Вычислительные методы статистики

2023/2024

Статус: Дисциплина общефакультетского пула

Кто читает: Международный институт экономики и финансов

Где читается: Международный институт экономики и финансов

Когда читается: 1, 2 модуль

Охват аудитории: для своего кампуса

Преподаватели: Люлько Ярослав Александрович

Язык: английский

Кредиты: 3

Контактные часы: 32

Full Syllabus

Abstract

Computational statistics is a one-semester course which is taught for ICEF BSc students. The course 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

Familiarise students with data analysis methods and tools which are provided in statistics courses.
Increase understanding of compulsory courses topics by considering examples and use cases where various methods can be applied.
Learn how to use statistical software and programming language (Python) to analyse the data.
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 study statistical methods which are available at modern software including reading relevant documentation, extra materials (books, articles) and applying new methods of data analysis.
Be able to further enhance programming skills by studying advanced techniques which allow to write more effective and professional code meeting international coding standards.

Course Contents

Python revision
Simulations
Datasets validations
Point estimates: construction and calculation
Interval estimates (confidence intervals)
Hypothesis testing
Pearson goodness of fit (Chi squared) tests
Regression analysis
Analysis of returns
Comprehensive data analysis

Assessment Elements

Homework 1
Homework 2
Final task
Activity

Interim Assessment

2023/2024 2nd module
0.16 * 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

Recommended Additional Bibliography

Derivatives analytics with Python : data analysis, models, simulation, calibration and hedging, Hilpisch, Y. J., 2015
McKinney, W. (2018). Python for Data Analysis : Data Wrangling with Pandas, NumPy, and IPython (Vol. Second edition). Sebastopol, CA: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1605925

Course Syllabus