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M.Sc. In Applied Statistics

The general aim of this course is to equip you with the computational skills to solve real-life problems using data modern computationally-intensive methods to solve problems or support data-driven decision-making and planning. The program will therefore have a professional orientation, emphasizing applications and applicable theory. It is intended to provide "operational" knowledge in the field of data processing and management.

 

Program Structure

The program will consist of course units ranging in credit value from 2 to 4, with the majority being 3-credit courses, in line with the credit rating for similar courses at many U.S. universities. Completion of a total of 36 credit hours is required for award of the degree. All courses will be taught and assessed on the basis of any combination of continuous assessment, examinations and practical or lab-based activities. A letter grade will be awarded for each course completed. Courses will be offered over 2 sessions, to cover 18 credits during each session.

  1. Matrix Algebra (3 credits)
  2. Advanced Probability Theory (3 credits)
  3. Computing for Statistical Analysis (2 credits)
  4. Introduction to Mathematical Statistics and Generalised Linear Models (4 credits)
  5. Sampling Theory and Methods (3 credits)
  6. Advanced Regression Analysis (3 credits)
  7. Experimental Design and Analysis of Variance (3 credits)
  8. Applied Multivariate Analysis (3 credits)
  9. Statistical Analysis of Randomized and Observational Studies (4 credits)
  10. Applied Data Mining (3 credits)
  11. Applied Time Series Analysis and Forecasting (3 credits)
  12. Working with Large Databases (2 credits)
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