lists, with at least 30 credits from each of lists A and B. Options may be selected from list C. The final choice requires approval from the Programme Manager.

List A

COMP3736Information Visualization10 creditsSemester 1 (Sep to Jan)
COMP5450MKnowledge Representation and Reasoning15 creditsSemester 1 (Sep to Jan)
COMP5611MMachine Learning15 creditsSemester 2 (Jan to Jun)
COMP5625MDeep Learning15 creditsSemester 2 (Jan to Jun)
COMP5712MProgramming for Data Science15 creditsSemester 1 (Sep to Jan)
COMP5840MData Mining and Text Analytics15 creditsSemester 2 (Jan to Jun)

List B

MATH3092Mixed Models10 creditsSemester 2 (Jan to Jun)
MATH3714Linear Regression and Robustness15 creditsSemester 1 (Sep to Jan)
MATH3723Statistical Theory15 creditsSemester 2 (Jan to Jun)
MATH3802Time Series10 creditsSemester 1 (Sep to Jan)
MATH3823Generalised Linear Models10 creditsSemester 2 (Jan to Jun)
MATH5092MMixed Models with Medical Applications15 creditsSemester 2 (Jan to Jun)
MATH5714MLinear Regression, Robustness and Smoothing20 creditsSemester 1 (Sep to Jan)
MATH5741MStatistical Theory and Methods15 creditsSemester 1 (Sep to Jan)
MATH5743MStatistical Learning15 creditsSemester 2 (Jan to Jun)
MATH5745MMultivariate Methods15 creditsSemester 2 (Jan to Jun)
MATH5772MMultivariate and Cluster Analysis15 creditsSemester 1 (Sep to Jan)
MATH5802MTime Series and Spectral Analysis15 creditsSemester 1 (Sep to Jan)
MATH5824MGeneralised Linear and Additive Models15 creditsSemester 2 (Jan to Jun)
MATH5835MStatistical Computing15 creditsSemester 1 (Sep to Jan)

List C

GEOG5042MGeographic Data Visualisation & Analysis15 creditsSemester 1 (Sep to Jan)
GEOG5255MGeodemographics and Neighbourhood Analysis15 creditsSemester 2 (Jan to Jun)
GEOG5917MBig Data and Consumer Analytics15 creditsSemester 2 (Jan to Jun)
GEOG5927MPredictive Analytics15 creditsSemester 2 (Jan to Jun)
GEOG5937MApplied GIS and Retail Modelling15 creditsSemester 1 (Sep to Jan)
LUBS5308MBusiness Analytics and Decision Science15 creditsSemester 1 (Sep to Jan)
LUBS5309MForecasting and Advanced Business Analytics15 creditsSemester 2 (Jan to Jun)
LUBS5990MMachine Learning in Practice15 creditsSemester 2 (Jan to Jun)
TRAN5340MTransport Data Science15 creditsSemester 2 (Jan to Jun)

Last updated: 20/02/2024 15:58:00


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