Subjects

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An introduction to statistical practice - NSTP200

Title:	Úvod do statistické praxe
Guaranteed by:	Department of Probability and Mathematical Statistics (32-KPMS)
Faculty:	Faculty of Mathematics and Physics
Actual:	from 2012 to 2012
Semester:	winter
E-Credits:	3
Hours per week, examination:	winter s.:0/2, C [HT]
Capacity:	unlimited
Min. number of students:	unlimited
4EU+:	no
Virtual mobility / capacity:	no
State of the course:	taught
Language:	Czech
Teaching methods:	full-time

Guarantor:	RNDr. Pavel Vaněček, Ph.D. RNDr. Pavel Ranocha, Ph.D.
Teacher(s):	RNDr. Pavel Ranocha, Ph.D. RNDr. Pavel Vaněček, Ph.D.
Classification:	Mathematics > Probability and Statistics

Opinion survey results Examination dates Schedule Noticeboard

Annotation -

Overview of both traditional and modern statistical methods with practical applications. Primer aim is to connect knowledge across various fields and demonstrate the wide range of data-mining techniques by solving some real-world problems, using multivariate statistical analysis or machine learning.

Last update: T_KPMS (25.04.2008)

Aim of the course -

Overview of both traditional and modern statistical methods withpractical applications. Primer aim is to connect knowledge acrossvarious fields and demonstrate the wide range of data-miningtechniques by solving some real-world problems, using multivariate statistical analysis or machine learning.

Last update: G_M (29.05.2008)

Literature - Czech

Berka, P.: Dobývání znalostí z databází. Academia, 2003.

Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer, 2001.

Hebák, P., Hustopecký, J.: Vícerozměrné statistické metody s aplikacemi. SNTL-Alfa, 1987.

Last update: T_KPMS (25.04.2008)

Teaching methods -

Seminar.

Last update: G_M (28.05.2008)

Syllabus -

1. Linear Regression (multicolinearity, correlated residuals, variable importance, interpretation)

2. Factor Analysis (interpretation, visualization)

3. Cluster Analysis (data preparation, overview of methods, description of segments)

4. Classification Algorithms (logistic regression, LDA, decision trees - CART, TreeNet, Random Forest, alternative methods)

5. Latent Classes

6. Correspondence Analysis (contingent tables, Pearson chi-square statistics, multidimensional tables)

7. Multivariate Scaling (problem description, perceptual maps, interpretation)

8. Databases (warehouse, data mart, ETL, OLAP, CRISP-DM)

9. Data Visualization (multivariate and categorical data, special chart types, rules of thumb)

10. Overview of various statistical packages

Case studies from marketing and telecommunication

Other topics according connected to data-mining possible

Last update: T_KPMS (23.05.2008)