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Computational Environment for Statistical Data Analysis - NMST440

Title:	Výpočetní prostředí pro statistickou analýzu dat
Guaranteed by:	Department of Probability and Mathematical Statistics (32-KPMS)
Faculty:	Faculty of Mathematics and Physics
Actual:	from 2014 to 2014
Semester:	summer
E-Credits:	5
Hours per week, examination:	summer s.:2/2, C+Ex [HT]
Capacity:	unlimited
Min. number of students:	unlimited
4EU+:	no
Virtual mobility / capacity:	no
State of the course:	not taught
Language:	English
Teaching methods:	full-time
Teaching methods:	full-time

Guarantor:	doc. RNDr. Arnošt Komárek, Ph.D.
Class:	M Mgr. PMSE M Mgr. PMSE > Povinně volitelné
Classification:	Informatics > Software Applications Mathematics > Probability and Statistics
Incompatibility :	NSTP004
Interchangeability :	NSTP004
Is interchangeable with:	NSTP004

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Annotation -

Last update: T_KPMS (30.05.2016)

Advanced aspects of R, a free software environment for statistical computation and graphics, basics of html, basics of C programming, use of computational clusters for extensive computation, overview on commercial products for statistical data analysis.

Aim of the course -

Last update: doc. RNDr. Arnošt Komárek, Ph.D. (19.05.2016)

Detailed introduction to software suitable for statistical data analysis and presentation.

Literature - Czech

Last update: doc. RNDr. Arnošt Komárek, Ph.D. (19.05.2016)

Manuály k probíranému software

Teaching methods -

Last update: doc. RNDr. Arnošt Komárek, Ph.D. (19.05.2016)

Lecture+exercises.

Syllabus -

Last update: doc. RNDr. Arnošt Komárek, Ph.D. (19.05.2016)

1. HTML and some information sources on internet (JSTOR, Kluwer Online, Wiley Interscience, ScienceDirect, SpringerLink, Zentralblatt, MathSciNet), LaTeX, BibTeX, makeindex, graphics.

2. The R system: data management. LATeX and HTML output, graphics, programming and simulations, functions and libraries.

3. Sweave for combining R and LaTeX, using C for R programming, usage of computational cluster.

4. Simple data management using scripts and programs sed and awk.

5. Selected computationally intensive methods: EM algorithm, bootstrap etc.

6. Data analysis using SAS.