Бакалавриат
2024/2025
Статистический анализ данных
Статус:
Курс по выбору (Международная программа по экономике и финансам)
Направление:
38.03.01. Экономика
Кто читает:
Международный институт экономики и финансов
Где читается:
Международный институт экономики и финансов
Когда читается:
3-й курс, 3, 4 модуль
Формат изучения:
без онлайн-курса
Охват аудитории:
для своего кампуса
Преподаватели:
Люлько Ярослав Александрович
Язык:
английский
Кредиты:
4
Контактные часы:
56
Course Syllabus
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 mathematical
models of financial markets, pricing of derivatives via analytical method, partial differential
equations (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
- Calibration of parameters in financial modelling
- Monte-Carlo methods for finance
- Principles of Object-oriented programming (OOP)
- Models for evolution of prices of financial instruments
- Methods of pricing of financial instruments
- Comprehensive data analysis
- Artificial intelligence (AI), basics of neural networks
- Application public interfaces (API)
Interim Assessment
- 2024/2025 4th module0.12 * Activity + 0.4 * Final exam + 0.2 * Homework + 0.28 * 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
- Python for data analysis : data wrangling with pandas, numPy, and IPhython, Mckinney, W., 2017
Recommended Additional Bibliography
- The elements of statistical learning : data mining, inference, and prediction, Hastie, T., 2017