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
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 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 Python programming language.
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.
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