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

Objectives

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

Contents

-        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.)

Format

Lectures, seminars, practical classes.

Assessment

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.