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Course Syllabus

Master 2025/2026

Data Analysis

Type: Compulsory course (System and Software Engineering)
Delivered by: School of Software Engineering
When: 2 year, 1, 2 module
Open to: students of one campus
Instructors: Alisa Melikyan
Language: English
Contact hours: 56

Course Syllabus

Full SyllabusAsk Question

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
  • Intro: course contents and administration
  • Data table. Feature modeling. Feature as mapping. Probability feature model.<br /> Categorical data: probability and frequency. Conditional probability; independence; Bayes theorem.<br /> Continuous distribution and density function. Mean and variance. Random sample. Distribution of the sample mean. Central limit theorem.
  • K-Means clustering: method and properties
  • Cluster interpretation: comparison of means, bootstrap for confidence intervals
  • Cluster interpretation at categorical features, Pearson chi-squared, Quetelet indexes
  • Clustering similarity and network data; k-means converted criterion and algorithms
  • Consensus clustering; two criteria; reduction to network clustering
  • Principal component analysis (PCA), Singular value decomposition (SVD), using PCA for data visualization
  • PCA: covariance and correlation matrices, meaning and properties of correlation coefficient in three perspectives; conventional formulation of PCA
  • Spectral clustering
  • Blockchain basics
  • Cryptography basics
  • Smart contracts
  • Deeper into blockchain
  • Private blockchains
Assessment Elements

Assessment Elements

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

Interim Assessment

  • 2025/2026 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

  • MELIKYAN ALISA VALEREVNA
  • Butskaia Evgeniia Aleksandrovna