230-0263/01 – Data analysis methods (MAD)
Gurantor department | Department of Mathematics | Credits | 10 |
Subject guarantor | doc. Ing. Martin Čermák, Ph.D. | Subject version guarantor | doc. Ing. Martin Čermák, Ph.D. |
Study level | postgraduate | Requirement | Choice-compulsory type B |
Year | | Semester | winter + summer |
| | Study language | Czech |
Year of introduction | 2020/2021 | Year of cancellation | |
Intended for the faculties | HGF | Intended for study types | Doctoral |
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:
Additional study materials
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
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction
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