Curriculum of the Department of Statistics and Insurance Science
The Study Program is presented in detail by semester below. Each course corresponds to 5 ECTS credits. Specifically, the
study program includes a total of 57 courses, each with 5 ECTS credits.
1st Year | |||
1st Semester Courses | Hours/Credits | 2nd Semester Courses | Hours/Credits |
Introduction to Probabilities | 3/101 | Probabilities I | 3/201 |
Financial Mathematics | 3/204 | Statistics I | 3/202 |
Mathematics I | 3/103 | Mathematics II | 3/203 |
Introduction to Statistics | 3/104 | Banking Accounting and Insurance Org Accounting | 3/205 |
Financial Accounting | 3/106 | Microeconomic Theory | 3/206 |
Methodology of Educational Research | 3/107 | Educational Technology | 3/207 |
2nd Year | |||
3rd Semester Courses | Hours/Credits | 4th Semester Courses | Hours/Credits |
Probabilities II | 3/301 | Statistics II | 3/401 |
Regression Analysis | 3/303 | Stochastic Processes | 3/402 |
Regression Analysis | 3/303 | Data Analysis with Python | 3/408 |
Financial Analysis | 3/305 | Analysis of Variance | 3/404 |
Linear Algebra | 3/306 | Social Insurances | 3/405 |
Educational Psychology – Designing online courses-Moodle | 3/307 | Critical Thinking and Statistical Reasoning | 3/407 |
Educational Assessment | 3/308 |
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3rd Year | |||
5th Semester Courses | Hours/Credits | 6th Semester Courses | Hours/Credits |
Mathematical Statistics | 3/501 | Economic Time Series Analysis | 3/601 |
Statistical Software I | 3/503 | Loss Distributions | 3/602 |
Business Insurances | 3/504 | Life Insurances | 3/604 |
Methods and Techniques of Sampling | 3/505 | Research Methodology | 3/605 |
Teaching Methodology and Didactics | 3/506 | Practical Exercise in Microteaching | 3/606 |
Biostatistics | 3/507 | Applied Statistics | 3/607 |
Data Management and Analysis | 3/508 | Quality Control Statistics | 3/608 |
Actuarial Mathematics (Elective) | 3/502 | Designing Socio-Economic Research (Elective) | 3/603 |
4th Year | |||
7th Semester Courses | Hours/Credits | 8th Semester Courses | Hours/Credits |
Non-Parametric Statistics | 3/701 | Statistical Programs II | 3/801 |
Machine Learning | 3/702 | Multicriteria Analysis | 3/802 |
Bayesian Statistics | 3/703 | Business Administration | 3/803 |
Operational Research | 3/704 | Simulation | 3/804 |
Data Analysis in Energy (Elective) | 3/705 | Multivariate Analysis (Elective) | 3/805 |
Survival Analysis (Elective) | 3/706 | Meta-analysis (Elective) | 3/806 |
Data Mining (Elective) | 3/707 | Big Data Analytics (Elective) | 3/807 |
Special Topics in Econometrics (Elective) | 3/708 | Programming (SQL) (Elective) | 3/808 |
Degree Requirements:
Successful examination in 48 courses corresponding to 240 ECTS credits is required, specifically:
- Successful examination in all compulsory (CP) courses offered in the program, either 41 courses corresponding to 205 ECTS credits.
- Successful examination in 7 elective (EP) courses of the program, 1 must belong to category A, 1 to category B, 1 to category C, 2 to category D, and 2 to category E as referred to in the following tables (35 ECTS credits).
Category A (1 choice) | Hours/Credits |
Educational Psychology – Designing online courses-Moodle | 3/307 |
Educational Assessment | 3/308 |
Category B (1 choice) | Hours/Credits |
Biostatistics | 3/507 |
Data Management and Analysis | 3/508 |
Actuarial Mathematics | 3/502 |
Biostatistics | 3/507 |
Category C (1 choice) | Hours/Credits |
Applied Statistics | 3/607 |
Quality Control Statistics | 3/608 |
Designing Socio-Economic Research | 3/603 |
Category D (2 choices) |
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Data Analysis in Energy | 3/705 |
Survival Analysis | 3/706 |
Data Mining | 3/707 |
Special Topics in Econometrics | 3/708 |
Each course listed under Categories C and D serves as an elective within the academic program, providing students with opportunities to specialize further in areas of statistics, data analysis, and research methods. Category C focuses on statistical application, quality control, and the design of socio-economic research, catering to students interested in applied statistics and research methodologies. Category D offers more specialized topics such as data analysis in the energy sector, survival analysis, data mining, and econometrics, allowing students to gain expertise in specific statistical techniques and their applications in various fields.
The degree grade is defined as the simple arithmetic mean of all the courses required for obtaining the degree.
Grading Scale:
8.50–10 “Excellent”
6.50–8.49 “Very Good”
5.00–6.49 “Good