## Level III Course Units

Course No. | Course Module | Credits | Type | |
---|---|---|---|---|

Sem I | ||||

IS 3001 | Sampling Techniques | 2 | 30L | x |

IS 3050 | Statistical Inference | 3 | 45L | x |

IS 3051 | Advanced Statistical Process Control | 2 | 30L | x |

ST 3006 | Regression Analysis | 2 | 30L | x |

ST 3074 | Time Series Analysis | 2 | 30L | o |

ST 3076 | Reliability Data Analysis | 3 | 45L | o |

ST 3085 | Computational Statistics | 2 | 30L | x |

MS 3002 | Advanced Marketing Research | 1 | 15L | x |

MS 3003 | Operational Research II | 2 | 30L | o |

CS 3112 | Advanced Web Development | 3 | 30L 30P | o |

Sem II | ||||

IS 3003 | Special Topics I | 2 | 15L 30P | o |

IS 3052 | Advanced Topics in Experimental Design | 2 | 30L | x |

IS 3053 | Data Mining Techniques | 2 | 15L 30P | x |

ST 3082 | Statistical Learning I | 2 | 60P | x |

ST 3083 | Multivariate Data Analysis | 3 | 45L | o |

IS 3054 | Literature Review in IS | 1 | 30P | x |

MS 3004 | Quality Management / Project Management | 2 | 30L | x |

IT 3002 | Database Systems | 3 | 30P 30L | o |

**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 Module | Credits | Type | |
---|---|---|---|---|

Sem I | ||||

IS 4002 | Advanced Statistical Modeling | 3 | 45L | o |

IS 4003 | Special Topics II | 2 | 30L | o |

ST 4011 | Econometrics | 2 | 30L | o |

ST 4031 | Stochastic Processes and Applications | 3 | 45L | x |

ST 4035 | Data Science | 3 | 30L 30P | o |

ST 4051 | Scientific Writing | 1 | 30P | x |

ST 4052 | Statistical Learning II | 2 | 60P | x |

Sem II | ||||

IS 4005 | Industrial Training | 4 | 120P | x |

IS 4006 | Individual Project | 6 | 180P | x |

CS 4113 | Natural Language Processing | 3 | 30L 30P | o |

**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 (s^{2}), independence of sample mean and s^{2}; 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