Data Analysis

2025/2026

Academic Year

ENG

Instruction in English

6

ECTS credits

Delivered by:

School of Software Engineering

Type:

Mago-Lego

When:

1, 2 module

Instructor

Melikyan, Alisa

Course Syllabus

Abstract

Students will study modern methods of data analysis and will acquire practical skills in using Python programming language for data manipulation and analysis. At the end of the course students will be able to carry out preliminary preparation of data, choose an appropriate method of data analysis depending on the type of data and the research task, conduct quantitative data analysis and interpret the obtained results.

Learning Objectives

• give students an introduction to the most widely used data analysis methods
• explain the data analysis methods using real data and concentrating on complications that may occur during the analysis in real-life research
• teach students how to organize their own research project using the knowledge obtained during the course
• explain how to use data analysis tools in the most effective way to perform the research tasks

Expected Learning Outcomes

• create a cluster model and describe it
• create a factor model and describe it
• create a regression model and describe it
• formulate research hypotheses and construct models
• prepare empirical data for their further analysis
• select appropriate methods of data analysis depending on the research question and types of empirical data

Course Contents

• Introduction to data analysis
• Descriptive data analysis
• Investigating relationships between variables
• Regression analysis
• Principal component analysis
• Cluster analysis
• Panel data analysis
• Time series analysis
• Web-scrapping

Assessment Elements

• Control Work 1
• Practical Tasks
• Exam
  Written work done on a computer.
• Home Project
• Control Work 2

Interim Assessment

• 2025/2026 2nd module
  0.3 * Exam + 0.15 * Control Work 1 + 0.2 * Home Project + 0.15 * Control Work 2 + 0.2 * Practical Tasks

Bibliography

Recommended Core Bibliography

• Core concepts in data analysis: summarization, correlation and visualization, Mirkin, B., 2011
• Introduction to econometrics, Dougherty, C., 2016
• McKinney, W. (2012). Python for Data Analysis : Data Wrangling with Pandas, NumPy, and IPython. Sebastopol, CA: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=495822

Recommended Additional Bibliography

• Idris, I. (2016). Python Data Analysis Cookbook. Birmingham, UK: Packt Publishing. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1290098
• Lutz, M. (2006). Programming Python (Vol. 3rd ed). Sebastopol, CA: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=415084
• 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

Authors

• MELIKYAN ALISA VALEREVNA
• Butskaia Evgeniia Aleksandrovna