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Programme Profile

The Master of Science in Applied Statistics programme is offered on a full-time and part-time basis in order to accommodate both working professionals and fresh graduates. The programme offers a curriculum that provides a balanced approach towards learning of statistical theory and its applications.

Programme Objective

To produce graduates who are able to:

Mode and Duration

Full Time : 3-4 Semesters
Part Time : 4-8 Semesters

Classes are conducted on weekday evenings and Saturday.

Programme Structure

Core courses : 22 credit hours
Dissertation : 9 credit hours
Elective courses : 9 credit hours
Total credit hours : 40 credit hours

Suitable Candidates

Fresh graduates, researchers, professionals and executives who wish to enhance their analytical skills in decision making that are relevant for their career advancements in the public and corporate sectors.

Career Opportunities

The graduate can be employed as statisticians, research executives in financial industries, banking sectors, communication and media, medical and pharmaceutical sectors, agricultural sectors and quality assurance executives.

Admission Requirements

Plan of Study

All students are required to take the following courses:

Semester 1 Year 1
Classical and Bayesian Statistical Theory, Advanced Multivariate Analysis, Sampling Theory and one elective course

Semester 2 Year 1
Statistical Computing, Applied Statistical Modelling, Seminar on Contemporary Statistical Issues, Statistical Consultancy and one elective course

Semester 3 Year 2
Philosophy of Statistical Sciences, Dissertation in Applied Statistics and one elective course

Elective Courses
Students are required to select one of the three specializations. They are required to select only three elective courses from the chosen specialization.

Specialization 1: Biotechnology Industry
Statistical Data Mining, Biostatistics, Design and Analysis of Experiments, Quality Standards, Control and Improvement, Stochastic Processes in Biotechnology, Categorical Data Analysis

Specialization 2: Business
Statistical Data Mining, Advanced Time Series Modelling and Forecasting, Input-Output Analysis, Consumer Research, Marketing Models, Organizational Behavior

Specialization 3: Social Science
Statistical Data Mining, Categorical Data Analysis, Demographic Analysis, Applied Social Statistics, Advanced Techniques of Population Analysis, Advance Time Series Modelling and Forecasting, Input-Output Analysis.