CP1: Introduction to Banking Risk (overview of different types of risk). Types of models used for banking risk analysis (e.g., liquidity risk or other risks, and the regulatory or internal metrics used to assess them).
CP2: Types of models for banking risk analysis (continuation), including their connection to day-to-day risk events.
CP3: Introduction to Data Science; practical exercises using the Orange tool.
CP4: Relevant variables in banking risk analysis (presentation and interpretation of variable outcomes).
CP5: Information visualization; predictive models using data.
CP6: Practical exercises in banking risk analysis using the Orange tool (e.g., predicting liquidity stress in a bank, simulation with a new event, and discussion of results).

Since the UC is mostly of practical experimentation and critical reasoning, there will be no fully written examination component. The final grade is calculated under the category "assessement along the semester" by assessing:
Report and presentation of the project assignent results in case studies

Mandatory

Cady, Field. The Data Science Handbook. Hoboken: John Wiley & Sons Inc., 2017. Stanton, Jeffrey M. and Jeffrey M. Saltz. An Introduction to Data Science. New York: Sage Publishing Inc., 2017. Bessis, J. (2015). Risk management in banking. John Wiley & Sons. Ruozi, R., & Ferrari, P. (2013). Liquidity risk management in banks: economic and regulatory issues. Berlin, Heidelberg: Springer Berlin Heidelberg.

Optional

Grus, Joel. Data Science from Scratch, First Principles with Python. Sebastopol: O’Reilly Media, Inc., 2015. Cornett, M. M., McNutt, J. J., Strahan, P. E., & Tehranian, H. (2011). Liquidity risk management and credit supply in the financial crisis. Journal of Financial Economics, 101(2), 297-312.