Вычислительные методы статистики

2026/2027

ID 1104347

Статус: Майнор

Кто читает: Международный институт экономики и финансов

Где читается: Международный институт экономики и финансов

Охват аудитории: для своего кампуса

Преподаватели: Купреева Екатерина Витальевна

Язык: английский

Кредиты: 5

Контактные часы: 64

Full Syllabus Ask Question

Abstract

Computational statistics is a one-semester course which focuses on practical aspects and applications of probability theory, statistics and mathematical finance. Course naturally complements corresponding compulsory course «Statistics» for second-year students. The purpose of the course is to apply skills and knowledge students got at the lectures and seminars of Statistics course to real data analysis using programming language.

Learning Objectives

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.
Familiarise students with data analysis methods and tools which are provided in Statistics course.
Show how various statistical methods can be applied together to make comprehensive data analysis, how various topics of statistics are connected and can complement each other.

Expected Learning Outcomes

Use and apply statistical methods for data analysis, research and modelling. This includes ability to select appropriate method/model, check its correctness and applicability, write a program, back test model on historical data and make conclusions.
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

Python revision
Distributions and their properties
Monte-Carlo methods
Applied statistics
Regressions
Bayesian Statistics

Assessment Elements

Class Attendance
Home Assignments
Midterm Test
Final Exam
In order to get a passing grade for the course, the student must sit the exam (all parts of the exam if the exam is divided into parts).

Interim Assessment

2026/2027 2nd module
0.195 * Home Assignments + 0.04 * Class Attendance + 0.265 * Midterm Test + 0.5 * Final Exam

Bibliography

Recommended Core Bibliography

Bayesian data analysis, Gelman, A., 2014
Introduction to probability models, Ross, S. M., 2010
Python for data analysis : data wrangling with pandas, numPy, and IPhython, Mckinney, W., 2017
Statistical inference, Casella, G., 2002

Recommended Additional Bibliography

Derivatives analytics with Python : data analysis, models, simulation, calibration and hedging, Hilpisch, Y. J., 2015

Authors

BORISOV STANISLAV VIKTOROVICH

Course Syllabus