Статистический анализ данных

Бакалавриат 2025/2026

Статус: Курс по выбору (Международная программа по экономике и финансам)

Кто читает: Международный институт экономики и финансов

Когда читается: 3-й курс, 3, 4 модуль

Охват аудитории: для своего кампуса

Преподаватели: Люлько Ярослав Александрович

Язык: английский

Контактные часы: 64

Full Syllabus Ask Question

Abstract

Statistical data analysis is a one-semester course which is taught for 3rd-year ICEF BSc students at modules 3, 4. The course focuses on practical aspects and applications of probability theory, statistics and mathematical finance. The first part of the course covers classic mathematicalmodels of financial markets, pricing of derivatives via analytical method, partial differentialequations (PDE) and Monte-Carlo simulations. Second part of the course focuses in recent developments and trends such as basics of machine learning, neural networks and blockchain. Inclass activity and problem solving are primarily done by programming in Python, special attention will be paid to developing object-oriented approach (OOP). Problem solving sessions will also be included to work out theoretical material from lectures.

Learning Objectives

Familiarise students with contemporary data analysis methods and tools.
Give introduction to modern areas where statistical analysis can be applied.
Learn how to use programming language (Python) to analyse the data.
Give necessary knowledge to allow students to further study statistical methods which are available at modern software including reading relevant documentation, extra materials (books, articles) and applying new methods of data analysis.
Give necessary coding skills to allow students to further enhance programming skills by studying advanced techniques which allow to write more effective and professional code meeting international coding standards.

Expected Learning Outcomes

Be able to estimate probability density function via kernel methods.
Be able to use splines for discount curve construction.
Understand OOP basic principles: polymorphism, inheritance, encapsulation.
Be able to solve programming problem via OOP, write necessary classes and functionality.
Be able to choose the best design for specific problem, design classes and their inheritance.
Understand how stochastic processes can be used to model prices and returns of financial assets.
Understand Binomial, Bachelier and Black-Scholes models.
Know main methods of financial instruments pricing: analytic, PDE, Monte-Carlo.
Be able to design and develop representation of financial instrument via OOP approach.
Be able to write tests, understand the difference between unit and regression tests.
Know basic machine learning algorithms, how statistical methods can be used in machine learning.
Know common functionality and design features which various APIs share.

Course Contents

Python essentials
Monte-Carlo methods for finance
Calibration of parameters in financial modelling
Models for evolution of prices of financial instruments
Principles of Object-oriented programming (OOP)
Methods of pricing of financial instruments
Comprehensive data analysis
Application public interfaces (API)

Assessment Elements

Activity
Home assignments
Midterm
Final exam
In order to get a passing grade for the course, the student must sit (all parts) of the examination.

Interim Assessment

2025/2026 4th module
0.12 * Activity + 0.45 * Final exam + 0.18 * Home assignments + 0.25 * Midterm

Bibliography

Recommended Core Bibliography

Hull, J. (2017). Fundamentals of Futures and Options Markets, Global Edition (Vol. Eighth edition). Boston: Pearson. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1419711
Monte Carlo methods in financial engineering, Glasserman, P., 2004
Python for data analysis : data wrangling with pandas, numPy, and IPhython, Mckinney, W., 2017

Recommended Additional Bibliography

Stochastic calculus for finance. Vol.1: The binomial asset pricing model, Shreve, S. E., 2004
Stochastic calculus for finance. Vol.2: Continuous-time models, Shreve, S. E., 2004
The elements of statistical learning : data mining, inference, and prediction, Hastie, T., 2017

Authors

Liulko Iaroslav ALEKSANDROVICH

Course Syllabus