DATA SCIENCE IN CENTRAL BANKING
| Title | ΕΠΙΣΤΗΜΗ ΤΩΝ ΔΕΔΟΜΕΝΩΝ ΣΤΗΝ ΚΕΝΤΡΙΚΗ ΤΡΑΠΕΖΙΚΗ / DATA SCIENCE IN CENTRAL BANKING |
| Code | ΕΔΕ07 |
| Faculty | Social and Economic Sciences |
| School | Economics |
| Cycle / Level | 2nd / Postgraduate |
| Teaching Period | Winter/Spring |
| Coordinator | Athanasios Kazanas |
| Common | No |
| Status | Active |
| Course ID | 600026521 |
Programme of Study: Data Science in Economics and Finance
Registered students: 0
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| Core | Elective Courses | 1 | 1 | 6 |
| Academic Year | 2025 – 2026 |
| Class Period | Winter |
| Faculty Instructors | |
| Class ID | 600273583
|
Course Type 2021
Specialization / Direction
Mode of Delivery
- Distance learning
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/600273583
Language of Instruction
- Greek (Instruction, Examination)
- English (Instruction, Examination)
Learning Outcomes
The course aims to deepen understanding of specialized topics within the field of monetary economics, focusing on the theory and policy of money. Particular emphasis is placed on aspects of central banking theory, such as the importance of banking supervision, the independence of the central bank, the measurement of monetary policy effectiveness, and the transmission mechanisms of monetary policy to the real economy. By the end of this module, students will be able to:
• Apply advanced data analytics techniques to central bank operations and policy analysis
• Evaluate the role of big data in monetary policy, financial stability, and macroprudential regulation
• Design and implement models for forecasting macroeconomic indicators relevant to central banking
• Critically assess the use of machine learning and econometrics in central bank decision-making
• Interpret and communicate analytical findings to support policy formulation
• Use of programming languages (e.g., Matlab, Python, R) for financial data analysis
General Competences
- Apply knowledge in practice
- Retrieve, analyse and synthesise data and information, with the use of necessary technologies
- Work in teams
- Advance free, creative and causative thinking
Course Content (Syllabus)
1. Introduction to Central Banking and Data Analytics
• Functions of central banks in modern economies
• Overview of data sources: macroeconomic, financial, regulatory
2. Monetary Policy and Data-Driven Decision Making
• Inflation targeting and interest rate modeling
• Real-time data analysis for monetary policy
3. Financial Stability and Macroprudential Surveillance
• Stress testing and systemic risk indicators
• Network analysis of financial institutions
4. Big Data and Machine Learning in Central Banking
• Applications of supervised and unsupervised learning
• Natural language processing for central bank communication
5. Forecasting and Nowcasting
• Time-series models (ARIMA, VAR, Bayesian models)
• Mixed-frequency data and real-time indicators
6. Case Studies and Policy Simulations
• ECB, Bank of England, and Bundesbank analytics frameworks
• Simulation of policy scenarios using synthetic data
7. Ethics, Transparency, and Communication
• Data governance in central banks
• Communicating uncertainty and model limitations
Keywords
Monetary Economics, Transmission Mechanism of Monetary Policy, Applications in Central Banking using programming languages (e.g., R, Matlab, Python)
Educational Material Types
- Slide presentations
- Book
Use of Information and Communication Technologies
Use of ICT
- Use of ICT in Course Teaching
- Use of ICT in Laboratory Teaching
- Use of ICT in Communication with Students
- Use of ICT in Student Assessment
Course Organization
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | 39 | ||||
| Reading Assigment | 60 | ||||
| Written assigments | 81 | ||||
| Total | 180 |
Student Assessment
Description
50% written exam + 30% midterm test + 20% group assignment (2,000 words) with a presentation on one of the course topics.
Student Assessment methods
- Written Exam with Multiple Choice Questions (Summative)
- Written Assignment (Summative)
- Performance / Staging (Summative)
- Written Exam with Problem Solving (Summative)
Bibliography
Course Bibliography (Eudoxus)
Belke Ansgar, Polleit Thorsten (2025). Νομισματική Οικονομική σε Περιβάλλον Παγκοσμιοποιημένων Χρηματοπιστωτικών Αγορών. Εκδόσεις Broken Hill.
Mishkin, Frederic S. (2022). Χρήμα, Τράπεζες και Χρηματοοικονομικές Αγορές, UTOPIA ΕΚΔΟΣΕΙΣ Μ. ΕΠΕ.
Last Update
06-09-2025
