Multivariate Data Analysis
Data is displayed for academic year: 2023./2024.
Lecturers
Associate Lecturers
Course Description
Multivariate data analysis forms one of the basic pillars of data science and is a generalization of univariate and bivariate statistical methods. Multivariate analysis is intended for simultaneous analysis and visualization of complex datasets with a large number of independent and/or dependent variables that are in different degrees of correlation, and their various effects cannot be interpreted separately. The contents of the course are grouped into three sections. The first part contains the basic concepts and basic techniques that precede the multivariate analysis, the second part relates to various advanced regression techniques and their understanding (with reference to high-dimensional data), and the third to techniques based on matrix decompositions (separation by eigenvalues and separation by singular values).
Study Programmes
University graduate
[FER3-EN] Data Science - profile
(2. semester)
Learning Outcomes
- Define main notions in the multivariate data analysis
- Explain mathematical backgrounds of main multivariate statistical procedures
- Apply linear multiple regression analysis
- Differentiate between principal component analysis and factor analysis
- Justify the adequacy of different multivariate statistical methods for various problems
- Interpret the results of multivariate statistical data analysis and explain their practical meaning
Forms of Teaching
Lectures
Lectures are given for 13 weeks in two two-hour sessions per week.
ExercisesAuditory exercises consist of solving practical examples and problems, and are integrated in the lecture sessions.
LaboratoryProgramming assignments, demonstrated to the instructor or teaching assistant.
Grading Method
Continuous Assessment | Exam | |||||
---|---|---|---|---|---|---|
Type | Threshold | Percent of Grade | Threshold | Percent of Grade | ||
Laboratory Exercises | 50 % | 30 % | 50 % | 30 % | ||
Mid Term Exam: Written | 0 % | 35 % | 0 % | |||
Final Exam: Written | 0 % | 35 % | ||||
Exam: Written | 50 % | 70 % |
Comment:
The passing threshold is 50% of the total sum of points in the midterm and final exams.
Week by Week Schedule
- Introductory concepts, statistical distance, sample geometry and random sampling
- Random vectors and matrices, matrix decomposition, eigenvalues
- Multivariate normal distribution
- Statistical inference about vector means
- Principal component analysis
- Exploratory factor analysis
- Multivariate linear regression and canonical correlation analysis
- Midterm exam
- Discriminant analysis
- Clustering and distance methods
- Correspondence analysis
- Survival analysis
- Time series analysis
- The Lasso method for high dimensional data
- Final exam
Literature
For students
General
ID 222937
Summer semester
5 ECTS
L1 English Level
L1 e-Learning
45 Lectures
0 Seminar
15 Exercises
6 Laboratory exercises
0 Project laboratory
Grading System
89 Excellent
76 Very Good
63 Good
50 Sufficient