Bachelor
2026/2027
Research Seminar "Data Science in Financial Markets"
Type:
Compulsory course (Data Science and Business Analytics)
Delivered by:
Big Data and Information Retrieval School
Where:
Faculty of Computer Science
When:
4 year, 1-3 module
Open to:
students of one campus
Language:
English
ECTS credits:
4
Contact hours:
52
Course Syllabus
Abstract
The specialization seminar offers the opportunity to study subjects and sections of mathematical statistics related to the application of differential equations, machine learning, probability theory and mathematical for modeling various solutions of a wide range of theoretical and applied problems. These tasks include analysis and forecasting of time series, automatic detection of trend changes, forecasting “black swan” events, and analysis of stable configurations in the community. The computational methods used are standard for machine learning: clustering, pattern recognition, dimension reduction. The purpose of the research seminar is to expand the research horizons of students. It is assumed that at the end of the course, the student will be able to prepare a research paper or grant application. To do this, the student will be involved in the following activities: attending classes (it is obligatory), analyzing a large number of sources in a foreign area for the student in order to learn how to highlight mathematical problems in non-mathematical articles, completing part of a group project, preparing presentations and discussion (peer review) of other people's projects and presentations. Prerequisites Knowledge of basic mathematics: analysis, linear algebra, probability theory, - algorithms, programming fundamentals, the ability to understand computational packages
Learning Objectives
- Be able to prepare and conduct a presentation with a report on a scientific topic, as well as conduct an academic discussion on the materials of the report.
- To be able to independently choose and study modern scientific articles, find relevant literature.
- Be able to write scientific texts.
Expected Learning Outcomes
- Be able to prepare and conduct a presentation with a report on a scientific topic, as well as conduct an academic discussion on the materials of the report.
- Methods for verifying empirical results: hypothesis testing, bootstrap, randomization, etc.
- Methods of mathematical modeling based on (stochastic) differential equations, probability theory.
- Modern computational methods used in related fields, in particular, when forecasting time series and solving inverse problems (Fourier analysis, wavelets, regression, SSA, dimension reduction, moving averages, neural networks, filters, etc. - understanding the advantages and disadvantages each of the methods.
- To be able to independently choose and study modern scientific articles, find relevant literature. Be able to write scientific texts.
Assessment Elements
- module 3 group presentations and individual research presentation
- module 2 group presentations
- module 1 group presentation
Interim Assessment
- 2026/2027 3rd module0.4 * module 3 group presentations and individual research presentation + 0.2 * module 1 group presentation + 0.4 * module 2 group presentations
Bibliography
Recommended Core Bibliography
- Algorithmic trading : winning strategies and their rationale, Chan, E. P., 2013
- Empirical market microstructure : the institutions, economics, and econometrics of securities trading, Hasbrouck, J., 2007
Recommended Additional Bibliography
- Pattern recognition and machine learning, Bishop, C. M., 2006