SubjectsSubjects(version: 850)
Course, academic year 2019/2020
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Computational Environment for Statistical Data Analysis - NMST440
Title in English: Výpočetní prostředí pro statistickou analýzu dat
Guaranteed by: Department of Probability and Mathematical Statistics (32-KPMS)
Faculty: Faculty of Mathematics and Physics
Actual: from 2018 to 2019
Semester: summer
E-Credits: 4
Hours per week, examination: summer s.:0/2 C [hours/week]
Capacity: unlimited
Min. number of students: unlimited
State of the course: taught
Language: English, Czech
Teaching methods: full-time
Additional information: http://msekce.karlin.mff.cuni.cz/~komarek/vyuka/nmst440.html
Guarantor: doc. RNDr. Arnošt Komárek, Ph.D.
Class: M Mgr. PMSE
M Mgr. PMSE > Povinně volitelné
Classification: Informatics > Software Applications
Mathematics > Probability and Statistics
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)

To teach students to use effectively advanced tools of R, a free software environment for statistical computation and graphics, related open-source software products and other selected computational tools. To provide an overview on selected commercial products used for statistical data analysis.

Course completion requirements -
Last update: doc. RNDr. Arnošt Komárek, Ph.D. (06.02.2018)

The course credit will be awarded to the student who hands in a satisfactory solution to each assignment by the prescribed deadline. The nature of these requirements precludes any possibility of additional attempts to obtain the exercise class credit.

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

Chambers, J. M. Software for Data Analysis: Programming with R. New York: Springer-Verlag, 2008, xiv + 500 s. ISBN: 978-0-387-75935-7.

Murrell, P. R Graphics, Second Edition. Boca Raton: Chapman & Hall/CRC, 2012, xxvii + 518 s. ISBN: 978-1-4398-3177-9.

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

Seminar + individual work with possibility of advice.

Syllabus -
Last update: doc. RNDr. Arnošt Komárek, Ph.D. (12.04.2018)

1. Basics of html, web presentations.

2. R programming (functions, vectorized operations, ...).

3. Advanced graphics in R (lattice, ggplot2, ...).

4. Automatized reporting using R, systems for automatized reporting and preparation of presentations (Sweave, markdown, shiny).

5. Basics of a programming language C, use for computationally intensive computation when integrated with the R environment.

6. Use of computational clusters for intensive computation.

7. Overview information on selected commercial products for statistical data analysis (SAS, TIBCO Statistica, IBM SPSS).

Entry requirements -
Last update: doc. RNDr. Arnošt Komárek, Ph.D. (25.05.2018)
  • Foundations of statistical inference (statistical test, confidence interval, standard error, consistency);
  • Basic procedures of statistical inference (asymptotic tests on expected value, one- and two-sample t-test, one-way analysis of variance, chi-square test of independence);
  • Linear model;
  • Intermediate knowledge of R, a free software environment for statistical computing and graphics (https://www.r-project.org);
  • Working knowledge of LaTeX;
  • Ability of algorithmic programming (in arbitrary language, e.g., Python, Pascal, C/C++, Fortran, ...).

 
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