The program is structured in three (3) academic semesters. Four (4) Compulsory Core Courses are taught in the first semester. In the 2nd semester two (2) Compulsory Core Courses are taught and a number of Elective Courses of which each student must choose two (2). In the third semester the students prepare their MSc Thesis.
|MCDA101||Methods For Statistical Data Analysis||7.5|
|MCDA201||Natural Computing and Neural Networks||7.5|
|MCDA102||Optimization and Decision Models||7.5|
|MCDA202||Algorithm Analysis and Data Structures||7.5|
|MCDA203||Databases and Data Mining||7.5|
|MCDA103||Data-driven Probabilistic Models in Decision Making Process||7.5|
|Code||Courses (two of the following)||ECTS|
|MCDA212||Numerical Methods for Data Science||7.5|
|MCDA111||Applied Bayesian Statistics and Simulation||7.5|
|MCDA112||Survival and Reliability Models||7.5|
|MCDA113||Time Series Analysis||7.5|
Multivariate Data Analysis and Statistical Inference
For the acquisition of the postgraduate qualification, ninety (90) European credits (ECTS) are required. Of these, thirty (30) units correspond to the four compulsory courses of the first semester (core courses), thirty (30) units correspond to the 4 courses of the second semester [:2 compulsory and 2 elective courses] and thirty (30) correspond to the MSc thesis of the 3rd semester.