Level III Course Units

Course No.Course ModuleCreditsTypeCombination 
Sem ISTST+CS
ST3003Marketing Research230Loo
ST3007Operational Research345Loo
ST3051Statistical Inference I345Lxx
ST3071Linear Models345Lx
ST3072Applied Regression Analysis345Lxx
ST3073Surveys and Sampling345Lx
ST3074Time Series Analysis230Lx
ST3075Design of Experiments230Lx
ST3076Reliability Data Analysis345Lx
CS3001Visual Programming 330L,30Px
CS3003Computer Graphics and Image Procesing330L,30Px
CS3005Neural Computing330L,30Px
CS3007Multimedia Technology330L,30Px
CS3008Introduction to Data Structures and Algorithems330L,30Pox
Sem II
ST3053Literature Review in ST130Px
ST3059Literature review in ST + CS130Px
ST3067Quality Control 230Lo
ST3070Special Topics230Lo
ST3077Medical Statistics345Lo
ST3078Case Studies / Assignments in ST130Px
ST3079Case Studies / Assignments in ST +CS130Px
ST3080Group project in ST130Px
ST3081Group project in ST+CS130Px
PM3001Real Analysis345Lo
PM3053Complex Analysis460Lo
IT3001Management Information systems330L,30Po
IT3002Database System330L,30Poo
EC4004Industrial Training390Poo

Note: Students may take a maximum of 33 credites. Abbreviations : x – core courses, o – electives, L – lectures, P – practicals, C – credits

Level IV Course Units

Course No.Course ModuleCreditsTypeCombination 
Sem IStSt+CS
ST4002Generalized Linear Models230Lxo
ST4011Econometrics230Lo
ST4016Categorical Data Analysis 345Lx
ST4030Multivariate Data Analysis345Lxx
ST4031Stochastic Processes and Applications345Lxx
ST4040Individual Project in ST+CS6180Px
ST4050Individual Project in ST6180Px
CS4005Intelligent Systems345Lo
CS4006Advanced Database Systems345L,30Po
CS4011Natural Language Processing330L,30Po
Sem II
ST 4001Statistical Inference – ll230Lxx
ST4012Special topics for ST230Lo
ST4013Special topics for ST + CS230Lo
ST4015Decision Theory230Lx
ST4032Case studies in ST 130Px
ST4033Case studies in ST+CS130Px
ST4034Computational Statistics 345Loo
PM4004Real Analysis 460Lo
CS4003Logic Programming and Prolog 330L,30Poo
CS4008Advanced Computer Graphics and Vision 330L,30Po
CS4017Wireless Ad-Hoc and Sensor Network 330L,30Po
CS4018Evolutionary Computing 345Lo
CS4019Computational Patern Recognition330L,30Pox
CS4020Advanced Concepts in Software Design & Development330L,30Poo

Note: Abbreviations : x – core courses, o – electives, L – lectures, P – practicals, C – credits

ST 3003 : Marketing Research (30L, 2C)

Syllabus:
Introduction, The Marketing Research Process, Defining the problem with exploratory research, Survey research: Methods of communication with respondents, Test marketing, Measurements and Attitude scaling, Questionnaire design, Sampling procedures, Data analysis, report writing and presentation: Stochastic models of brand choice, Applications of General Linear Models in marketing, Conjoint analysis, Correspondence analysis, Advertising media models, Marketing response models.
Evaluation Criteria:
End-of-semester examination and assignments
Suggested Readings:
Marketing Research (D.R. Lehmann, S. Gupta, J.H. Steckel), Marketing Research, (Melvin Crask, Richard J. Fox, Roy Stout), Marketing Research (David A. Aakar)


ST 3007 – Operational Research (45L, 3C)

Dependencies:
AM 2003, AM 2004
Syllabus:
Integer programming and solution techniques, Zero-one programming and solution techniques, Transportation models, Assignment models and their solution techniques. Project planning and evaluation techniques, Deterministic inventory models with shortages and without shortages, Queuing models, Different queuing systems and disciplines.
Evaluation Criteria:
End-of-semester examination and Assignments
Suggested Readings:
Operational Research an Introduction (Hamdy A. Taha), Operational research (Harvey M. Wagner)


ST 3051 – Statistical Inference I (45L, 3C)

Dependencies:
AM 1001
Syllabus:
Characteristic function, Sampling from Normal population, sampling distributions of sample mean and sample variance (S2), independence of sample mean and S2, Estimation Criterion: Mean-squared error, Unbiasedness, Consistency, Sufficiency, Completeness, Efficiency, Factorization criterion. Variance Reduction: Cramer Rao Lower Bound, Rao-Blackwell Theorem, Lehmann- Scheffe’ Theorem. Methods of Estimation: Method of moments, Maximum Likelihood and Its Properties, Least Squares. Interval Estimation: Pivotal Method, General Method.
Evaluation Criteria:
Examinations and assignments
Suggested Readings:
Introduction to Mathematical Statistics (Hogg and Craig), Statistical Theory (Lindgren), Statistical Inference (Casella and Berger)


ST 3071 – Linear Models (45L, 3C)

Syllabus:
Elementary linear and matrix algebra; Generalized and conditional inverses; Solutions of linear equations, idempotent matrices, trace of matrices; Derivatives of quadratic forms, expectation of random matrices; Multivariate normal distribution and its properties, distribution of quadratic forms; General linear model; optimal estimation and hypothesis testing, applications to regression model; Continued application of optimal inference; Design models, estimability, solving normal equations; Components of variance models and mixed models.
Evaluation Criteria:
End-of-semester examination and Assignments
Suggested Readings:
Theory and applications of the linear model (Graybill, F. A.), Matrices with applications in Statistics(Graybill, F. A.).


ST 3072 – Applied Regression Analysis (45L, 3C)

Dependencies:
ST1004, ST2004
Syllabus:
Introduction to regression, Correlation, Uses of regression, Simple linear regression model, Parameter estimation, inferences about the model, model diagnostics and prediction, Multiple regressions, Qualitative variables as predictors, Model building, Comparison of Regressions using dummy variables, Analysis of collinear data, Transformation of Variables, Polynomial Regression
Evaluation Criteria:
End-of-semester examination and Assignments
Suggested Readings:
Applied Regression Analysis (Draper & Smith), Applied Linear Regression (Sanford Weisberg), Introduction to Linear Regression Analysis (Montgomery, D.C. & Peck, E.A), Applied Regression Analysis (Draper, N.R. & Smith H.)


ST 3073 – Surveys and Sampling (45L, 3C)

Syllabus:
Fundamentals of probability sampling and estimation, Simple Random Sampling: Theory involved in estimation procedures, Estimating population mean, variance, total & proportion, Estimating a ratio & its variance, Estimation using Ratio and Regression methods and their properties, Sample size determination. Stratified Random Sampling: Proportional and optimal cost allocations to strata, Estimating population mean, variance, total & proportion, Overview of advanced topics in stratified random sampling, Estimating a ratio & its variance, Regression estimators, Sample size determination. Post-stratification, Quota sampling, Cluster Sampling: Overview of cluster sampling, clustering with equal and unequal probabilities, Sample size determination, Design effect and intra-cluster correlation. Multi-stage sampling: Complex surveys and related problems, Sources of errors in surveys.
Evaluation Criteria:
End-of-semester examination and Assignments
Suggested Readings:
Elementary Sampling Theory (Vic Barnet), Survey Sampling (Leslie Kish), Sampling Techniques (William G. Cochran)



ST 3074 – Time Series Analysis (30L, 2C)

Syllabus:
Introduction: Definition, Types of time series, Components of time series, Time plot, Time series decomposition, Transformation, Differencing, Autocorrelation, Stationarity: Stationary & non-stationary time series, Tests for stationary, Modelling time series: Time series models, Model identification, Parameter estimation, Diagnostic checks, Forecasting, Spectral analysis
Evaluation Criteria:
End-of-semester examination and Assignments
Suggested Readings:
Forecasting Methods and Applications (Makridakis, S. Weelwright, S. C. and Hyndman, R. J.), The analysis of Time Series: An Introduction (Chatfield, C), Forecasting and Control (Box, G. E. P., Jenkins, G. M. and Reinsell)


ST 3075 – Design of Experiments (30L, 2C)

Syllabus:
Principles of planning and designing comparative experiments; Review of ANOVA and related topics; Basic designs: completely randomized design (C.R.D), randomized complete block design (R. C. B. D), Latin squares/multiple Latin squares, treatment contrasts and mean comparisons; Factorial experiments (2k and others); confounding and partial confounding in 2k experiments; split-plot designs; analysis of covariance
Evaluation Criteria:
End-of-semester examination and Assignments
Suggested Readings:
Design and analysis of experiments (Montgomery, D. C.), Statistics for experiments: An introduction to design, data analysis and model building (Box, Hunter, and Hunter), The design of experiments (Mead, R).


ST 3076 – Reliability Data Analysis (45L, 3C)

Syllabus:
Reliability concepts and Reliability data, Models, censoring and likelihood for failure time data, Non-parametric estimation, Location-Scale based parametric distributions, Probability plotting, Parametric likelihood fitting concepts, Maximum likelihood estimates for the exponential mean based on the density approximation. Failure time regression analysis; failure time regression models, accelerated failure time models, regression diagnostics, proportional hazards model, weibull proportional hazards model. Accelerated test models: Different types of acceleration, accelerated life tests, methods of acceleration, acceleration models. Planning accelerated life tests: planning information, evaluation of test plans, planning single variable ALT experiments.
Evaluation Criteria:
End-of-semester examination and Assignments
Suggested Readings:
Statistical methods for reliability data (Meeker, M.Q. and Escobar, L.A.), Reliability Modeling: A Statistical approach (Wolstenholme L.C.)


CS 3001 – Visual Programming Technologies (30L,30P, 3C)

Conduct by University of Colombo School of Computing


CS 3003 – Computer Graphics and Image Procesing (30L,30P, 3C)

Conduct by University of Colombo School of Computing


CS 3005 – Neural Computing (30L,30P, 3C)

Conduct by University of Colombo School of Computing


CS 3007 – Multimedia Technology (30L,30P, 3C)

Conduct by University of Colombo School of Computing


CS 3008 – Introduction to Data Structures and Algorithems (30L,30P, 3C)

Conduct by University of Colombo School of Computing


ST 3053/ ST 3059 – Literature Review in ST / ST+CS (30P, 1C)

Syllabus:
Read and discuss text/papers for a general sense of what research is/are about, how one thinks when doing research, and what the major research activities are, Identify research articles from different areas of statistics/computer science, involving different methodologies of research, and abstract them, Select an area related to statistics, which is of particular interest to you. Write a professional quality literature review for a problem of your choice.
Evaluation Criteria:
Examinations


ST 3067 – Quality Control (30L, 2C)

Dependencies:
ST1006
Syllabus:
Sampling plans of attribute type; OC curve scheme, Dodge and Romig approach, Decision theory approach, Double sampling plans, Sampling inspection by variables, Sequential sampling plans, Control charts; Variable type control charts, Attribute type control charts.
Evaluation Criteria:
Examinations and assignments
Suggested Readings:
Statistical Quality Control (Mahajan, M), Statistical Quality Control, (Gupta, R.C.), Quality Control and Industrial Statistics (Duncan, A.J.)


ST 3070 – Special Topics (30L, 2C)

Syllabus:
Selected topics depending on the availability of teaching staff.
Evaluation Criteria: Examinations and assignments


ST 3077 – Medical Statistics (45L, 3C)

Syllabus:
Introduction, Epidemiology: Basic designs for epidemiological studies, relative risk and odds ratio, confounding and interaction. Analysis of data from cohort and case control studies, Matched case control studies, logistic regression. Clinical trials: Introduction, protocols for clinical trials, cross-over designs, allocation to treatment, sample size determination, Phase I and Phase II studies. Survival Analysis: Analysis of survival data, the survival and hazard functions. Non-parametric procedures: Kaplan-Meier estimate of survivor functions, log-rank test for comparing two survival times. Parametric modeling: Proportional hazards model, Cox’s proportional hazards model.
Evaluation Criteria:
End-of-semester examination and Assignments
Suggested Readings:
Statistical methods in medical research (Armitage, P.), Case-control studies ( Schlesselman, J.J.), Clinical trials (Pocock, S.J.), Modeling survival data in medical research (Collett, D.)



ST 3078 – Case Studies/Assignments in ST (30P, 1C)

Syllabus:
Students will be given case studies/assignments which contain applications of theory covered in different courses followed by the students. The aims of this course it to enhance students’ analytical, presentation and writing skills.
Evaluation Criteria:
Continuous Assignments


ST 3079 – Case Studies/Assignments in ST + CS (30P, 1C)

Syllabus:
Students will be given case studies/assignments which contain applications of theory covered in different courses followed by the students. The aims of this course it to enhance students’ analytical, presentation and writing skills.
Evaluation Criteria:
Continuous Assignments


ST 3080 – Group Project in ST (30P, 1C)

Syllabus:
Students will work as a team to achieve a common goal: finding a solution to a given problem. This will be done under the supervision of a suitable academic staff member.
Evaluation Criteria:
Report, Viva and Supervisor’s marks



ST 3081 – Group Project in ST + CS (30P, 1C)

Syllabus:

Students will work as a team to achieve a common goal: finding a solution to a given problem. This will be done under the supervision of a suitable academic staff member.
Evaluation Criteria:
Report, Viva and Supervisor’s marks


PM 3001 – Real Analysis (45L, 3C)

Conduct by Department of Mathematics


PM 3053 – Complex Analysis (60L, 4C)

Conduct by Department of Mathematics


IT 3001 – Management Information Systems (45L, 3C)

Contact Dean’s office


IT 3002 – Data Base Management System (45L, 3C)

Contact Dean’s office


EC 4004 – Industrial Training (Enhancement course) (45L, 3C)

Syllabus:
Train students in analysing real/simulated data using latest statistical software: SPSS, SAS, MINTAB, STATISTICA, etc. The emphasis will be on applications of theory covered in the other subject areas as well as on presentation and report writing.
Evaluation Criteria:
Examinations

ST 4002 – Generalized Linear Models (30L, 2C)

Dependencies:
ST 3051
Syllabus:
Generalized linear models: Continuous models, logit models, probit models, Model diagnostics, Biological assay, Over-dispersion, Quasi Likelihood models.
Evaluation Criteria:
End of semester written examination (70%), and practical and/or assignments (30%)
Suggested Readings:
Categorical Data Analysis (Alan Agresti), Modelling Binary Data (D. Collett) Generalised Linear Models (McCullah and Nelder)


ST 4011 – Econometrics (30L, 2C)

Syllabus:
Linear regression model and properties of least squares estimates; Autocorrelation; Heteroscadasity; Multicollinearity; Model specification; Simultaneous equations; Unit roots, Non stationary and Cointegration.
Evaluation Criteria:
Examinations and assignments
Suggested Readings:
Econometric Model and Economic Forecasts (Robert Pindyck and Daniel Rubinfeld), Introductory Econometrics: A Modern Approach (Jeffrey Wooldridge), Econometric Analysis (Greene, W.G.), Econometric Methods (Jack Johnston and John DiNardo)


ST 4016 – Catergorical Data Analysis (45L, 3C)

Dependencies:
ST 4002
Syllabus:
Categorical response data, description and inference of two dimensional contingency tables, models for binary response variables, logistic regression, model diagnostics, Polytomous response variables, Log-linear models: Log-linear models for two dimensions, log-linear models for three or more dimensions, testing goodness of fit, estimation model parameters, iterative MLEs, hierarchical model fitting, diagnostics, partitioning chi-square to compare models, Strategies in model selection, Analysis of deviance, Testing conditional independence. Ordinal data: Log-linear models for ordinal variables, Models for ordinal variables in multidimensional tables.
Evaluation Criteria:
End of semester written examination (70%), and practical and/or assignments (30%)
Suggested Readings:
Categorical Data Analysis (Alan Agresti)
Analysis Ordinal Categorical Data (Alan Agresti)
Applied Logistic Regression (Hosmer and Lemeshow)


ST 4030 – Multivariate Data Analysis (45L, 3C)

Dependencies:
ST 4002
Syllabus:
Review of matrix algebra; Mean and variance- covariance of a random vector, correlation matrix; Properties of multivariate normal distribution and applications, checking for multivariate normality; Hypothesis testing using multivariate tests, tests on covariance matrices, tests of independence; Principal components analysis; Factor analysis; Discriminant analysis; Cluster analysis; Multivariate regression; MAOVA
Evaluation Criteria:
End-of-semester examination and Assignments
Suggested Readings:
Applied multivariate statistical analysis (Johnson and Wichern), Multivariate statistical methods (Morrison), Applied multivariate methods for data analysts (Johnson).


ST 4031 – Stochastic Processes and Applications (3C, 45L)

Syllabus:
Generating functions, Convolution, Compounding, Random walks, Recurrent events, Discrete parameter Markov Chains, Continuous parameter Markov Chains, Birth and Death processes, Queuing processes.
Evaluation Criteria:
End-of-semester examination and Assignments
Suggested Readings:
The Elements of Stochastic Processes(Bailey), An Introduction to Probability Theory and Applications (Feller), Stochastic Processes(Cox & Miller), Probability and Statistics with Reliability Queues and Computer Science Applications(Kishor S. Trivedi)


ST 4040/ST 4050 – Individual Project in ST/ST+CS (180P(60P+120P), 6C)

Syllabus:
The project topic could be selected from any area in the third and fourth year Statistics and/or Computer Science subject units. The selection of the project is done at the beginning of the year. The project will be done throughout the year and consists of six (6) progress reports (3 per each semester). Students are supposed to collect data for their individual projects from different Ministries/Research Institutes/Organizations, etc. They would be visiting these places during their fourth year for this purpose.
Evaluation Criteria:
Examinations
Suggested Readings:
Statistical Theory (Lindgren)


CS 4005 – Intelligent Systems (45L, 3C)

Conduct by University of Colombo School of Computing


CS 4006 – Advanced Database Systems (45L,30P, 4C)

Conduct by University of Colombo School of Computing


CS 4011 – Natural Language Processing (45L,30P,4C)

Conduct by University of Colombo School of Computing


ST 4001 – Statistical Inference – II (30L, 2C)

Dependencies:
ST 3051
Syllabus:
Parametric Inference: Introduction to Hypothesis Testing, Errors, Power, Neymann-Pearson Lemma, Most Powerful Tests, Uniformly Most Powerful Tests, Likelihood Ratio Tests: Sequential Tests; Sequential Probability Ratio Test (SPRT), Wald’s Identity, Average Sample Number (ASN). Distribution-free Inference: Tests of Randomness; Run Tests. One sample Location Tests for Median; Sign Test. Asymptotic Relative Efficiency (ARE), Two sample Location problem.
Evaluation Criteria:
Examinations and assignments
Suggested Readings:
Introduction to Theory of Statistics (Mood. Graybill and Boes), Statistical Theory (Lindgren)


ST 4012 – Special Topics for ST (30L, 2C)

Syllabus:
Selected topics depending on the availability of teaching staff.
Evaluation Criteria:

Examinations and assignments


ST 4013 – Special Topics For ST + CS (30L, 2C)

Selected topics depending on the availability of teaching staff.
Evaluation Criteria:
Examinations and assignments


ST 4015 – Decision Theory (30L, 2C)

Dependencies:
ST 3051
Syllabus:
Convex Combinations, Utility, Personal probability. No Data Problem: Loss and Regret, Mixed Actions, Minimax Principle, Bayes Actions, Admissibility. Data in Decisions: Risk function, Estimation and Testing as Special cases, Properties of Decision Rules. Bayes Theorem: Posterior Distribution, Solving the Decision Problem, Conjugate Families, Estimation and Testing. Limiting distributions, laws of large numbers.
Evaluation Criteria:
Examinations and assignments


ST 4032 – Case Studies in ST (30P, 1C)

Syllabus:
Students will be given case studies/assignments which contain applications of theory covered in different courses followed by the students. The aims of this course it to enhance students’ analytical, presentation and writing skills.
Evaluation Criteria:
Continuous Assignments


ST 4033 – Case Studies in ST+CS (30P, 1C)

Syllabus:
Students will be given case studies/assignments which contain applications of theory covered in different courses followed by the students. The aims of this course it to enhance students’ analytical, presentation and writing skills.
Evaluation Criteria:
Continuous Assignments


ST 4034 – Computational Statistics (3C, 45L)

Syllabus:
Introduction to software package R. Introduction to Random numbers: pseudo random numbers, properties of random numbers, testing for basic properties, Software for random number generation; Introduction to Simulation: Simulation of random variables, Monte Carlo simulation methods, Simulation of inventory models, Simulation of Queuing models; Data Re-sampling: Introduction to Bootstrap, Bootstrap estimation of Variance, Bootstrap Confidence Intervals, Non-parametric bootstrap algorithm, Introduction to EM algorithm, Markov Chain Monte Carlo Methods
Evaluation Criteria:
Examinations and assignments
Suggested Readings:
Computational Statistics (Givens, G. H. and Hoeting, J. A.), Elements of Computational Statistics (Gentle, J. E), An introduction to the bootstrap (Efron and Tibshirani).


PM 4004 – Real Analysis (60L, 4C)

Conduct by Department of Mathematics


CS 4003 – Logic Programming and Prolog (30L,30P, 3C)

Conduct by University of Colombo School of Computing


CS 4008 – Advanced Computer Graphics and Vision (30L,30P, 3C)

Conduct by University of Colombo School of Computing


CS 4017 – Wireless Ad-Hoc and Sensor Network (30L,30P, 3C)

Conduct by University of Colombo School of Computing


CS 4018 – Evolutionary Computing (45L, 3C)

Conduct by University of Colombo School of Computing


CS 4019 – Computational Pattern Recognition (45L,30P,4C)

Conduct by University of Colombo School of Computing


CS 4020 – Advanced Concepts in Software Design & Development (30L,30P,3C)

Conduct by University of Colombo School of Computing