Methods and Models for Multivariate Data Analysis (Master’s Program: Big Data)
Entry requirements: basic knowledge in field of probability theory and mathematical statistics
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.