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2019/20 Taught Postgraduate Programme Catalogue

MSc Medical Statistics

Programme code:MSC-STAT/MDUCAS code:
Duration:12 Months Method of Attendance: Full Time
Programme manager:Dr Arief Gusnanto Contact address:arief@maths.leeds.ac.uk

Total credits: 180

Entry requirements:

2:1 in a first degree with a substantial statistical and mathematical component.

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:

At the end of the programme, students should:
- appreciate theoretical and practical perspectives on issues in medical statistics;
- be able to select and apply appropriate statistical methods for the analysis of medical data, using suitably chosen software packages;
- be prepared to embark on a programme of research as a research student or a career as a medical statistician;
- be able to explain statistical methods and results of statistical analysis in both written and verbal form, to both technical and non-technical audiences;
- have undertaken a substantial statistical project under supervision.


Year1 - View timetable

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

Students 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:

Students will be required to study the following compulsory modules:

EPIB3036Introduction to Clinical Trials15 creditsSemester 2 (Jan to Jun)
EPIB5040MIntroduction to Health Data Science15 creditsSemester 1 (Sep to Jan)
EPIB5042MModelling Prediction and Causality with Observational Data15 creditsSemester 1 (Sep to Jan)
MATH5835MStatistical Computing15 creditsSemester 1 (Sep to Jan)

Optional modules:

Students must take both modules from either List A or List B.

They are also required to study at least 25 credits from Lists C and D including at least one module from List C.

LIST A

MATH5825MIndependent Learning and Skills Project15 creditsSemester 2 (Jan to Jun)
MATH5871MDissertation in Statistics60 credits1 Jun to 30 Sep

LIST B

EPIB5001MResearch Project60 credits1 Oct to 30 Sep (12mth)
EPIB5044MProfessional Skills for Health Data Scientists15 creditsSemester 2 (Jan to Jun)

LIST C

MATH3714Linear Regression and Robustness15 creditsSemester 1 (Sep to Jan)
MATH3723Statistical Theory15 creditsSemester 2 (Jan to Jun)
MATH3772Multivariate Analysis10 creditsSemester 1 (Sep to Jan)
MATH3802Time Series10 creditsSemester 1 (Sep to Jan)
MATH3820Bayesian Statistics10 creditsSemester 2 (Jan to Jun)
MATH3823Generalised Linear Models10 creditsSemester 2 (Jan to Jun)
MATH5714MLinear Regression, Robustness and Smoothing20 creditsSemester 1 (Sep to Jan)
MATH5772MMultivariate and Cluster Analysis15 creditsSemester 1 (Sep to Jan)
MATH5802MTime Series and Spectral Analysis15 creditsSemester 1 (Sep to Jan)
MATH5820MBayesian Statistics and Causality15 creditsSemester 2 (Jan to Jun)
MATH5824MGeneralised Linear and Additive Models15 creditsSemester 2 (Jan to Jun)

LIST D

EPIB5043MFurther techniques in Health Data Analytics15 creditsSemester 1 (Sep to Jan)
EPIB5045MModelling Strategies for Causal Inference with Observational Data15 creditsSemester 2 (Jan to Jun)
EPIB5046MLatent Variable Methods15 creditsSemester 2 (Jan to Jun)
EPIB5047MIndependent Skills in Health Data Analytics15 creditsSemester 2 (Jan to Jun)

Elective modules:

Student may choose up to 20 credits of elective/Discovery modules at Level 3 or 5M, which should normally include a substantive statistical component, and are subject to the written approval of the Programme Manager.

Last updated: 30/04/2019

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