Статистический анализ данных

2021/2022

Статус: Дисциплина общефакультетского пула

Кто читает: Международный институт экономики и финансов

Где читается: Международный институт экономики и финансов

Когда читается: 3, 4 модуль

Охват аудитории: для своего кампуса

Преподаватели: Люлько Ярослав Александрович

Язык: английский

Кредиты: 3

Контактные часы: 32

Full Syllabus

Abstract

Statistical data analysis is a two-semester course and it is taught for first- and second-year ICEF students. The course focuses on practical aspects and applications of probability theory and statistics and naturally complements corresponding compulsory courses for first- and second-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 and statistics at the level of Introduction to Probability Theory and Statistics as well as Calculus courses which are taught in the first year of studies. First-year students are supposed to be familiar with the courses above at the level up to time of the start of present course. Ability to write basic programs in any programming language is a plus but not strongly necessary. The course itself can be considered as complementary to Statistics course for second year students and Introduction to Probability Theory and Statistics for first year students.

Learning Objectives

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 and statistical software. Specifically, the aims of the course are:
• 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 (Stata) 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

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.
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.

Course Contents

Introduction to Python and Stata
Graphical data representation and descriptive statistics.
Point estimates: construction and calculation
Interval estimates (confidence intervals)
Hypothesis testing
Correlation analysis
Pearson goodness of fit (Chi squared) tests
Regression analysis
Comprehensive data analysis

Assessment Elements

Home assignment 1
Home assignment 2
Home assignment 3
Home assignment 4
Final group project

Interim Assessment

2021/2022 4th module
0.15 * Home assignment 4 + 0.4 * Final group project + 0.15 * Home assignment 2 + 0.15 * Home assignment 1 + 0.15 * Home assignment 3

Bibliography

Recommended Core Bibliography

Newbold, P., Carlson, W. L., & Thorne, B. (2013). Statistics for Business and Economics: Global Edition (Vol. Eight edition). Boston, Massachusetts: Pearson Education. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1417883

Recommended Additional Bibliography

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