Data Preprocessing - N500014
Title: Předzpracování dat
Guaranteed by: CTU in Prague, Faculty of Information Technology (500)
Faculty: University of Chemistry and Technology, Prague
Actual: from 2013 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: 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.
Is interchangeable with: M500004
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: Jirát Jiří (10.01.2014)
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: Jirát Jiří (31.01.2014)
Literature -

R:Pyle, D. ''Data Preparation for Data Mining''. Morgan Kaufmann, 1999. ISBN 1558605290.

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

Last update: Jirát Jiří (10.01.2014)
Learning resources -

(login necessary)

Last update: Jirát Jiří (10.01.2014)
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: Jirát Jiří (10.01.2014)
Registration requirements -

Fundamentals of statistics, FCD course in data mining.

Last update: Jirát Jiří (10.01.2014)
Teaching methods
Activity Credits Hours
Účast na přednáškách 1 28
Práce na individuálním projektu 2.2 61
Účast na seminářích 0.5 14
4 / 4 103 / 112