Master
2025/2026
Financial Databases and Data Processing
Type:
Elective course (Strategic Corporate Finance)
Delivered by:
School of Finance
Where:
Faculty of Economic Sciences
When:
1 year, 3 module
Open to:
students of one campus
Instructors:
Aleksandr Tomtosov
Language:
English
ECTS credits:
3
Contact hours:
40
Course Syllabus
Abstract
The course is devoted to building queries to unload financial data and writing Python programs for further processing and hypothesis testing.
The course consists of three parts:
1) The unit on databases includes working with the Wind terminal through the MS Excel add-in and offloading financial data from Yahoo Finance, Fred, and the Kenneth French database. Data processing includes cleaning outliers, bringing tables with different financials to a standard format, and constructing benchmarks. Hypothesis testing uses standard econometric methods for large datasets and practice-oriented methods, including fundamental indexing and portfolio construction techniques.
2) An overview of graph methods in business. Modern ML and practical applications. Anti-fraud, customer segmentation, and risk identification.
3) AI and ML techniques in banking analytics.
Learning Objectives
- - Understanding how to use Python for getting financial data, including stock market data, financial report data, and alternative data.
- - Understanding how to use ML techniques for business analytics
- - Be able to apply AI and ML tools for analysing banking performance.
Expected Learning Outcomes
- Be able to collect financial data from Windows, Yahoo Finance, and FRED databases using Excel and Python add-ins.
Course Contents
- Financial databases and data processing
- Modern ML and practical applications in business
- AI and ML techniques in banking analytics.
Assessment Elements
- TestTest is based on ML in business analytics topics
- Project in AI and ML in bankingProject is based on a AI and ML methods in banking topics
- Project with financial databases
Interim Assessment
- 2025/2026 3rd module0.4 * Project in AI and ML in banking + 0.3 * Project with financial databases + 0.3 * Test
Bibliography
Recommended Core Bibliography
- Nelli, F. (2018). Python Data Analytics : With Pandas, NumPy, and Matplotlib (Vol. Second edition). New York, NY: Apress. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1905344
- Python for data analysis : data wrangling with pandas, numPy, and IPhython, Mckinney, W., 2017
Recommended Additional Bibliography
- Pandas for everyone : Python data analysis, Chen, D. Y., 2023