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CZECH TECHNICAL UNIVERSITY IN PRAGUE
STUDY PLANS
2024/2025
Faculties | School elective courses | ECTS courses catalogue | Settings | Česká verze
Welcome page > Faculty of Information Technology > Department of Applied Mathematics > Data Visualization

Data Visualization

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Code Completion Credits Range Language
BI-VIZ.21 KZ 5 3P Czech
Course guarantor:
Magda Friedjungová
Lecturer:
Magda Friedjungová
Tutor:
Magda Friedjungová
Supervisor:
Department of Applied Mathematics
Synopsis:

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.

Requirements:

Basic knowledge of programming (Python).

Syllabus of 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.

Syllabus of tutorials:

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.

Study Objective:

The goal of the course is to introduce students to the area of data visualization, its basic principles and methods. Practical examples of different visualization methods in Python are also shown during the lectures. The course focuses on the application of knowledge in data mining and machine learning.

Study materials:

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.

Note:
Time-table for winter semester 2024/2025:
06:00–08:0008:00–10:0010:00–12:0012:00–14:0014:00–16:0016:00–18:0018:00–20:0020:00–22:0022:00–24:00
Mon
Tue
roomJP:B-671
Friedjungová M.
07:30–09:00
EVEN WEEK
(lecture parallel1)
Jugoslávských partyzánů 3 roomJP:B-671
Friedjungová M.
09:15–10:45
ODD WEEK
(lecture parallel1)
Jugoslávských partyzánů 3
roomJP:B-671
Friedjungová M.
09:15–10:45
EVEN WEEK
(lecture parallel1
parallel nr.101)
Jugoslávských partyzánů 3
Wed
Thu
Fri
Time-table for summer semester 2024/2025:
Time-table is not available yet
The course is a part of the following study plans:
Data valid to 2024-11-21
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