2022/2023
Statistical Data Analysis
Type:
Optional course (faculty)
Delivered by:
International College of Economics and Finance
When:
1, 2 module
Open to:
students of one campus
Instructors:
Yaroslav Lyulko
Language:
English
ECTS credits:
3
Contact hours:
32
Course Syllabus
Abstract
Statistical data analysis is a one 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
- 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.
Interim Assessment
- 2022/2023 2nd module0.28 * Home assignment 2 + 0.16 * Activity in the class + 0.28 * Final project + 0.28 * Home assignment 1
