230-0263/01 – Data analysis methods (MAD)

Gurantor departmentDepartment of MathematicsCredits10
Subject guarantordoc. Ing. Martin Čermák, Ph.D.Subject version guarantordoc. Ing. Martin Čermák, Ph.D.
Study levelpostgraduateRequirementChoice-compulsory type B
YearSemesterwinter + summer
Study languageCzech
Year of introduction2020/2021Year of cancellation
Intended for the facultiesHGFIntended for study typesDoctoral
Instruction secured by
LoginNameTuitorTeacher giving lectures
CER365 doc. Ing. Martin Čermák, Ph.D.
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Examination 20+0
Part-time Examination 20+0

Subject aims expressed by acquired skills and competences

The aim of the subject is to introduce students to statistical analysis to the extent needed for processing measurements, data sets, and time series. After the successful completion of the course, students will be able to formulate questions which can be answered using data and, in order to do so, will become familiar with the principles of data collection, data processing, and relevant data presentation. Students will also learn to practically analyse time series using commonly used methods choosing the most suitable one for efficient analysis. Moreover, students will acquire skills needed for design and evaluation of inferences and predictions using data as well as assess the model suitability in the context of data processed.

Teaching methods

Lectures
Individual consultations

Summary

The aim of the subject is to introduce students to statistical analysis to the extent needed for processing measurements, data sets, and time series. After the successful completion of the course, students will be able to formulate questions which can be answered using data and, in order to do so, will become familiar with the principles of data collection, data processing, and relevant data presentation. Students will also learn to practically analyse time series using commonly used methods choosing the most suitable one for efficient analysis. Moreover, students will acquire skills needed for design and evaluation of inferences and predictions using data as well as assess the model suitability in the context of data processed.

Compulsory literature:

BRIŠ, R. Probability and statistics for engineers. VŠB-TU Ostrava, 2011. https://homel.vsb.cz/~bri10/Teaching/Prob%20&%20Stat.pdf SHUMWAY, R. H., STOFFER, D. S. Time Series Analysis and Its Applications: With R Examples. Springer, 4th ed. 2017. ISBN10 3319524518

Recommended literature:

ŠKŇOUŘILOVÁ, P., BRIŠ, R. Statistics I, VŠB-TU Ostrava, Ostrava 2007. http://mdg.vsb.cz/portal/en/Statistics1.pdf MARTINEZ, W. L. Exploratory data analysis with MATLAB. Boca Raton, Fla.: Champman&Hall/CRC, c2005. ISBN 1-58488-366-9 KANTZ, H., SCHREIBER, T. Nonlinear Time Series Analysis, Cambridge University Press. 2nd ed. 2004. ISBN10 0521529026

Way of continuous check of knowledge in the course of semester

ústní zkouška

E-learning

oral exam

Other requirements

Control tests, semestral project, consultations, oral exam.

Prerequisities

Subject has no prerequisities.

Co-requisities

Subject has no co-requisities.

Subject syllabus:

Fundamentals of Statistics, Linear algebra Optimization methods, Mathematical analysis Data processing software: R, Matlab, Excel, etc. Regression models (polynomial, autoregressive), regularization Bayesian statistics, Markov chains Spectral analysis: PCA and SVD Data reduction methods: regularized K-means clustering Time series – basic terms, graphical analysis Time series – descriptive characteristics, dynamics Model properties analysis

Conditions for subject completion

Part-time form (validity from: 2020/2021 Winter semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of points
Examination Examination  
Mandatory attendence parzicipation:

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Occurrence in study plans

Academic yearProgrammeField of studySpec.ZaměřeníFormStudy language Tut. centreYearWSType of duty
2022/2023 (P0724D290003) Environmental Protection within Industry K Czech Ostrava Choice-compulsory type B study plan
2022/2023 (P0724D290003) Environmental Protection within Industry P Czech Ostrava Choice-compulsory type B study plan
2021/2022 (P0724D290003) Environmental Protection within Industry K Czech Ostrava Choice-compulsory type B study plan
2021/2022 (P0724D290003) Environmental Protection within Industry P Czech Ostrava Choice-compulsory type B study plan

Occurrence in special blocks

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