Module Descriptions

Updated 14th August 2017

The prerequisites for the modules below are all "pass" prerequisites.

 

DSA1101 Introduction to Data Science

Modular Credits: 4 

Workload: 3-1-1-2-3

Prerequisites: H2 pass in Mathematics or equivalent.

This module is only offered to DSA students. Preclusion: nil  Cross-listing: nil 

This module is designed to provide a basic introduction to data science along with real examplesand case studies from both academic and industrial sources, in areas as diverse as finance, biological sciences, physics and pharmacy.

 

DSA4211 High-Dimensional Statistical Analysis

Modular Credits: 4 

Workload: 3-1-0-3-3

Prerequisites: H2 pass in Mathematics or equivalent.

This module is only offered to DSA students. Preclusion: nil  Cross-listing: nil 

Dimensionality is an issue that can arise in many scientific fields such as medicine, genetics, business and finance, among others. The statistical properties of estimation and inference procedures must be carefully established when the number of variables is much larger than the number of observations. This module will discuss several statistical methodologies useful for exploring voluminous data. They include principal component analysis, clustering and classification, tree-structured analysis, neural network, hidden Markov models, sliced inverse regression, multiple testing, sure independent screening (SIS) and penalized estimation for variable selection. Real data will be used for illustration of these methods. Some fundamental theory for high-dimensional learning will be covered.

 

DSA4212 Optimisation for Large-Scale Data-Driven Inference

Modular Credits: 4 

Workload: 3-1-0-3-3

Prerequisites: MA1101R and MA1104 or MA2311 and ST2132

Preclusion: nil  Cross-listing: nil 

Computational optimisation is ubiquitous in statistical learning and machine learning. This module covers several current and advanced topics in optimisation, with an emphasis on efficient algorithms for solving large-scale data-driven inference problems. Topics include first and second order methods, stochastic gradient type approaches and duality principles. Many relevant examples in statistical learning and machine learning will be covered in detail.

 

GEM2900 Understanding Uncertainty & Stats Thinking

Modular Credits: 4 

Workload: 4-0-0-3-3 

Prerequisites: nil 

Preclusion: Not for Statistics

Major students 

Cross-listing: nil 

This module, using a minimum of mathematical or statistical prerequisites, aims to help the student make rational decisions in an uncertain world. Uncertainty, variability and incomplete information are inherent; to a greater or lesser extend, in all disciplines. One approach to dealing with this is through statistical and probabilistic ideas about information. The student will, throughout the module, gain an understanding of the strengths and weaknesses of such a data based approach and learn how and when such an approach is appropriate. The student will also learn practical skills in interpreting statistical information and gain the ability to critically evaluate statistically based arguments. 

 

GEM2901 Reporting Statistics in the Media 

Modular Credits: 4 

Workload: 4-0-0-3-3 

Prerequisites: NIL 

Preclusion: NIL 

Cross-listing: NIL 

Statistical thinking will one day be as necessary for efficient citizenship as the ability to read and write' (H.G. Wells). In the Information Age every educated person is surrounded by statistical information of all kinds. This information comes frequently through the media from governmental, scientific and commercial worlds. This module, using a minimum of mathematical or statistical prerequisites, aims to make the student statistically literate in reading and understanding such information. The course will be based on real world case studies of issues of current importance and relevance. The students' objectives in this course are as follows: (1) Students will learn to read, critically analyze, write about and present reports about all types of quantitative information. (2) Students will learn the strengths and weaknesses of using quantitative information in different circumstances. (3) Students will study a number of case studies of current interest. They will be able to compare and contrast the statistical treatments from different sources. 

 

ST1131 Introduction to Statistics 

Modular Credits: 4

Workload: 3-1-0-3-3

Pre-requisite: GCE 'AO' level or H1 Pass in Mathematics or its equivalent, or H1 Pass in Statistics together with a pass in O-Level Additional Mathematics, or MA1301 or MA1301FC or MA1301X Preclusions: ST1131A, ST1232, ST2334, CE2407, CN3421, EC2231, EC2303, PR2103, DSC2008. Engineering students except ISE students

This module introduces students to the basic concepts and the methods of statistics. A computer package is used to enhance learning and to enable students to analyse real life data. Topics include descriptive statistics, basic concepts of probability, sampling distribution, statistical estimation, hypothesis testing, linear regression. This module is targeted at students interested in Statistics who are able to meet the prerequisite. It is also an essential module for students in the following programmes: Industrial and Systems Engineering (FoE); E-Commerce (SoC); Project & Facilities Management and Real Estate (SDE).

 

ST1131A Introduction to Statistics 

Modular Credits: 4

Workload: 3-1-0-3-3

Pre-requisite: FBA students 

Preclusions: ST1131, ST1232, ST2334, CE2407, CN3421, EC2231, EC2303, PR2103, DSC2008

This module introduces students to the basic concepts and the methods of statistics. A computer package is used to enhance the effect of learning and to enable students to analyse complicated data. Topics include descriptive statistics, basic concepts of probability, sampling distribution, statistical estimation, hypothesis testing, linear regression. This module is essential to students from School of Business.

 

ST1232 Statistics for Life Sciences 

Modular Credits: 4

Workload: 3-1-0-0-6

Prerequisite: GCE 'AO' level or H1 Pass in Mathematics or its equivalent, or H1 Pass in Statistics together with a pass in O-Level Additional Mathematics 

Preclusion: ST1131, ST1131A, ST2334, CE2407, CN3421, EC2231, EC2303, PR2103, DSC2008

This module introduces life science students to the basic principles and methods of biostatistics, and their applications and interpretation. A computer package is used to enhance learning and to enable students to analyze real life data sets. Topics include probability, probability distributions, sampling distributions, statistical inference for one and two sample problems, nonparametric tests, categorical data analysis, correlation and regression analysis, multi-sample inference. This module is essential to students of the Life Sciences.

 

ST2131 Probability 

Modular Credits: 4

Workload: 3-1-0-3-3

Pre-requisite: MA1102 or MA1102R or MA1312 or MA1507 or MA1505 or MA1505C or MA1521 

Preclusions: MA2216, ST2334, CE2407

Cross Listing: MA2216

The objective of this module is to give an elementary introduction to probability theory for science (including computing science, social sciences and management sciences) and engineering students with knowledge of elementary calculus. It will cover not only the mathematics of probability theory but will work through many diversified examples to illustrate the wide scope of applicability of probability. Topics covered are: counting methods, sample space and events, exioms of probability, conditional probability, independence, random variables, discrete and continuous distributions, joint and marginal distributions, conditional distribution, independence of random variables, expectation, conditional expectation, moment generating function, central limit theorem, the weak law of large numbers. This module is targeted at students who are interested in Statistics and are able to meet the prerequisite. It is an essential module for Industrial and Systems Engineering students.

 

ST2132 Mathematical Statistics 

Modular Credits: 4

Workload: 3-1-0-3-3

Pre-requisite: ST2131 or ST2334 or MA2216

Preclusions: Nil

This module introduces students to the theoretical underpinnings of statistical methodology and concentrates on inferential procedures within the framework of parametric models. Topics include: random sample and statistics, method of moments, maximum likelihood estimate, Fisher information, sufficiency and completeness, consistency and unbiasedness, sampling distributions, x2-, t- and F- distributions, confidence intervals, exact and asymptotic pivotal method, concepts of hypothesis testing, likelihood ratio test,, Neyman-Pearson lemma. This module is targeted at students who are interested in Statistic and are able to meet the prerequisite. 

 

ST2137 Computer Aided Data Analysis (Elective, 4MC)

Modular Credits: 4

Workload: 3-1-0-3-3

Pre-requisite: ST1131 or ST1131A or ST1232 or ST2131 or ST2334 or MA2216

This module introduces students to the statistical computer packages, with main focus on SAS, Splus and SPSS, that provide the computational tools for performing statistical data analysis using the methodology covered in the prerequisite modules. Topics include data access, transformations, estimation, testing hypotheses, ANOVA, performing resampling methods and simulations. It also equips students with basic computational techniques for maximum likelihood estimation. This module is targeted at students who are interested in Statistics and are able to meet the prerequisite.

 

ST2334 Probability and Statistics 

Modular Credits: 4

Workload: 3-1-0-3-3

Pre-requisite: MA1102R or MA1505 or MA1521 or MA1312 or MA1507

Preclusion(s): ST1131, ST1131A, ST1232, ST2131, MA2216, CE2407, EC2231, PR2103, EC2303, DSC2008. ME students taking or having taken ME4273. All ISE students

Cross-listing(s): Nil 

Basic concepts of probability, conditional probability, independence, random variables, joint and marginal distributions, mean and variance, some common probability distributions, sampling distributions, estimation and hypothesis testing based on a normal population. This module is targeted at students who are interested in Statistics and are able to meet the prerequisites. Preclude ME students taking or have taken ME4273. 

 

ST2335 Statistical Methods 

Modular Credits: 4

Workload: 3-1-0-3-3

Pre-requisite: ST1131 or ST2334

Preclusions: ST3131

Descriptive statistics, conditional expectation, correlation coefficient, bivariate normal distribution, simple linear regression, analysis of variance, nonparametric methods. This module is targeted at students who are interested in Statistics and are able to meet the pre-requisites.

 

ST2288 Basic Undergraduate Research in Statistics and Applied Probability I

Modular Credits: 4

Workload: 3-1-0-3-3

Pre-requisite: ST1131 or ST1232 and Departmental Approval

Preclusions: NIL

For details, please refer to the “Undergraduate Research Opportunities Programme in Science” (UROPS).

 

ST2289 Basic Undergraduate Research in Statistics and Applied Probability II

Modular Credits: 4

Workload: 3-1-0-3-3

Pre-requisite: ST2288 and Departmental Approval

Preclusions: NIL Cross-listing(s): Nil 

For details, please refer to the “Undergraduate Research Opportunities Programme in Science” (UROPS).

 

ST3131 Regression Analysis 

Modular Credits: 4

Workload: 3-1-0-3-3

Pre-requisite: ST2131 or ST2334 or MA2216

Preclusions: ST2335, EC3303

This module focuses on data analysis using multiple regression models. Topics include simple linear regression, multiple regression, model building and regression diagnostics. One and two factor analysis of variance, analysis of covariance, linear model as special case of generalized linear model. This module is targeted at students who are interested in Statistics and are able to meet the pre-requisites.

 

ST3232 Design and Analysis of Experiments 

Modular Credits: 4

Workload: 3-1-0-3-3

Pre-requisite: ST2132 or ST2334

Preclusions: Nil

This module covers common designs of experiments and their analysis. Topics include basic experimental designs, analysis of one-way and two way layout data, multiple comparisons, factorial designs, 2k-factorial designs, blocking and confounding, fractional factorial design and nested designs. This module is targeted at students who are interested in Statistics and are able to meet the pre-requisites.

 

ST3233 Applied Times Series Analysis 

Modular Credits: 4

Workload: 3-1-0-3-3

Pre-requisites: ST2132 or ST2334

Preclusions: Nil

This module introduces the modelling and analysis of time series data. A computer package will be used to analyse real data sets. Topics include stationary time series, ARIMA models, estimation and forecasting with ARIMA models This module is targeted at students who are interested in Statistics and are able to meet the pre-requisites.

 

ST3234 Actuarial Statistics 

Modular Credits: 4

Workload: 3-1-0-3-3

Pre-requisites: ST2131 or ST2334 or MA2216

Preclusions: Nil


This module focuses on life contingencies and theory of risk. Topics include survival models and life tables, life annuities, assurances and premiums, reserves, joint life and last survivor statuses, multiple decrement tables, expenses, individual and collective risk theory. This module is targeted at students who are interested in Statistics and are able to meet the pre-requisites.

 

ST3235 Statistical Quality Control

Modular Credits: 4

Workload: 3-1-0-3-3

Pre-requisite: ST2131 or ST2334 or MA2216

Preclusions: All ISE students

This module focuses on the use of modern statistical methods for quality control and improvement. The objective is to give students a sound understanding of the principles and the basis for applying them in a variety of situations. Topics include: properties, designs and application of control charts, Shewhart charts, straight moving average chart, cumulative sum chart, exponentially weighted moving average chart, basic concepts of acceptance sampling, single, multiple and sequential sampling by attributes, variable sampling. This module is targeted at students who are interested in Statistics and are able to meet the prerequisite.

 

ST3236 Stochastic Processes 1 

Modular Credits: 4

Workload: 3-1-0-3-3

Pre-requisites: (MA1101 or MA1101R or MA1311 or MA1508)and (ST2131 or MA2216)

Preclusion: MA3238. All ISE students Cross-listing: MA3238

This module introduces the concept of modelling dependence and focuses on discrete-time Markov chains. Topics include discrete-time Markov chains, examples of discrete-time Markov chains, classification of states, irreducibility, periodicity, first passage times, recurrence and transience, convergence theorems and stationary distributions. This module is targeted at students who are interested in Statistics and are able to meet the pre-requisites.

 

ST3239 Survey Methodology

Modular Credits: 4

Workload: 3-1-0-3-3

Pre-requisite: ST2131 or ST2334 or MA2216

Preclusion: Nil

This module gives an introduction to the design of sample surveys and estimation procedures, with emphasis on practical applications in survey sampling. Topics include planning of surveys, questionnaire construction, methods of data collection, fieldwork procedures, sources of errors, basic ideas of sampling, simple random sampling, stratified, systematic, replicated, cluster and quota sampling, sample size determination and cost. This module is targeted at students who are interested in Statistics and are able to meet the pre-requisites.

 

ST3240 Multivariate Statistical Analysis

Modular Credits: 4

Workload: 3-1-0-3-3

Pre-requisite: ST3131 

Preclusion: Nil

This module focuses on the classical theory and methods of multivariate statistical analysis. Topics include distribution theory: multivariate normal distribution, Hotelling's T2 and Wishart distributions, inference on the mean and covariance, principal components and canonical correlation, factor analysis, discrimination and classification. This module is targeted at students who are interested in Statistics and are able to meet the pre-requisites.

 

ST3241 Categorical Data Analysis I 

Modular Credits: 4

Workload: 3-1-0-3-3

Pre-requisite: ST3131

Preclusion: Nil

This module introduces methods for analysing response data that are categorical, rather than continuous. Topics include: categorical response data and contingency tables, loglinear and logit models, Poisson regression, framework of generalised linear models, model diagnostics, ordinal data. This module is targeted at students who are interested in Statistics and are able to meet the prerequisite. 

 

ST3242 Introduction to Survival Analysis

Modular Credits: 4

Workload: 3-1-0-3-3

Pre-requisite: ST2132

Preclusion: Nil

This module focuses on the analysis of survival data or “failure times”, which measure the length of time until the occurrence of an event, with the objective of modeling the underlying distribution of the failure time variable and to assess the dependence of the failure time variable on the independent variables. Topics include: examples of survival data, concepts and techniques used in the analysis of time to event data, including censoring, hazard rates, estimation of survival curves, parametric and nonparametric models, regression techniques, regression diagnostics. This module is targeted at students who are interested in Statistics and are able to meet the prerequisite.

 

ST3243 Statistical Method in Epidemiology

Modular Credits: 4

Workload: 3-1-0-3-3

Pre-requisites: (ST2131 or MA2216) and (ST2132)

Preclusion: Nil

This course will provide an introduction to the key concepts and principles of epidemiology. It emphasizes a quantitative approach to clinical and public health problems through the statistical analysis of epidemiologic data. The students will be equipped with the skills needed to understand critically the epidemiologic literature. Principles and methods are illustrated with examples. Topics include incidence prevalence and risk, mortality and morbidity rates, types of study designs: prospective, retrospective and cross-sectional study, association and causation, confounding and standardization, precision and validity of epidemiologic studies, matching, screening, contingency tables, stratified analysis, logistic regression. This module is targeted at students who are interested in Statistics and are able to meet the pre-requisites.

 

ST3244 Demographic Methods 

Modular Credits: 4

Workload: 3-1-0-3-3

Pre-requisite: ST1131

Preclusion: Nil

This course will provide an introduction to the fundamental principles and methods of demography. The role of demographic data in describing the health status of a population, spotting trend and making projection will be highlighted. Topics include sources and interpretation of demographic data, rates, proportions and ratios, standardization, complete and abridged life tables, estimation and projection of fertility, mortality and migration, Interrelations among demographic variables, population dynamics, demographic models. This module is targeted at students who are interested in Statistics and are able to meet the pre-requisites.

 

ST3245 Statistics in Molecular Biology

Modular Credits: 4

Workload: 3-1-0-3-3

Pre-requisite: ST2131 or ST2334

Preclusion: Nil


The module focuses on how statistics has been used successfully in solving important problems in molecular biology. Major topics covered are: Genetics, basic molecular biology, discrete probability, stochastic processes, design of experiments, parameter estimation, the bootstrap, testing hypotheses, Markov Chain Monte Carlo. This module is targeted at students who are intersted in Statistics and are able to meet the pre-requisites.

 

ST3246 Statistical Modelling for Actuarial Science

Modular Credits: 4

Workload: 3-1-0-3-3

Pre-requisite: ST2132

The main objective of this module is to teach students how to apply statistical methods to construct actuarial loss model in insurance fields. Model-based approach is used to introduce those major topics in the module, such as loss distributions, frequency distributions, aggregate loss model, and credibility. Statistical methods and approaches, such as point and interval estimations, test of hypotheses, goodness of fit, maximum likelihood functions, Bayesian estimation, etc. are also discussed in details. 

 

ST3247 Simulation 

Modular Credits: 4

Workload: 3-1-0-3-3 

Pre-requisite: ST2131 or ST2334 or MA2216 and CS1010 or CS1010E or CS1010S or CS1010FC or IT1006

Preclusion: Nil


The advent of fast and inexpensive computational power has facilitated the description of real phenomenon using realistic stochastic models which can be analysed using simulation studies. This module teaches students how to analyse a model by use of a simulation study and the topics include: pseudorandom number generation, generating discrete and continuous random variables, simulating discrete events, statistical analysis of simulated data, variance reduction, Markov Chain Monte Carlo methods. It also covers topics in stochastic optimization such as simulated annealing. This module is targeted at students who are interested in Statistics and are able to meet the prerequisites.

 

ST3288 Advanced UROPS in Statistics and Applied Probability I 

Modular Credits: 4

Workload: 3-1-0-3-3

Pre-requisites: Nil


For details, please refer to the “Undergraduate Research Opportunities Programme in Science” (UROPS).

 

ST3289 Advanced UROPS in Statistics and Applied Probability II 

Modular Credits: 4

Workload: Nil

Pre-requisites: Nil

For details, please refer to the “Undergraduate Research Opportunities Programme in Science” (UROPS).

 

ST4199 Honours Project in Statistics

Modular Credits: 12

Workload: 0-2-0-0-13

Pre-requisite: At least one major at B.Sc./B.Appl.Sc. level; and minimum overall CAP of 3.20 on completion of 100 MCs or more

Pre-requisites: Nil 


The objectives of the course are to develop the basic skills for independent scientific research, and to promote an appreciation of problem solving strategies in science. On completion of the course, students will be able to demostrate an appreciation of the current state of knowledge in a particular field of research, to master the basic techniques required for the study of a research question, and to communicate scientific information clearly and concisely in written and spoken English.

 

ST4231 Computer Intensive Statistical Methods 

Modular Credits: 4

Workload: 3-1-0-3-3

Pre-requisite: ST2132

Preclusion: Nil


The availability of high-speed computation has led to the development of “modern” statistical methods which are implemented in the form of well-understood computer algorithms. This module introduces students to several computer intensive statistical methods and the topics include: empirical distribution and plug-in principle, general algorithm of bootstrap method, bootstrap estimates of standard deviation and bias, jack-knife method, bootstrap confidence intervals, the empirical likelihood for the mean and parameters defined by simple estimating function, Wilks theorem, and EL confidence intervals, missing data, EM algorithm, Markov Chain Monte Carlo methods. This module is targeted at students who are interested in Statistics and are able to meet the prerequisite. 

 

ST4232 Nonparametric Statistics 

Modular Credits: 4

Workload: 3-1-0-3-3

Pre-requisite: ST2132

Preclusion: Nil

This module focuses on the theory and methods of making statistical inference based on nonparametric techniques. Students will see the analyses of real data from various areas of applications. Topics include properties of order statistics, statistics based on ranks, distribution-free statistics, inference concerning location and scale parameters for one and two samples, Hajek's projection. This module is targeted at students who are interested in Statistics and are able to meet the pre-requisites.

 

ST4233 Linear Models 

Modular Credits: 4

Workload: 3-1-0-3-3

Pre-requisite: ST3131

Preclusion: Nil

Linear statistical models are used to study the way a response variable depends on an unknown, linear combination of explanatory and/or classification variables. This module focuses on the theory of linear models and the topics include linear regression model, general linear model, prediction problems, sensitivity analysis, analysis of incomplete data, robust regression, multiple comparisons, introduction to generalised linear models. This module is targeted at students who are interested in Statistics and are able to meet the prerequisite. 

 

ST4234 Bayesian Statistics

Modular Credits: 4

Workload: 3-1-0-3-3

Pre-requisite: ST2132

Preclusion: Nil

Bayesian principles: Bayes' theorem, estimation, hypothesis testing, prior distributions, likelihood, predictive distributions. Bayesian computation: numerical approximation, posterior simulation and integration, Markov chain simulation, models and applications: hierarchical linear models, generalized linear models, multivariate models, mixture models, models for missing data, case studies. This module is targeted at students who are interested in Statistics and are able to meet the pre-requisites.

 

ST4237 Probability Theory I

Modular Credits: 4

Workload: 3-1-0-3-3

Pre-requisites: (MA2216 or ST2131)

Preclusion: Nil

Probability space, weak and strong laws of large numbers, convergence of random series, zero-one laws, weak convergence of probability measures, characteristic function, central limit theorem. This module is targeted at students who are interested in Statistics and are able to meet the pre-requisites.

 

ST4238 Stochastic Processes II

Modular Credits: 4

Workload: 3-1-0-3-3

Pre-requisites: MA3238 or ST3236

Preclusions: MA4251 

Cross-listing: MA4251

This module builds on ST3236 and introduces an array of stochastic models with biomedical and other real world applications. Topics include Poisson process, compound Poisson process, marked Poisson process, point process, epidemic models, continuous time Markov chain, birth and death processes, martingale. This module is targeted at students who are interested in Statistics and are able to meet the pre-requisites.

 

ST4240 Data Mining

Modular Credits: 4

Workload: 3-1-0-3-3

Pre-requisite: ST3131

Preclusion: Nil

The module covers statistical techniques and tools such as kernel methods for estimating the density and regression functions, machine learning, hidden Markov Chain, EM algorithm, classification, cluster analysis and support vector machines for analyzing large data sets and for searching for unexpected relationships in the data. It also covers model selection for searching through a large collection of potential local models that describe some aspect of the data in an easily understandable way. This module is targeted at students who are interested in Statistics and are able to meet the pre-requisites.

 

ST4241 Design and Analysis of Clinical Trials 

Modular Credits: 4

Workload: 3-1-0-3-3

Pre-requisite: ST2132 or ST3242

Preclusion: Nil

This course will provide an introduction to the design and analysis of clinical trials. Emphasis is on the statistical aspects. Topics include introduction to clinical trials, phases of clinical trials, objectives and endpoints, the study cohort, controls, randomization and blinding, sample size determination, treatment allocation, monitoring trial progress: compliance, dropouts and interim analyses, monitoring for evidence of adverse or beneficial treatment effects, ethical issues, quality of life assessment, data analysis involving multiple treatment groups and endpoints, stratification and subgroup analysis, intent to treat analysis, analysis of compliance data, surrogate endpoints, multi-centre trials and good practice versus misconduct. This module is targeted at students who are interested in Statistics and are able to meet the pre-requisites.

 

ST4242 Analysis of Longitudinal Data

Modular Credits: 4

Workload: 3-1-0-3-3

Pre-requisite: ST3131

Preclusion: Nil

This course covers modern methods for the analysis of repeated measures, clustered data, correlated outcomes and longitudinal data, with a strong emphasis on applications in the biological and health sciences. Both continuous and discrete response variables will be considered. The use of generalized estimating equations (GEE) will be emphasized. Topics include introduction to longitudinal studies, exploring longitudinal data, analysis of variance for repeated measures, general linear models for longitudinal data, growth curves, models for covariance structure, estimation of individual trajectories, generalized linear models for longitudinal discrete data, marginal models, generalized estimating equations, random effects models and transition models. This module is targeted at students who are interested in Statistics and are able to meet the pre-requisites.

 

ST4243 Statistical Methods for DNA Microarray Analysis 

Modular Credits: 4

Workload: 3-1-0-3-3

Pre-requisites LSM1102 and ST3240

Preclusion: Nil

This is a level 4000 advance course on the statistical design and analysis of genetic experiments with concentration in DNA microarray experiments. The course covers a variety of statistical methods including basic array designs, statistical models and hypothesis testing, cluster analysis and other multivariate analysis methods that play a role in the analysis of DNA microarray experiments. The students will be required to have the knowledge of statistics and of statistical genetics that is provided by the Pre-requisites or equivalent. The students will have access to real data from microarray experiments and will practice with specialized software. Since this is a new expanding area and the experiments are constantly evolving, emphasis will be placed on gaining the basic knowledge and software expertise for designing new experiments and analyzing the results. The students will gain the knowledge and the practice to be able to analyze data from genetic experiments involving DNA microarrays and similar experiments. Topics include introduction to experimental genetics and DNA microarray techniques, basic design of experiments for microarrays, statistical models, modeling and testing for gene upregulation, principal components analysis and cluster analysis and gene clustering. This module is targeted at students who are interested in Statistics and are able to meet the pre-requisites.

 

ST4245 Statistical Methods for Finance

Modular Credits: 4

Workload: 3-1-0-3-3

Pre-requisites: ST3131 

Preclusion: Nil

The module aims to equip students with a repertoire of statistical analysis and modelling methods that are commonly used in the finance industry. Major topics include statistical properties of returns, regression analysis with applications to single and multi-factor pricing models, multivariate analysis with applications in Markowitz's portfolio management, modelling and estimation of volatilities, calculation of value-at-risk, nonparametric methods with applications to option pricing and interest rate markets. Students are assumed to have had no background in finance or economics and will be acquainted with the foundations of finance such as portfolio optimization and the Capital Asset Pricing Model. This module is targeted at students who are interested in Statistics and are able to meet the pre-requisite.

 

ST4261 Special Topics

Modular Credits: 4

Workload: 3-1-0-3-3

Pre-requisites: Departmental approval 

Preclusion: Nil

This module consists of selected topics, which may vary from year to year depending on the interests and availability of staff. 

 

ST4261 Special Topics II

Modular Credits: 4

Workload: 3-1-0-3-3

Pre-requisites: Departmental approval 

Preclusion: Nil

This module consists of selected topics, which may vary from year to year depending on the interests and availability of staff.