The Inter-Institutional Postgraduate Program (IIPP) in Data Analysis and Machine-Statistical Learning (DAMSL) is jointly organized by the University of Crete and the Foundation for Research and Technology – Hellas (FORTH).
The programme provides advanced interdisciplinary education in data analysis, machine learning, statistical methods, and computational sciences.
Students gain theoretical foundations, practical analytical skills, and hands-on training for careers in industry, research institutions, academia, and the public sector.
All courses are taught in English.
| Category | Details |
|---|---|
| Programme Duration | Minimum 3 Academic Semesters |
| Study Mode | Full-Time or Part-Time |
| Maximum Part-Time Duration | 6 Academic Semesters |
| Language | English |
| Total Credits | 90 ECTS |
| Core Credits | 30 ECTS |
| Elective Credits | 40 ECTS |
| Master Thesis | 20 ECTS |
Admission is competitive and based on academic achievements, research potential, and professional qualifications.
• Bachelor’s degree performance
• Relevant coursework and diploma thesis
• Research publications and conference participation
• Professional or research experience
• Awards, honors, or patents
• Written evaluation tests
• Interview performance
• Letters of recommendation
The programme is designed for graduates with strong quantitative and computational backgrounds.
The curriculum combines preparatory courses, compulsory core courses, elective specializations, and a mandatory Master's thesis.
| Semester |
|---|
| 1st Semester |
| 2nd Semester |
| 3rd Semester |
| Academic Structure |
|---|
| Preparatory Courses + Core Courses |
| Core Courses + Elective Courses |
| Master's Thesis + Elective Courses |
The programme provides a strong foundation in data science, machine learning, mathematics, and statistics through preparatory and core coursework.
| Preparatory Courses |
|---|
| Data Structures – 5 ECTS |
| Numerical Algorithms – 5 ECTS |
| Probability – 5 ECTS |
| Core Courses |
|---|
| Introduction to Data Science – 10 ECTS |
| Introduction to Machine Learning – 10 ECTS |
| Mathematical & Computational Statistics – 10 ECTS |
Students may select elective courses according to their academic interests and research objectives.
| Elective Courses |
|---|
| Deep Learning |
| Optimization |
| Machine Vision |
| Signal Analysis |
| Numerical Analysis |
| Information Retrieval |
| Big Data Analytics |
| Elective Courses |
|---|
| Time Series Analysis |
| Development of Scientific Software |
| Geometric Algorithms |
| Information Theory |
| Topics in Statistics |
| Topics in Data Science |
| Topics in Machine Learning |
Completion of the Master’s Thesis is mandatory and corresponds to 20 ECTS credits. The thesis is carried out within the thematic areas of the programme under the supervision of an academic advisor and examination committee. Students must submit the thesis in English and deliver the final text to the examination committee at least 15 days prior to the defense.
