Applied Statistics deal with the current need to collect and analyze big data for industrial, commercial, financial, social and development applications. In today’s world, there is need for data on virtually everything. Organizations such as Google are providing data on almost any real life situation including housing, population growth and movement, transport, financial transactions, economic and social data, health, education, etc. Management of these large data require modern skills backed up with computer applications. The program in Applied Statistics equips the student with the analytical, scientific and computational skills to solve real-life data problems using modern computationally-intensive methods to solve problems, thereby improving productivity, accountability and profitability of public and private organizations.

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

The program consists 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 of coursework and a project 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 3 semesters (18 calendar months), to cover 18 credits during each session. Courses include: Matrix Algebra; Advanced Probability Theory; Computing for Statistical Analysis; Introduction to Mathematical Statistics and Generalised Linear Models; Sampling Theory and Methods; Advanced Regression Analysis; Experimental Design and Analysis of Variance; Applied Multivariate Analysis; Statistical Analysis of Randomized and Observational Studies; Applied Data Mining; Applied Time Series Analysis and Forecasting; Working with Large Databases.

Admission requirements for the MSc program include a Bachelor’s degree in relevant field with a second class upper (minimum CGPA of 3.0 on a 4 point scale and 3.5 on a 5 point scale). The candidate must obtain a minimum CGPA of 3.0 to graduate from the program. The programme is very intensive and only well-motivated and prepared students are encouraged to apply. The courses are taught as 3- or 4-week modules of 45 to 60 hours, including lectures and problem solving sessions. Details of the course outline are in the handbook. The Problem Based Learning (PBL) and Outcome Based Education (OBE) approaches are used in teaching.

For more details on course description, fees, faculty, visit: www.aust.edu.ng or send email to: dap@aust.edu.ng