Terminated in academic year 2022/2023

Data Analysis I

Type of study	Follow-up MasterMaster
Language of instruction	Czech
Code	460-4071/01
Abbreviation	MAD I
Course title	Data Analysis I
Credits	4
Coordinating department	Department of Computer Science
Course coordinator	doc. Mgr. Miloš Kudělka, Ph.D.

Subject syllabus

1. Data for data mining, types and sources of data
2. Attributes and their types, sparse data, incomplete and inaccurate data
3. Algebraic and geometric interpretation of data
4. Probabilistic interpretation of data
5. Numerical and categorial attributes, the basic analytical approaches
6. Data mining, pre-processing and data cleaning
7. Data representation
8. Foundations of data analysis (classification, clustering)
9. Networks and their properties
10. Types of networks and their representation
11. Basic measures and metrics
12. Structure and global properties of networks
13. Basic data structures for network representation
14. Basic algorithms for network analysis

Literature

Presentations of lectures.
Ian H. Witten, Eibe Frank , Mark A. Hall. Data Mining: Practical Machine Learning Tools and Techniques (Third Edition). The Morgan Kaufmann Series in Data Management Systems, 2011. ISBN 978-0123748560.
Zaki, M. J., Meira Jr, W. (2014). Data Mining and Analysis: Fundamental Concepts and Algorithms. Cambridge University Press.
Mark Newman. Networks: An Introduction. Oxford University Press, 2010. ISBN 978-0199206650.

Advised literature

Bramer, M. (2013). Principles of data mining. Springer.
Leskovec, J., Rajaraman, A., Ullman, J. D. (2014). Mining of massive datasets. Cambridge University Press.

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