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2020/21 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 address:L.Cutillo@leeds.ac.uk

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 enrol on exactly 180 or 185 credits overall, with at least 135 credits at level 5M. Please note that students must pass 135 credits at level 5M to gain the MSc.

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 2 (Jan to Jun), 1 Jun to 30 Sep
MATH5747MLearning Skills through Case Studies15 credits1 Jan to 31 May, 1 May to 30 Sep
MATH5872MDissertation in Data Science and Analytics60 credits1 Jun to 30 Sep, 1 Aug to 31 Oct

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.

COMP3736Information Visualization10 creditsSemester 1 (Sep to Jan)
COMP5611MMachine Learning15 credits1 Sep to 31 Jan (adv yr), Semester 1 (Sep to Jan)
COMP5623MArtificial Intelligence15 creditsSemester 2 (Jan to Jun)
COMP5712MProgramming for Data Science15 creditsSemester 1 (Sep to Jan), 1 Jun to 30 Sep, 1 Jan to 31 May, 1 Sep to 31 Jan (adv yr)
COMP5840MData Mining and Text Analytics15 creditsSemester 2 (Jan to Jun)
GEOG5042MGeographic Data Visualisation & Analysis15 credits1 Jan to 31 May
GEOG5937MApplied GIS and Retail Modelling15 credits1 Jan to 31 May
LUBS5308MBusiness Analytics and Decision Science15 credits1 Jan to 31 May
MATH2775Survival Analysis10 creditsSemester 2 (Jan to Jun)
MATH3092Mixed Models10 creditsSemester 2 (Jan to Jun), 1 Jan to 31 May
MATH3714Linear Regression and Robustness15 creditsSemester 1 (Sep to Jan)
MATH3723Statistical Theory15 creditsSemester 1 (Sep to Jan)
MATH3802Time Series10 credits1 Sep to 31 Dec (adv yr), 1 Jan to 31 May
MATH3823Generalised Linear Models10 credits1 Jan to 31 May
MATH5092MMixed Models with Medical Applications15 credits1 Jan to 31 May
MATH5714MLinear Regression, Robustness and Smoothing20 credits1 May to 30 Sep, Semester 1 (Sep to Jan)
MATH5741MStatistical Theory and Methods15 credits1 Jan to 31 May, Semester 1 (Sep to Jan)
MATH5743MStatistical Learning15 credits1 Jan to 31 May, 1 May to 30 Sep
MATH5745MMultivariate Methods15 credits1 Jan to 31 May
MATH5772MMultivariate and Cluster Analysis15 credits1 May to 30 Sep, Semester 1 (Sep to Jan)
MATH5802MTime Series and Spectral Analysis15 credits1 Jan to 31 May, 1 May to 30 Sep
MATH5824MGeneralised Linear and Additive Models15 credits1 Jan to 31 May
MATH5835MStatistical Computing15 creditsSemester 1 (Sep to Jan), 1 May to 30 Sep
TRAN5340MTransport Data Science15 creditsSemester 2 (Jan to Jun)
YCHI5010MInformatics in Health Care15 creditsSemester 1 (Sep to Jan)

Last updated: 24/05/2021 09:54:00

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