Data Visualization
Data is displayed for academic year: 2023./2024.
Laboratory exercises
Course Description
Introduction to data visualisation. The purpose and principles of data visualisation. What, how and why to visualise data. Basic visualisation techniques and tools. Visualisation of univariate, bivariate and multivariate data. Visualisation of temporal, hierarchical, textual and geo-spatial data. Visualisation of linked data, trees, graphs and networks.
Study Programmes
University graduate
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[FER3-HR] Computer Engineering - profile
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[FER3-HR] Control Systems and Robotics - profile
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[FER3-HR] Data Science - profile
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[FER3-HR] Electric Machines, Drives and Automation - profile
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[FER3-HR] Electronic and Computer Engineering - profile
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(1. semester)
(3. semester)
[FER3-HR] Electronics - profile
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(1. semester)
(3. semester)
[FER3-HR] Information and Communication Engineering - profile
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(1. semester)
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[FER3-HR] Network Science - profile
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(1. semester)
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(1. semester)
(3. semester)
[FER3-HR] Software Engineering and Information Systems - profile
Elective Course of the profile
(3. semester)
Elective Course of the Profile
(1. semester)
Elective Courses
(1. semester)
(3. semester)
Learning Outcomes
- explain the basic elements of a data visualization
- propose the design of a data visualisation
- create an effective data visualisation using appropriate tools
- critically assess the design of a data visualisation
Forms of Teaching
Lectures
The classes are organized in two blocks: The first block comprises 7 classes and a midterm exam, while the second comprises 6 classes and a final exam. this makes in total 15 weeks with 2 hours per week.
Independent assignmentsStudents need to resolve independently practical tasks as preparation for laboratory exercises.
LaboratoryDuring the laboratory exercises, students work on cleaning, exploratory analysis and visualization of different types of data using different software frameworks for data visualization.
Week by Week Schedule
- Data and image models
- Graphical perception
- Visual coding
- Color, animation, interaction
- Visualization toolkits
- Multivariate data visualization
- Multivariate data visualization
- Midterm exam
- Temporal data visualization
- Hierarchical data visualization
- Textual data visualization
- Geo-spatial data visualization
- Graph and tree visualizations
- Network and linked data visualization
- Final exam
Literature
Edward R. Tufte (2001.), The visual display of quantitative information, 2nd ed., Graphics
Kristen Sosulski (2018.), Data Visualization Made Simple: Insights Into Becoming Visual, Routledge
Leland Wilkinson (2005.), The grammar of graphics, Springer
Hadley Wickham (2016.), ggplot2: Elegant Graphics for Data Analysis, Springer
Tamara Munzner (2014.), Visualization Analysis and Design, CRC Press
For students
General
ID 223735
Winter semester
5 ECTS
L1 English Level
L1 e-Learning
30 Lectures
0 Seminar
0 Exercises
12 Laboratory exercises
0 Project laboratory
0 Physical education excercises
Grading System
Excellent
Very Good
Good
Sufficient