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Lecturer(s)
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Pánek David, doc. Ing. Ph.D.
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Course content
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1. Introduction - Signal Processing and Data Analysis. Importance of Data Preprocessing. 2. Data Sources and Data Formats - Types of Sources: Sensors, Industrial Systems, Logs, API, Web Scraping, Structured, Semi-structured and Unstructured Data. 3. Basic concepts and methods - normalization, standardization, outliers, dimensionality reduction, feature selection, 4. Time series - working with missing values (interpolation, imputation), 5. Time series - moving statistics, decomposition (trend, seasonality), 6. Time series - spectral analysis (FFT, STFT), wavelets, sampling, 7. Image data - pixel resizing and normalization, color spaces (RGB, grayscale), 8. Image data - augmentation (rotation, enlargement, cropping, noise), filtration (blur, Gauss, Median), 9. Image data - histogram equalization, segmentation (thresholding, watershed, CNN-based), 10. Text data - text preprocessing (cleaning, removing word traces, lemmatization), 11. Text data - tokenization, N-grams, TF-IDF, 12. Text data - vector representation of words, contextual models, 13. Signal processing and data analysis in industrial informatics.
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Learning activities and teaching methods
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One-to-One tutorial, Group discussion, Individual study
- Preparation for formative assessments (2-20)
- 10 hours per semester
- Preparation for an examination (30-60)
- 40 hours per semester
- Individual project (40)
- 25 hours per semester
- Contact hours
- 52 hours per semester
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| prerequisite |
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| Knowledge |
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| explain basic statistical concepts such as mean, median, variance, standard deviation and correlation |
| describe basic concepts of linear algebra, especially the concepts of vector, matrix, dot product and orthogonality |
| state the principles of basic data analysis methods such as classification, regression and the meaning of features |
| recognize the meaning of dimensionality reduction (e.g. PCA) and know basic related concepts such as eigenvalue and eigenvector |
| Skills |
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| create and edit simple scripts |
| write a script/program that reads and processes tabular data |
| perform basic data visualizations |
| work with basic data and program structures (lists, dictionaries, loops, functions) |
| load a data file (e.g. CSV), check it and perform basic data cleaning |
| Competences |
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| N/A |
| N/A |
| learning outcomes |
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| Knowledge |
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| explain the importance of data preprocessing in the context of data analysis and modeling |
| describe basic techniques of normalization, standardization and outlier detection |
| distinguish different approaches to dimensionality reduction and feature selection and explain their use |
| characterize the specifics of temporal, image and text data in terms of their structure and appropriate processing methods |
| compare different methods of representing text and images in numerical form |
| describe the basic principles of spectral and time-frequency analysis |
| Explain the importance of data preprocessing in the context of data analysis and modeling. |
| Skills |
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| design appropriate preprocessing procedures for different data types and purposes |
| analyze data quality and perform steps to clean and transform them |
| apply smoothing, interpolation, decomposition and time series transformation techniques |
| perform basic operations with image data such as resizing, filtering and segmentation |
| prepare text data for subsequent processing including tokenization, word reduction and vectorization |
| combine different preprocessing techniques to meet the requirements of a specific task (e.g. classification, prediction, clustering) |
| Competences |
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| N/A |
| N/A |
| N/A |
| teaching methods |
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| Knowledge |
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| Lecture |
| Lecture supplemented with a discussion |
| Skills |
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| Practicum |
| Lecture |
| Competences |
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| Project-based instruction |
| Task-based study method |
| assessment methods |
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| Knowledge |
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| Combined exam |
| Test |
| Skills |
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| Combined exam |
| Practical exam |
| Competences |
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| Combined exam |
| Continuous assessment |
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Recommended literature
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Ahad, Md. Atiqur Rahman; Mahbub, Upal; Turk, Matthew; Hartley, Richard. Computer vision : challenges, trends, and opportunities. First edition. 2025. ISBN 978-1-032-31705-2.
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Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani. An Introduction to Statistical Learning. .
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Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani. An introduction to statistical learning: with applications in R. 2017. ISBN 978-1-4614-7137-0.
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Chris Chatfield. The Analysis of Time Series: An Introduction. New York. 2003.
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Richard Szeliski. Computer Vision: Algorithms and Applications. 2020.
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Simon J.D. Prince. Computer vision: models, learning and inference. 2012.
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Steven Bird, Ewan Klein, and Edward Loper. Natural Language Processing with Python. .
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