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B4M36SAN	Statistical Data Analysis
Roles:	PS, PO, P	Extent of teaching:	2P+2C
Department:	13136	Language of teaching:	CS
Guarantors:	Kléma J.	Completion:	Z,ZK
Lecturers:	Kléma J., Pevný T.	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.

Study targets:

Po absolvování předmětu budou studující schopni navrhnout a provést vícerozměrnou analýzu dat včetně návrhu vhodných statistických modelů a jejich srozumitelné interpretace. Bude schopen posoudit účinnost vytvořených modelů a ovládat postupy pro jejich empirické vyhodnocení, včetně validace, testování generalizace a odhadu statistické síly. Rozumí potenciálním problémům modelování, jako je přeučení, multikolinearita, nevyváženost dat či citlivost na odlehlé hodnoty. Získané dovednosti využije jak při výzkumu, tak při analýze komplexních dat v praxi.

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.		Statistical testing, t-test, significance, power of the test.
2.		Simple linear regression.
3.		Shrinked linear regression
4.		Non-linear regression
5.		Discriminant analysis.
6.		Generalized linear models
7.		Mid-term test, the final assignment
8.		Dimension reduction.
9.		Robust statistics
10.		Anomaly detection
11.		Empirical study design, power analysis.
12.		Clustering
13.		Time series analysis.
14.		The final assignment – team presentations.

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
MPOI1_2018	Human-Computer Interaction	PO	3
MPOI9_2018	Data Science	PO	1
MPOI2_2026	Cyber Security	PS	1
MPOI9_2026	Data Science	PS	1
MPOI2_2018	Cyber Security	PO	1
MPOI8_2018	Bioinformatics	PO	1
MPBIO1_2018	Bioinformatics	P	1
MPBIO4_2018	Signal processing	P	1
MPBIO3_2018	Image processing	P	1
MPBIO2_2018	Medical Instrumentation	P	1

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Page updated 14.9.2026 12:56:11, semester: Z/2026-7, L/2029-30, L/2028-9, L/2025-6, Z,L/2027-8, Z/2028-9, L/2026-7, Send comments about the content to the Administrators of the Academic Programs

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