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CZECH TECHNICAL UNIVERSITY IN PRAGUE
STUDY PLANS
2024/2025
Faculties | School elective courses | ECTS courses catalogue | Settings | Česká verze
Welcome page > Faculty of Information Technology > Department of Applied Mathematics > Practical Statistics
NOTICE: Study plans for the following academic year are available.

Practical Statistics

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Code Completion Credits Range Language
BI-PRS.21 KZ 5 1P+2C Czech
Course guarantor:
Petr Novák
Lecturer:
Kamil Dedecius, Petr Novák
Tutor:
Kamil Dedecius, Petr Novák
Supervisor:
Department of Applied Mathematics
Synopsis:

The students will be introduced to methods of applied statistics. They will learn how to work with various types of data, perform analyses, and choose models fitting the data. The course will encompass regression and correlation analysis, analysis of variance and non-parametric methods. Students will learn to use the statistical software R and will apply the studied methods on data from real problems.

Requirements:

Basics of probability and statistics, mathematical analysis and linear algebra

Syllabus of lectures:

1. Introduction to statistical analysis and the R language ecosystem.

2. Basic descriptive statistics, visualization of data - tables and plots.

3. Statistical tests, comparison of multiple data sets.

4. Non-parametric methods.

5. Regression analysis, estimation, evaluation of results.

6. Regression analysis with categorical variables.

7. Advanced regression models, parameter estimation, evaluation.

8. Basic methods of outlier detection.

9. Model selection, selection criteriaa.

10. Analysis of variance.

11. Multiple comparisons.

12. Analysis of categorical data.

13. R and LaTeX.

Syllabus of tutorials:

1. Introduction to statistical analysis and the R language ecosystem.

2. Basic descriptive statistics, visualization of data - tables and plots.

3. Statistical tests, comparison of multiple data sets.

4. Non-parametric methods.

5. Regression analysis, estimation, evaluation of results.

6. Regression analysis with categorical variables.

7. Advanced regression models, parameter estimation, evaluation.

8. Basic methods of outlier detection.

9. Model selection, selection criteriaa.

10. Analysis of variance.

11. Multiple comparisons.

12. Analysis of categorical data.

13. R and LaTeX.

Study Objective:
Study materials:

1. Ahn H. : Probability and Statistics for Science and Engineering with Examples in R. Cognella, 2017. ISBN 978-1516513987.

2. Bruce P., Bruce A. : Practical Statistics for Data Scientists: 50 Essential Concepts. OReilly Media, 2017. ISBN 978-1491952962.

3. Venables W. N., Smith D. M. : An Introduction to R. R Foundation for Statistical Computing, 2009. ISBN 978-0954612085.

4. Chambers J. M. : Software for Data Analysis: Programming with R. Springer, 2008. ISBN 978-0-387-75935-7.

5. Anděl J. : Základy matematické statistiky. Matfyzpress, 2011. ISBN 978-80-7378-162-0.

Note:

The course is presented in Czech.

Further information:
https://courses.fit.cvut.cz/BI-PRS
Time-table for winter semester 2024/2025:
Time-table is not available yet
Time-table for summer semester 2024/2025:
06:00–08:0008:00–10:0010:00–12:0012:00–14:0014:00–16:0016:00–18:0018:00–20:0020:00–22:0022:00–24:00
Mon
Tue
roomJP:B-671
Novák P.
Dedecius K.
09:15–10:45
EVEN WEEK
(lecture parallel1)
Jugoslávských partyzánů 3 roomT9:349
Novák P.
Dedecius K.
12:45–14:15
(lecture parallel1
parallel nr.101)
Dejvice roomT9:349
Novák P.
Dedecius K.
14:30–16:00
(lecture parallel1
parallel nr.102)
Dejvice roomTH:A-1442
Novák P.
Dedecius K.
16:15–17:45
(lecture parallel1
parallel nr.103)
Thákurova 7 (budova FSv)
Wed
Thu
Fri
The course is a part of the following study plans:
Data valid to 2025-05-08
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