Магистратура
2026/2027
Семинар наставника
ID 1120538
Статус:
Курс обязательный (Стратегическое управление финансами фирмы)
Кто читает:
Школа финансов
Где читается:
Факультет экономических наук
Когда читается:
1-й курс, 1-4 модуль
Охват аудитории:
для своего кампуса
Преподаватели:
Бабынин Андрей Игоревич
Язык:
английский
Кредиты:
7
Контактные часы:
14
Course Syllabus
Abstract
Financial Data Science is a practice-oriented track focused on the application of machine learning, artificial intelligence, and modern data-analysis tools to financial problems. The track is designed for students who want to work with financial data in a practical setting and understand how ML and AI are used in banking, fintech, investment analysis, risk management, and corporate finance.
During the track, students will work with real or realistic financial datasets and study the full applied workflow: data preparation, feature construction, model selection, validation, interpretation, and implementation. Particular attention will be paid to machine learning for tabular financial data, text analysis and large language models, alternative data, forecasting, classification, and the practical limitations of AI models in finance.
The track is suitable for students who are interested in careers in fintech, banking, quantitative and data-driven finance, financial analytics, and related roles where strong programming and data skills are required. It is especially useful for those who want to build a portfolio of applied projects and learn how to use modern AI tools to solve financial problems rather than study them only at a theoretical level.
Learning Objectives
- To obtain a systematic understanding of modern applications of machine learning, artificial intelligence, and data analysis in finance.
- To form an individual study plan
- To develop a research proposal
Expected Learning Outcomes
- Writing a research proposal and research schedule (for the 1st year students)
- Defence of Master's thesis (for the 2nd year students)
Course Contents
- Introduction to the Financial Data Science Track
- Financial Data and Empirical Workflow
- AI, Text and Alternative Data in Finance
- Discussion of problems in study or research process
- Discussion of the research proposal
Assessment Elements
- pre-defence of Master's thesis
- Защита research memo
- Research ProposalFinal report for students
- Master's thesis discussions in case of student questions
