2023/2024
Вычислительные методы теории вероятности
Статус:
Дисциплина общефакультетского пула
Кто читает:
Международный институт экономики и финансов
Когда читается:
1-3 модуль
Охват аудитории:
для своего кампуса
Язык:
английский
Кредиты:
3
Контактные часы:
32
Course Syllabus
Abstract
Course description
Computational probability is a one-semester optional course which is taught for ICEF BSc
students. The course focuses on practical aspects and applications of probability theory, statistics
and basics of mathematical finance. Course naturally complements corresponding compulsory
course «Probability theory and Statistics» for first-year students.
The course is taught in English. The students are also studying for Russian degree in
Economics, and knowing Russian terminology through reading in Russian is also required.
Course prerequisites
Students are supposed to be familiar with basic probability theory concepts: probabilities of
events, conditional probability, independence, random variables. There are no prerequisites in
terms of programming language skills – basics of Python programming language will be given at
classes.
Learning Objectives
- The purpose of the course is to apply skills and knowledge students got at the lectures and seminars of «Probability theory and Statistics» course to real data analysis using programming language.
- Learn how to use programming language (Python) to analyse the data.
- Increase understanding of compulsory course topics by considering examples and use cases where various methods can be applied.
- Give introduction to statistics from data analysis point of view.
- Familiarise students with data analysis methods and tools which are provided in compulsory courses.
Expected Learning Outcomes
- Be able to further enhance programming skills by studying advanced techniques which allow to write more effective and professional code meeting international coding standards.
- Be able 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.
Course Contents
- Introduction to Python
- Graphical data representation and descriptive statistics
- Concept of probability
- Simulations
- Continuous random variables. Central limit theorem.
- Binomial model
- Complex probability problems
- SQL basics
Interim Assessment
- 2023/2024 3rd module0.16 * Activity + 0.28 * Final task + 0.28 * Homework 1 + 0.28 * Homework 2
Bibliography
Recommended Core Bibliography
- Python for data analysis : data wrangling with pandas, numPy, and IPhython, Mckinney, W., 2017
- Severance, C. (2016). Python for Everybody : Exploring Data Using Python 3. Place of publication not identified: Severance, Charles. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsotl&AN=edsotl.OTLid0000336
Recommended Additional Bibliography
- Modeling and simulation in python, Kinser, J. M., 2022