Methods and Models for Multivariate Data Analysis (Master’s Program: Big Data)

Entry requirements: basic knowledge in field of probability theory and mathematical statistics

Credits: 4

Semester: 2

Course: Core

Language of the course: English


  • to improve backgrounds in probability theory
  • to develop skills in probabilistic modelling and statistic assessment


-        Probabilistic models for random variables. Univariate random variable. (CDF and PDF. Probability distribution parameters estimation. Probabilistic interval. Confidence interval. Tolerant interval.)

-        Probabilistic models for random variables. Multivariate random variable. (Regression and correlation analysis. Linear and non-linear regression. Canonical correlations. Principal component analysis. Empirical orthogonal functions. Factor analysis. ANOVA method. Cluster analysis. Multidimentional interval estimates. Nonlinear methods for dimensionality reduction.)


Lectures, seminars, practical classes.


Grading: 30% participation in class discussions and/or individual presentation on a topic of interest, 60% results of practical tasks; 10% results of tests.

Additional opportunity to improve scores during the exam.