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Course Syllabus

2024/2025

Data Analysis

Type: Mago-Lego
Delivered by: School of Software Engineering
When: 1, 2 module
Open to: students of one campus
Instructors: Alisa Melikyan
Language: English
ECTS credits: 6

Course Syllabus

Full 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

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

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

Course Contents

  • Introduction to data analysis
  • Descriptive data analysis
  • Investigating relationships between variables
  • Regression analysis
  • Factor analysis
  • Cluster analysis
  • Panel data analysis
  • Time series analysis
Assessment Elements

Assessment Elements

  • non-blocking Exam
    Written work done on a computer.
  • non-blocking Home Project
  • non-blocking Practical Tasks
  • non-blocking Control Work 1
  • non-blocking Control Work 2
Interim Assessment

Interim Assessment

  • 2024/2025 2nd module
    0.15 * Control Work 1 + 0.15 * Control Work 2 + 0.3 * Exam + 0.2 * Home Project + 0.2 * Practical Tasks
Bibliography

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

  • Melikian ALISA VALEREVNA
  • Butskaia Evgeniia Aleksandrovna