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2019/20 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 Arief Gusnanto 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 enrol on exactly 180 or 185 credits overall, with at least 135 credits at level 5M.

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
MATH5747MLearning Skills through Case Studies15 creditsSemester 2
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

COMP3211Distributed Systems10 creditsSemester 1
COMP3611Machine Learning10 creditsSemester 1
COMP3736Information Visualization10 creditsSemester 1
COMP5111MBig Data Systems15 creditsSemester 2
COMP5112MData Management15 creditsNot running in 201920
COMP5400MBio-Inspired Computing15 creditsSemester 2
COMP5450MKnowledge Representation and Reasoning15 creditsSemester 1
COMP5623MArtificial Intelligence15 creditsSemester 2
COMP5700MSystems Programming15 creditsNot running in 201920
COMP5710MAlgorithms15 creditsNot running in 201920
COMP5711MPractical Programming15 creditsSemester 1
COMP5840MData Mining and Text Analytics15 creditsSemester 2
COMP5850MCloud Computing15 creditsSemester 2
COMP5860MSemantic Technologies and Applications15 creditsSemester 2
COMP5920MScheduling15 creditsSemester 2
COMP5930MScientific Computation15 creditsSemester 1
COMP5940MGraph Theory: Structure and Algorithms15 creditsSemester 2

List B

MATH3714Linear Regression and Robustness15 creditsSemester 1
MATH3723Statistical Theory15 creditsSemester 2
MATH3734Stochastic Calculus for Finance15 creditsSemester 2
MATH3772Multivariate Analysis10 creditsSemester 1
MATH3802Time Series10 creditsSemester 1
MATH3820Bayesian Statistics10 creditsSemester 2
MATH3823Generalised Linear Models10 creditsSemester 2
MATH5741MStatistical Theory and Methods15 creditsSemester 1
MATH5743MStatistical Learning15 creditsSemester 2
MATH5745MMultivariate Methods15 creditsSemester 2
MATH5772MMultivariate and Cluster Analysis15 creditsSemester 1
MATH5802MTime Series and Spectral Analysis15 creditsSemester 1
MATH5820MBayesian Statistics and Causality15 creditsSemester 2
MATH5824MGeneralised Linear and Additive Models15 creditsSemester 2
MATH5835MStatistical Computing15 creditsSemester 1

List C

GEOG5042MGeographic Data Visualisation & Analysis15 creditsSemester 1
GEOG5255MGeodemographics and Neighbourhood Analysis15 creditsSemester 2
GEOG5917MBig Data and Consumer Analytics15 creditsSemester 2
GEOG5927MPredictive Analytics15 creditsSemester 1
GEOG5937MApplied GIS and Retail Modelling15 creditsSemester 2
LUBS5221MEffective Decision Making15 creditsSemester 1
LUBS5253MAdvanced Management Decision Making15 creditsSemester 2
LUBS5308MBusiness Analytics and Decision Science15 creditsSemester 1
LUBS5309MForecasting and Advanced Business Analytics15 creditsSemester 2
TRAN5340MTransport Data Science15 creditsSemester 2

Last updated: 06/01/2020 12:17:50


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