Master of Applied Statistics (LAMSAMA Accreditation)
Our on-campus curriculum consists of on-campus lectures, project work, and contact moments where feasible, but also online teaching materials and Q&A sessions when on-campus activities cannot be conducted or attended.
What is it About?
The Master of Applied Statistics program is an eighteen-month program consisting of 41–44 credits. The program focuses on developing expertise in data analysis and its direct application to real issues. Students can study on campus, online, or through a hybrid program, allowing for a flexible, student-centered education. The curriculum includes courses in statistics for business and industry, a statistical approach to social sciences, actuarial sciences, and epidemiology—quantitative and data science. Students will learn from internationally renowned statisticians and data scientists and gain valuable knowledge for their future careers. The program is in high demand on the employment market and is accredited by the LAMSAMA.
What Will I Study?
Select one of four specializations to concentrate your studies. There are four specialities available:
Statistics for business and industry;
statistics for the social sciences.
Actuarial Sciences
Epidemiology Quantitative and Data Science
Why Study With Us?
Eighteen Months (41-44 Credits)
English broadcast
Develop expertise in data analysis with direct application to societal issues
Flexible, student-centered education: study on campus, online or hybrid.
Gain knowledge from internationally renowned statisticians and data scientists
Statistics for business and industry, and a statistical approach to social sciences
Actuarial Sciences
Epidemiology Quantitative and Data Science
Strong demand on the employment market
Accreditation from the LAMSAMA
Career Paths
All data science specializations give a solid foundation, but the first three emphasize statistics more. The Data Science concentration maintains a solid foundation in statistics, but offers more courses on other data science topics (e.g. data visualization, data management, programming and algorithms).
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