Skip to main content
Skip header

ECTS Course Overview



Data Analysis I

* Exchange students do not have to consider this information when selecting suitable courses for an exchange stay.

Course Unit Code460-4071/02
Number of ECTS Credits Allocated4 ECTS credits
Type of Course Unit *Optional
Level of Course Unit *Second Cycle
Year of Study *
Semester when the Course Unit is deliveredWinter Semester
Mode of DeliveryFace-to-face
Language of InstructionCzech, English
Prerequisites and Co-Requisites Course succeeds to compulsory courses of previous semester
Name of Lecturer(s)Personal IDName
OH140RNDr. Eliška Ochodková, Ph.D.
KUD007doc. Mgr. Miloš Kudělka, Ph.D.
Summary
The course is focused on basic approaches, methods, and algorithms for data mining and network analysis so that it can be applied to the individual work of students in labs. Exercises will provide space for discussion of problems, demonstration of practical tasks and practice on simple assignments.
Learning Outcomes of the Course Unit
The course provides basic information about methods used for data mining and network analysis. Students will gain knowledge and skills necessary for further development in this area and the ability to apply them to simple problems. They will be able to assess the applicability of methods for different types of data and evaluate the outcomes of the application of the used methods.
Course Contents
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
Recommended or Required Reading
Required Reading:
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.
Prezentace k přednáškám.
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.

Recommended Reading:
Bramer, M. (2013). Principles of data mining. Springer.
Leskovec, J., Rajaraman, A., Ullman, J. D. (2014). Mining of massive datasets. Cambridge University Press.
Bramer, M. (2013). Principles of data mining. Springer.
Leskovec, J., Rajaraman, A., Ullman, J. D. (2014). Mining of massive datasets. Cambridge University Press.
Planned learning activities and teaching methods
Lectures, Tutorials
Assesment methods and criteria
Tasks are not Defined