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Data Science 3 - NMFP535

Title:	Data Science 3
Guaranteed by:	Department of Probability and Mathematical Statistics (32-KPMS)
Faculty:	Faculty of Mathematics and Physics
Actual:	from 2023
Semester:	winter
E-Credits:	5
Hours per week, examination:	winter s.:2/2, C+Ex [HT]
Capacity:	unlimited
Min. number of students:	unlimited
4EU+:	no
Virtual mobility / capacity:	no
State of the course:	taught
Language:	English, Czech
Teaching methods:	full-time
Teaching methods:	full-time
Is provided by:	NMST539

Guarantor:	doc. RNDr. Zdeněk Hlávka, Ph.D. doc. RNDr. Matúš Maciak, Ph.D. doc. RNDr. Ivan Mizera, CSc.
Class:	M Mgr. FPM M Mgr. FPM > Povinně volitelné
Classification:	Mathematics > Financial and Insurance Math.
Incompatibility :	NMST539
Interchangeability :	NMST539
Is incompatible with:	NMST539
Is pre-requisite for:	NMFP556

Opinion survey results Examination dates WS schedule Noticeboard

Annotation -

Last update: doc. Ing. Marek Omelka, Ph.D. (01.06.2023)

An introduction to traditional and modern methods of multivariate statistics.

Aim of the course -

Last update: doc. RNDr. Zdeněk Hlávka, Ph.D. (08.12.2020)

To acquaint students with basic methods of multivariate statistics.

Course completion requirements -

Last update: doc. RNDr. Ivan Mizera, CSc. (15.10.2023)

Requirements for obtaining the credit (zápočet): participation in the exercises (max 3 absences) and continual solving of the assigned problems (acquiring at least 36 credits, where one solved problem typically amount to one credit). The nature of these requirements precludes any possibility of additional attempts to obtain the class credit. Acquired credit is a condition for attending the examination, which will be in the written form, and apart from simple questions similar to those covered in the exercises will contain also questions regarding principles, motivations, algorithms, and applications of the techniques covered in the lectures.

Literature -

Last update: doc. RNDr. Zdeněk Hlávka, Ph.D. (08.12.2020)

Bouveyron C., Celeux G., Murphy T.B., Raftery A. E.: Model-based Clustering and Classification for Data Science: with Applications in R. Cambridge University Press, 2019.

Härdle, W. K., Hlávka, Z.: Multivariate Statistics: Exercises and Solutions, 2nd edition, Springer, 2015.

Härdle W. K., & Simar L.: Applied Multivariate Statistical Analysis, 4th edition, Springer, 2015.

Mardia K.V., Kent J.T., Bibby J.M.: Multivariate Analysis. Academia Press. London, 1979.

Rao C.R.: Linear Statistical Inference and Its Applications. 2nd edition. Wiley. New York, 1973. (existuje český překlad)

Venables W.N. Ripley B.D.: Modern Applied Statistics with S, 4th edition, Springer, 2002.

Teaching methods -

Last update: RNDr. Jitka Zichová, Dr. (29.05.2022)

Lecture + exercises.

Syllabus -

Last update: doc. RNDr. Martin Branda, Ph.D. (09.12.2020)

1. Multivariate normal distribution.

2. Wishart and Hotelling distribution.

3. Multivariate statistical inference.

4. Principal components and factor analysis.

5. Canonical correlations, correspondence analysis.

6. Discriminant and cluster analysis.

7. Projections-based methods, data depth.

8. Statistical software.