MSc in Applied Statistics
The postgraduate program in applied statistics is to train postgraduate students to the following fields:
Big Data and Computational Statistics
In particular, the program aims at:
Creating specialized scientists in fields compatible with the research activity, the respective specialization fields of the undergraduate program and with the department’s faculty.
Educating senior executives of companies and organizations in the private and public sector in order to meet the needs of their specialized activities in data analysis.
To internationally promoting the University and to develop collaborative networks with the international scientific community and the maximum possible activation within the framework of the opportunities offered at European and global level.
Linking the educational process to the needs of the market and the economy in general.
The duration of the Program is five semesters, of which the 5th is dedicated to the thesis writing. Classes take place 2 afternoons per week.
During the first, second and third semester there is a total of eight obligatory courses. In the third and fourth semester, there are four optional courses.
- Applied Probability – Estimation (5 ECTS)
- Hypothesis Testing – Linear Models (5 ECTS)
- Statistical Applications using R (5 ECTS)
- Optimization Techniques (5 ECTS)
- Generalized Linear Models (5 ECTS)
- Time Series Analysis and Forecasting using R (5 ECTS)
- Medical Statistics (5 ECTS)
- Computational Statistics using R (5 ECTS)
- Optional Course taken from the list below (5 ECTS)
Three Optional Courses taken from the list below (15 ECTS in total)
Optional Courses List
- Applied Bayesian Statistics (5 ECTS)
- Statistical Learning (5 ECTS)
- Survival Analysis (5 ECTS)
- Quality Control and Reliability (5 ECTS)
- High dimensional Statistics (5 ECTS)
- Clinical Trials (5 ECTS)
- Financial Mathematics with Applications in MATLAB and PYTHON (5 ECTS)
Thesis writing (30 ECTS)
You can find a short description of the courses here.
The study guide can be found here.