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BE4M36SAN	Statistical data analysis
Roles:	PO, P	Extent of teaching:	2P+2C
Department:	13136	Language of teaching:	EN
Guarantors:	Kléma J.	Completion:	Z,ZK
Lecturers:	Kléma J.	Credits:	6
Tutors:	Anuarbekov A., Kléma J., Pevný T.	Semester:	Z

Web page:

https://cw.fel.cvut.cz/wiki/courses/B4M36SAN

Anotation:

This course builds on the skills developed in introductory statistics courses. It is practically oriented and gives an introduction to applied statistics. It mainly aims at multivariate statistical analysis and modelling, i.e., the methods that help to understand, interpret, visualize and model potentially high-dimensional data. It can be seen as a purely statistical counterpart to machine learning and data mining courses.

Course outlines:

1.		Introduction, motivation, a course map, review of the basic statistical terms and methods.
2.		Dimension reduction (PCA and kernel PCA).
3.		Dimension reduction (other non-linear methods).
4.		Clustering (basic methods, spectral clustering).
5.		Clustering (biclustering, semi-supervised clustering)
6.		Multivariate confirmation analysis (ANOVA and MANOVA).
7.		Discriminant analysis (categorical dependent variable, LDA, logistic regression).
8.		Multivariate regression (continuous dependent variable, linear regression, p-values, overfitting)
9.		Multivariate regression (non-linear models, polynomial and local regression).
10.		Anomaly detection.
11.		Robust statistics.
12.		Empirical studies, their design and evaluation.
13.		Power analysis.
14.		The final review, spare lecture.

Exercises outline:

1.		Programming in R, introduction.
2.		R libraries, statistical packages, learning package Swirl.
3.		Data visualization in R.
4.		Dimension reduction - assignment.
5.		Clustering - assignment.
6.		Multivariate confirmation analysis - assignment.
7.		Discriminant analysis - assignment.
8.		Mid-term test.
9.		Multivariate linear regression - assignment.
10.		Multivariate non-linear regression - assignment.
11.		Anomaly detection - assignment.
12.		Empirical study design - assignment.
13.		Power analysis - assignment.
14.		Spare lab, credits.

Literature:

1.		Hair, J. F., et al.: Multivariate Data Analysis: A Global Perspective. 7th ed., Prentice Hall, 2009.
2.		James, G. et al.: An Introduction to Statistical Learning with Applications in R., Springer, 2013.

Requirements:

The general statistical concepts covered in the course B0B01PST. The knowledge of linear classification, clustering and dimensionality reduction, see B4B33RPZ for details.

Keywords:

multivariate data analysis multivariate regression clustering dimensionality reduction anomaly detection power analysis

Subject is included into these academic programs:

Program	Branch	Role	Recommended semester
MEBIO2_2018	Medical Instrumentation	P	1
MEBIO1_2018	Bioinformatics	P	1
MEBIO4_2018	Signal Processing	P	1
MEBIO3_2018	Image Processing	P	1
MEOI1_2018	Human-Computer Interaction	PO	3
MEOI2_2018	Cyber Security	PO	1
MEOI8_2018	Bioinformatics	PO	1
MEOI9_2018	Data Science	PO	1

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Page updated 30.12.2024 17:50:55, semester: Z/2025-6, Z,L/2024-5, Send comments about the content to the Administrators of the Academic Programs

Proposal and Realization: I. Halaška (K336), J. Novák (K336)