MASTER DISSERTATION THESIS
| Title | ΜΕΤΑΠΤΥΧΙΑΚΗ ΔΙΠΛΩΜΑΤΚΗ ΕΡΓΑΣΙΑ / MASTER DISSERTATION THESIS |
| Code | ΕΔΥ13 |
| Faculty | Social and Economic Sciences |
| School | Economics |
| Cycle / Level | 2nd / Postgraduate |
| Teaching Period | Winter/Spring |
| Coordinator | Dimitrios Ntantakas |
| Common | No |
| Status | Active |
| Course ID | 600026527 |
Programme of Study: Data Science in Economics and Finance
Registered students: 0
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| Core | Dissertation | 3 | 2 | 30 |
| Academic Year | 2025 – 2026 |
| Class Period | Winter |
| Faculty Instructors | |
| Class ID | 600273579
|
Course Type 2021
Specialization / Direction
Mode of Delivery
- Face to face
- Distance learning
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/600273579
- eLearning:
Language of Instruction
- Greek (Instruction)
- English (Examination)
Learning Outcomes
Upon successful completion of the Master’s Thesis, students will be able to design, implement, and present a comprehensive research or applied study in the field of Data Science in Economics and Finance. They will be capable of collecting, processing, and analyzing data using advanced methods of machine learning, econometrics, and network analysis, employing tools such as R and/or Python and/or other quantitative analysis platforms. Furthermore, they will be able to draw well-documented conclusions and connect them to theoretical and practical issues of policy, business strategy, or decision-making. Finally, they will develop skills in independent research, critical thinking, and the communication of scientific results.
General Competences
- Apply knowledge in practice
- Retrieve, analyse and synthesise data and information, with the use of necessary technologies
- Adapt to new situations
- Make decisions
- Work autonomously
- Work in an international context
- Work in an interdisciplinary team
- Generate new research ideas
- Design and manage projects
- Be critical and self-critical
- Advance free, creative and causative thinking
Course Content (Syllabus)
The Master’s Thesis constitutes the final and integrative component of the program, aiming at the application of the knowledge and skills acquired throughout the coursework. Students are required to select a research or applied topic related to Data Science in Economics and Finance, formulate research questions, design the appropriate methodology, collect and analyze data, and interpret the results in light of relevant theory and literature. The thesis is completed with the writing and presentation of the study, following established scientific standards and ethical guidelines.
Keywords
Data Science; Economics; Finance
Use of Information and Communication Technologies
Use of ICT
- Use of ICT in Communication with Students
- Use of ICT in Student Assessment
Course Organization
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Reading Assigment | 400 | ✓ | |||
| Written assigments | 500 | ✓ | |||
| Total | 900 |
Student Assessment
Student Assessment methods
- Written Assignment (Formative, Summative)
- Performance / Staging (Summative)
Last Update
19-10-2025
