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Master of Science in Applied Statistics
Coursework 41514. Research 41521
Normally a learner shall not be admitted to the Masters programme in Applied Statistics unless he/she
has obtained an aggregate of at least 60% for the Honours degree.
The qualification may be obtained in one of two ways, i.e. –
An extended dissertation (STA 701, 256 credits); OR
A dissertation of limited scope (128 credits) plus three papers (each
carrying 40 credits) from those listed below. The Masters papers listed
below cover the same topics as the Honours papers, but in greater
depth. A maximum of two papers (50 credits) may be selected from
Masters papers offered in Applied Mathematics, Computer Science,
GIS and/or Mathematics. Any selection must be made in consultation
with the Head of Department.
STA 702:
A half dissertation (128 credits),
STA 704:
Advanced Multivariate Statistical Analysis
Contents:
Wishart Distribution and an introduction to Zonal
Polynomials. Introduction to Differential Geometry
and Statistical Manifolds. Extreme Value
Distributions and their use in the Investigation of the
Probabilities of Rare Events (40 Credits).
STA 709:
Advanced Experimental Design
Contents:
Analysis of Variance, unbalanced and nested factors: Complete randomised design,
Randomised complete block design, Latin squares split-plot and repeated measures
design: incomplete block, fractional factorial, response surface designs; confounding.
(40 Credits).
Prerequisites: STS 504, STM 506.
STD 701:
Generalised Mixed Models. (Previously STS 701)
Contents:
Linear Models. Generalised Linear Models. Mixed Models. Generalised Mixed
Models (40 Credits).
STD 702:
Applied Non-parametric Statistics (Previously STS 702)
Contents:
Nonparametric testing and estimation procedures are introduced. Topics include one
sample location problem, two sample location problem, two sample dispersion
problem, one-way layout, independence problem and regression problems (40
Credits).
STD 703:
Advanced Time Series Analysis (Previously STS 702)
Contents:
The course deals primarily with spectral theory of time series. Spectral representation
of stationary stochastic processes with discrete and continuous time, autoregressive
and moving average models, theory of filtering and prediction of time series,
parametric and nonparametric spectral estimation will be discussed in detail. In
addition to this, the course will include an introduction to Kalman filtering and state-
space models (40 Credits).
STD 704:
Applied Statistical Decision Theory (Previously STS 704)
Contents:
Introduction to loss function and risk, conditional Bayesian decision principle,
Frequentist Decision theory, Discision between Bayesian and frequentist, sufficient
statistic, convexity (40 Credits).
and any of the modules listed under Applied Statistics Honours not already offered by the candidate.
MASTER OF SCIENCE IN MATHEMATICAL STATISTICS
Coursework 41508 Research 41522
Normally a learner shall not be admitted to the Masters programme in Mathematical Statistics unless
he/she has obtained an aggregate of at least 60% for the Honours degree.
The qualification may be obtained in one of two ways, i.e. –
An extended dissertation (STA 700, 256 credits); OR
A dissertation of limited scope (128 credits), plus three papers (each carrying 40 credits) from those
listed below. The Masters papers listed below cover the same topics as the Honours papers, but in
greater depth. A maximum of two papers (40 credits) may be selected from Masters papers offered in
Applied Mathematics, Computer Science or Mathematical Statistics. Any selection must be made in
consultation with the Head of Department.
STA 702:
A half dissertation (128 credits),
STA 704:
Advanced Multivariate Statistical Analysis.
Contents:
Wishart Distribution and an introduction to Zonal Polynomials. Introduction to
Differential Geometry and Statistical Manifolds. Extreme Value Distributions and
their use in the Investigation of the Probabilities of Rare Events (40 Credits).
STA 705:
Advanced Regression Theory
Contents:
Exponential Family of Distributions; Generalized Linear Models; Random Effects
Models; Covariance Structures; Non-normal errors; Repeated Measures; Binary
data; Categorical data. Generalised Estimating Equation (40 Credits).
STA 709:
Advanced Experimental Design
Contents:
Analysis of Variance, unbalanced and nested factors: Complete randomised design,
Randomised complete block design, Latin squares split-plot and repeated measures
design: incomplete block, fractional factorial, response surface designs; confounding.
(40 Credits).
Prerequisites: STS 504, STM 506.
STA 707:
Methods of Multivariate Statistics
Contents:
Principal Components; Graphing Principal Components; Factor Rotation and Factor
Scores; Discrimination; Classification; Classification with two Multivariate Normal
Populations;
Clustering (40 Credits).
Credits:
40
STA 708:
Advanced Non-Parametric Statistics
Contents:
Non-Parametric testing and estimation procedures are introduced. Topics include
one- and two-sample location problems, one-way layouts independence and
regression problems (40 Credits).
STD 701:
Generalised Mixed Models.
Contents:
Linear Models. Generalised Linear Models. Mixed Models. Generalised Mixed
Models (40 Credits).
STD 702:
Applied Non-parametric Statistics
Contents:
Exponential Family of Distributions; Generalized Linear Models; Random Effects
Models; Covariance Structures; Non-normal errors; Repeated Measures; Binary
data; Categorical data. Generalised Estimating Equation (40 Credits).
STD 703:
Advanced Time Series Analysis
Contents:
The course deals primarily with spectral theory of time series. Spectral representation
of stationary stochastic processes with discrete and continuous time, autoregressive
and moving average models, theory of filtering and prediction of time series,
parametric and nonparametric spectral estimation will be discussed in detail. In
addition to this, the course will include an introduction to Kalman filtering and state-
space models (40 Credits).
STD 704:
Advanced Statistical Decision Theory
Contents:
Introduction to loss function and risk, conditional Bayesian decision principle,
Frequentist Decision theory, Decision between Bayesian and frequentist, sufficient
statistic, convexity (40 Credits).
STD 705:
Graphical Modelling
Contents:
Introduction to Theory of Graphs. Discrete Models. Continuous Models. Mixed
Models, Hypothesis testing. Model Selection and Criticism (40 Credits).
and any of the modules listed under Applied Statistics Honours and Mathematical Statistics Honours
not already offered by the candidate.
MASTER OF SCIENCE IN BIOSTATISTICS & EPIDEMIOLOGY 41515
This qualification will comprise three modules (of 40 credits each) and a dissertation (128 credits) of
limited scope, to be decided in consultation with the Head of Department.
STE 701:
Half dissertation (Biostatistics and epidemiology)
Contents:
Experimental Design; Data Collection; Data Analysis; Correct reporting of results.
(128 Credits)
STE 702:
Survival Analysis
Contents:
Estimation of Survival Curves using the Kaplan-Meier Estimator; Estimation of
instantaneous Mortality or Hazard Rates; Comparison of two or more Survival
Curves using a Log-Rank test; Fitting of Cox Model to data and the Assessment of
the Significance and Scientific Impact of included Predictors; Use of Time-Dependent
covariates in the Cox Model; Nested Cohort Methods; Analysis of Correlated Survival
Data; Parametric Survival Methods and Accelerated
Failure time Models (40 Credits).
STE 703:
Clinical Epidemiology.
Contents:
Clinical Epidemiology; Diagnostic and Screening Tests; Randomized Controlled
Trials;
Non-randomized Studies; Therapeutic Safety (40 Credits).
STE 704:
Epidemiology.
Contents:
Dynamics of Disease Transmission; Measurement of Occurrence of Disease;
Randomized Trials; Cohort Studies; Case-control and Cross-sectional Studies; Risk
Assessment; Application of Epidemiology to Evaluation and Policy (40 Credits).
STE 705:
Design of Clinical Trials.
Contents:
Research Designs; Randomization; Case-Control Studies; Cohort Studies;
Diagnostic Tests; Methods of Regression (40 Credits).
STE 706:
Categorical Data Analysis.
Contents:
Logistic Regression Analysis with Multiple Predictors; Testing for Significant
covariates; Graphical and other methods for Model assessment; Interpretation of all
coefficients in model; Conditional Logistic Regression; Multivariate Logistic
Regression; Generalised Estimating Equations (40 Credits).
STE 707:
Biostatistics.
Contents:
Estimation; Hypothesis testing; Inference; Non-Parametric statistics; Analysis of
Variance and Covariance; Robustness; Discrimination and Classification; Principle
Component Analysis (40 Credits).
Now your are serious!
The department provides
students with strong
analytic and quantitative
skills needed to conduct
professional level public
health research, disease
surveillance, program
evaluation, and public
health practice.
MASTERS COURSES
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