SubjectsSubjects(version: 953)
Course, academic year 2019/2020
Data Preprocessing - M500004
Title: Předzpracování dat
Guaranteed by: CTU in Prague, Faculty of Information Technology (500)
Faculty: University of Chemistry and Technology, Prague
Actual: from 2019 to 2019
Semester: winter
Points: winter s.:4
E-Credits: winter s.:4
Examination process: winter s.:
Hours per week, examination: winter s.:2/1, C+Ex [HT]
Capacity: unknown / unknown (unknown)
Min. number of students: unlimited
State of the course: not taught
Language: Czech
Teaching methods: full-time
Teaching methods: full-time
Note: course can be enrolled in outside the study plan
enabled for web enrollment
Guarantor: Jiřina Marcel doc. RNDr. Ing. Ph.D.
Interchangeability : N500014
Examination dates   Schedule   
Annotation -
Students learn to prepare raw data for further processing and analysis. They learn what algorithms can be used to extract parameters from various data sources, such as images, texts, time series, etc., and learn the skills to apply these theoretical concepts to solve a specific problem in individual projects - e.g., parameter extraction from image data or from Internet.
Last update: Hladíková Jana (05.01.2018)
Aim of the course -

Students will be able to:

Apply knowledge of algorithms for extraction of parameters from various data sources as a fundamental part of knowledge engineering,

Last update: Hladíková Jana (05.01.2018)
Literature -

R: Pokorný L. Metody předzpracování dat při získávání znalostí, VUT Brno 2009,

R: Kalina J., Tebbens J. D., Metody pro redukci dimenze v mnohorozměrné statistice a jejich výpočet, Nečasovo centrum matematického modelování MFF UK, Praha 2013,

R: Zheng, A., Casari, A. "Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists", O'Reilly Media, 2018. ISBN 1491953241.

A: Guyon, I., Gunn, S., Nikravesh, M., Zadeh, L. A. "Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)". Springer, 2006. ISBN 3540354875.

A: Pyle, D. "Data Preparation for Data Mining". Morgan Kaufmann, 1999. ISBN 1558605290.

Last update: Svozil Daniel (04.11.2018)
Learning resources -

(login necessary)

Last update: Hladíková Jana (05.01.2018)
Syllabus -

1. Data exploration, exploratory analysis techniques, visualization of raw data.

2. Descriptive statistics.

3. Methods to determine the relevance of features.

4. Problems with data ? dimensionality, noise, outliers, inconsistency, missing values, non-numeric data.

5. Data cleaning, transformation, imputing, discretization, binning.

6. Reduction of data dimension.

7. Reduction of data volume, class balancing.

8. Feature extraction from text.

9. Feature extraction from documents, web. Preprocessing of structured data.

10. Feature extraction from time series.

11. Feature extraction from images.

12. Data preparation case studies.

13. Automation of data preprocessing.

Last update: Hladíková Jana (05.01.2018)
Registration requirements -

Statistical data analysis, Data mining

Last update: Svozil Daniel (08.02.2018)
Course completion requirements - Czech

Pro zı́skánı́ zápočtu je potřeba dostatek bodů ze semestrálnı́ práce a úloh na cvičení. Zkouška se skládá z pı́semné části a nepovinné ústnı́ části.

Last update: Svozil Daniel (07.02.2018)
Teaching methods
Activity Credits Hours
Účast na přednáškách 1 28
Práce na individuálním projektu 1.5 42
Příprava na zkoušku a její absolvování 1 28
Účast na seminářích 0.5 14
4 / 4 112 / 112
Coursework assessment
Form Significance
Report from individual projects 20
Examination test 60
Continuous assessment of study performance and course -credit tests 20