Level III Course Units

Course No.Course ModuleCreditsType 
Sem I
IS 3001Sampling Techniques230Lx
IS 3050Statistical Inference 345Lx
IS 3051Advanced Statistical Process Control230Lx
ST 3006Regression Analysis230Lx
ST 3074Time Series Analysis230Lo
ST 3076Reliability Data Analysis345Lo
ST 3085Computational Statistics230Lx
MS 3002Advanced Marketing Research115Lx
MS 3003Operational Research II230Lo
CS 3112Advanced Web Development330L 30Po
Sem II
IS 3003Special Topics I215L 30Po
IS 3052Advanced Topics in Experimental Design230Lx
IS 3053Data Mining Techniques215L 30Px
ST 3082Statistical Learning I260Px
ST 3083Multivariate Data Analysis345Lo
IS 3054Literature Review in IS 130Px
MS 3004Quality Management / Project Management230Lx
IT 3002Database Systems330P 30Lo

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

Level IV Course Units

Course No.Course ModuleCreditsType 
Sem I
IS 4002Advanced Statistical Modeling 345Lo
IS 4003Special Topics II 230Lo
ST 4011Econometrics230Lo
ST 4031Stochastic Processes and Applications345Lx
ST 4035Data Science 330L 30Po
ST 4051Scientific Writing 130Px
ST 4052Statistical Learning II260Px
Sem II
IS 4005Industrial Training 4120Px
IS 4006Individual Project 6180Px
CS 4113Natural Language Processing 330L 30Po

Course Title: Sampling Techniques (2C, 30L)

Course Code: IS 3001

Prerequisites: IS 1009/ST 1011, ST 2006

Intended learning outcomes:

Upon the successful completion of the course the students should be able to identify and effectively use the theory behind sampling techniques that are commonly used in statistics.

Course content:

Simple Random Sampling (SRS), Sample size determination, Ratio and Regression estimators under SRS, Stratified, Systematic, and Quota sampling. Separate and combined estimators for stratified sampling. Cluster sampling, Multi-stage sampling, Complex sample designs and related issues.

 Method/s of evaluation: End of semester examination (70%) and Continuous assessments & group projects (30%)


Course Title:  Statistical Inference – [3credits (45L), For IS (core)]

Course Code: IS 3050

Intended Learning Outcomes: Upon successful completion of the course, students should be able to recognize the underlying theory behind statistical estimation, apply the necessary techniques to find estimates of population parameters and appraise the properties of estimators.

Course Content: Generating moments using characteristic function; Sampling from Normal population: sampling distributions of sample mean and sample variance (s2), independence of sample mean and s2; Properties of estimators: 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.

Method of Delivery: Interactive classroom sessions

Evaluation Criteria:  End-of-semester examination (70%) and assignments (30%)


Course Title: Time Series Analysis– [2credits (30L), For ST (core), ST+CS (optional)]

Course Code:  ST 3074

Learning Outcomes:  Upon the successful completion students should be able to model and forecast univariate time series.

Course Content:  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 stationarity; Modelling time series: time series models, model identification, parameter estimation, diagnostic checks, forecasting.

Method of Delivery: Lectures and Practicals

Method of Evaluation: End-of-semester examination (80%) and In-class Assignments (20%)


Course Title: Computational Statistics – [2 credits (30L), For ST (core), IS(core) ST+CS (optional)]

Course Code: ST 3085

Learning Outcomes: After a successful completion, students should be able to generate random numbers; simulate data; apply bootstrap methods to analyze data.

Course Content: 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, Introduction to EM algorithm.

Method of Delivery: Lectures and lab sessions

Evaluation Criteria:  End-of –semester examination (70%) and continuous assessments (30%)


Course Title: Multivariate Data Analysis– [3credits (45L), For ST (core), ST+CS (core), IS (elective)]

Course Code: ST 3083

Learning Outcomes:  After a successful completion, students should be able to make decisions based on multivariate hypothesis tests; carryout dimension reduction methods; clustering data and discriminate new observations to pre-defined clusters

Course Content: 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;  MANOVA ; Principal component analysis; Factor analysis; Discriminant analysis; Cluster analysis.

Method of Delivery: Lectures

Method of Evaluation: End-of-semester examination and Assignments

 


Course Title: Essential Mathematics for Statistics – [3 credit (45L), For ST (core), ST+CS (core), IS (core), 4G (core)]
Course Code: ST 3013
Learning Outcomes: After a successful completion, students should be able to apply basic mathematical tools in solving theoretical and practical problems in Statistics.

Course Content: Linear algebra: Linear dependence, rank and the solution of homogeneous equations, characteristic polynomials, eigenvalues , eigenvectors, spectral theorem for symmetric matrices, idempotent matrices and properties, orthogonal projections, trace of a matrix and properties, positive definite/semi definite matrices, quadratic forms, differential calculus in matrix notation, direct product (kronecker)of any two matrices, generalized inverse /conditional inverse ; Calculus: Concepts of functions, limits and continuity, L’Hopital’s rule, the fundamental theorem of calculus, approximation of definite integrals, Improper integrals; Series and Sequences: sequences and their convergence, series and convergence of series, power series and their convergence of radius, Taylor series and their application; Several variable calculus: functions of several variables, continuity, differentiability, derivatives, multiple integrals, change of variables

Method of Delivery: Lectures
Method of Evaluation: End-of-semester examination


ST 3082 –  Statistical Learning I – [2credit (60P), For ST (core), ST+CS (core), IS (core)]

Indented learning outcomes: Upon completion of this course, student should be able to explore complex data sets, select appropriate statistical techniques to solve problems involved and justify their choice. The students should be able to implement these techniques using an appropriate programming language, evaluate the results and explain the results to non statisticians using non statistical terms.
Course Contents: Introduction to statistical learning; Advanced regression model: understanding models, variable selection, validation and cross validation, Shrinkage method and ridge regression, Lasso, principal component regression and partial least squares; Resembling method: cross validation, bootstrap; Classification: understanding classification problems using logistic regression, multivariate logistic regression, and discriminate analysis.
Method of Delivery: Interactive lab sessions and assignments
Evaluation Criteria: Assignments


Course Title Literature Review – [1credit (30P), For ST (core), ST+CS (core), IS (core)]

Course Code: ST 4052

Learning Outcomes: Upon completion of this course, student should be able to search, identify, read, and analyze research articles which are relevant to their research activities; Write a quality scientific literature review for a selected research problem.

Course Content: 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 students, write a professional quality literature review for a problem of your choice.

Method of Delivery: Interactive class room sessions and assignments

Evaluation Criteria: Assignments

 


Course Title: Advanced Statistical Modeling –[3credits (45L), For IS(elective)]

Course Code: IS 4002

Intended Learning Outcomes:  At the completion of the course, the students should be able to identify and apply a suitable statistical model for a given dataset. Student should also be able to apply appropriate diagnostics to evaluate the model.

Course Contents: Introduction to statistical modeling; Exponential family of distributions; Generalized linear models: link function, parameter estimation and inferences, hypothesis testing, diagnostics, and adequacy; Logistic regression and over dispersion; multinomial regression; Poisson regression; Mixed models.

Method of Delivery: Lectures

Evaluation Criteria:  Based on end of semester examination (80%) + In-class Assignments (20%)


Course Title: Econometrics (2C, 30L)

Course Code: ST 4011

Prerequisites: ST 3008, ST 3009

Intended learning outcomes:

Upon successful completion the students will be able to apply statistical methods in the context of economics and carry out a successful econometric analysis.

Course Content:

The application of linear regression model and the interpretations of properties of least squares estimates in the context of economic theory, an introduction to violations of OLS assumptions in economics, Simultaneous equations, Time Series Econometrics, Case studies.

 Method/s of evaluation: End of semester examination (70%) and Continuous assessment [minimum of 3 ] (30%)


ST 4052 – Statistical Learning II – [2credit (60P), For ST (core) , ST+CS (core), IS (core)]

Indented learning outcomes: Upon completion of this course, student should be able to explore complex data sets, select the relevant statistical techniques discussed to solve problems involved and justify their choice. The students should be able to implement these techniques using an appropriate programming language, evaluate the results and explain the results to non statisticians using non statistical terms.
Course Contents: Moving beyond linearity: polynomial regression, regression splines, smoothing splines; Tree-based methods: the basics of decision tree, bagging, random forest, boosting; Support Vector machines; Unsupervised learning: dimension reduction techniques, clustering
Method of Delivery: Interactive lab sessions and assignments
Evaluation criteria: Assignments
Suggested reading: An Introduction to Statistical Learning by James, Witten, Hastie, and Tibshirani


Course Title: Data Science (3C, 30L 15P)

Course Code: ST 4035

Prerequisites: ST 3011, ST 3013, CS 3008

Intended Learning Outcomes:

After a successful completion a student should be able to apply basic techniques of Data Science for decision making.

Course Content:

Introduction; Ethics; Data Wrangling & Pre-processing; How to deal with large data sets: Parallel computing, Map reduce framework – Hadoop; Data Communication & Visualization; Statistical Methods: Regression, Logistic Regression, Random Forest, Support Vector Machines; Machine Learning Algorithms.

 Method/s of Evaluation: Minimum of 3 continuous assessments(Inclass) and 1 group project