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A course is the basic teaching unit, it's design as a medium for a student to acquire comprehensive knowledge and skills indispensable in the given field. A course guarantor is responsible for the factual content of the course.
For each course, there is a department responsible for the course organisation. A person responsible for timetabling for a given department sets a time schedule of teaching and for each class, s/he assigns an instructor and/or an examiner.
Expected time consumption of the course is expressed by a course attribute extent of teaching. For example, extent = 2 +2 indicates two teaching hours of lectures and two teaching hours of seminar (lab) per week.
At the end of each semester, the course instructor has to evaluate the extent to which a student has acquired the expected knowledge and skills. The type of this evaluation is indicated by the attribute completion. So, a course can be completed by just an assessment ('pouze zápočet'), by a graded assessment ('klasifikovaný zápočet'), or by just an examination ('pouze zkouška') or by an assessment and examination ('zápočet a zkouška') .
The difficulty of a given course is evaluated by the amount of ECTS credits.
The course is in session (cf. teaching is going on) during a semester. Each course is offered either in the winter ('zimní') or summer ('letní') semester of an academic year. Exceptionally, a course might be offered in both semesters.
The subject matter of a course is described in various texts.

BI-VIZ.21 Data Visualization Extent of teaching: 3P
Instructor: Friedjungová M. Completion: KZ
Department: 18105 Credits: 5 Semester: Z

Annotation:
The course offers an overview of the types and characteristics of data as well as suitable visualization methods. This will aid the students in understanding data, their content and their application in areas such as data mining and machine learning. Within the course, students will be introduced to exploratory data analysis, preprocessing, and ways of visualizing different kinds of data such as text, social networks, time series or basic image data processing. Students will get hands-on experience in applications of selected methods to real-world examples in the Python programming language.

Lecture syllabus:
1. Introduction to data visualization, definition, history and motivation.
2. Tools for advanced data manipulation.
3. Basic approaches to data visualization.
4. Basic data analysis methods.
5. Data journalism.
6. [2] Visualization in machine learning and TensorBoard.
8. Image data processing.
9. Graphs and social networks.
10. Visualization in natural language processing.
11. Time series.
12. Advanced data analysis methods.

Seminar syllabus:
Exercises are held together with lectures.
1. Introduction to data visualization, definition, history and motivation.
2. Tools for advanced data manipulation.
3. Basic approaches to data visualization.
4. Basic data analysis methods.
5. Data journalism.
6. [2] Visualization in machine learning and TensorBoard.
8. Image data processing.
9. Graphs and social networks.
10. Visualization in natural language processing.
11. Time series.
12. Advanced data analysis methods.

Literature:
1. Munzner T. : Visualization Analysis & Design. CRC press, 2014. ISBN 9781466508910.
2. Few S. : Information Dashboard Design: The Eective Visual Communication of Data. O'Reilly Media, 2006. ISBN 978-0-596-10016-2.
3. Ward M. O., Grinstein G., Keim D. : Interactive data visualization: foundations, techniques, and applications. A. K. Peters, 2015. ISBN 978-1-4822-5737-3.
4. Yau N. : Visualize this: the FlowingData guide to design, visualization, and statistics. John Wiley & Sons, 2011. ISBN 978-0-470-94488-2.

Requirements:
Basic knowledge of programming (Python).

The course is also part of the following Study plans:
Study Plan Study Branch/Specialization Role Recommended semester
BI-SPOL.21 Unspecified Branch/Specialisation of Study VO 3
BI-SI.21 Software Engineering 2021 (in Czech) V 3
BI-MI.21 Business Informatics 2021 (In Czech) V 3
BI-PG.21 Computer Graphics 2021 (in Czech) V 3
BI-IB.21 Information Security 2021 (in Czech) V 3
BI-TI.21 Computer Science 2021 (in Czech) V 3
BI-WI.21 Web Engineering 2021 (in Czech) V 3
BI-PV.21 Computer Systems and Virtualization 2021 (in Czech) V 3
BI-PS.21 Computer Networks and Internet 2021 (in Czech) V 3
BI-UI.21 Artificial Intelligence 2021 (in Czech) PS 3
BI-PI.21 Computer Engineering 2021 (in Czech) V 3


Page updated 29. 3. 2024, semester: L/2021-2, Z,L/2023-4, Z/2021-2, Z/2020-1, Z/2019-20, L/2020-1, Z,L/2022-3, L/2019-20, Send comments to the content presented here to Administrator of study plans Design and implementation: J. Novák, I. Halaška