Statistics for Applied Informatics

Course guarantor:

Lecturer:

Tutor:

Supervisor:

Faculty of Information Technology

Synopsis:

Students will become familiar with methods of applied statistics and their theoretical foundations. They will learn to work with different types of data, perform analyses, and appropriately select models that describe the data. The course covers regression and correlation analysis, analysis of variance, and an introduction to nonparametric methods. Students will also be introduced to the R statistical environment and will apply the learned methods to real-world data.

Requirements:

Syllabus of lectures:

1. Introduction, basic descriptive statistics

2. Theory of random sample

3. Important distributions, important statistical tests

4. Theory of statistical testing

5. Regression analysis, estimation, evaluation of results

6. Theory of linear regression models, estimation and properties

7. Analysis of variance

8. Advanced theory of estimation

9. Non-parametric methods

10. Likelihood methods

11. Generalized linear models

12. Simulation methods

13. Bootstrap

Syllabus of tutorials:

Study Objective:

Students will become familiar with methods of applied statistics and their theoretical foundations. They will learn to work with different types of data, perform analyses, and appropriately select models that describe the data. The course covers regression and correlation analysis, analysis of variance, and an introduction to nonparametric methods. Students will also be introduced to the R statistical environment and will apply the learned methods to real-world data.

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. O'Reilly 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.

Note:

Further information:

bude doplněno

No time-table has been prepared for this course

The course is a part of the following study plans:

• Master program ANI for the phase of study without specialisation (compulsory course in the program)