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
[FER3-HR] Audio Technologies and Electroacoustics - profile
Elective Courses (1. semester) (3. semester)
[FER3-HR] Communication and Space Technologies - profile
Elective Courses (1. semester) (3. semester)
[FER3-HR] Computational Modelling in Engineering - profile
Elective Courses (1. semester) (3. semester)
[FER3-HR] Computer Engineering - profile
Elective Course of the Profile (1. semester)
Elective Courses (1. semester) (3. semester)
Elective Courses of the Profile (3. semester)
[FER3-HR] Computer Science - profile
Elective Courses (1. semester) (3. semester)
[FER3-HR] Control Systems and Robotics - profile
Elective Courses (1. semester) (3. semester)
[FER3-HR] Data Science - profile
Elective Courses (1. semester) (3. semester)
Elective Courses of the Profile (1. semester) (3. semester)
[FER3-HR] Electrical Power Engineering - profile
Elective Courses (1. semester) (3. semester)
[FER3-HR] Electric Machines, Drives and Automation - profile
Elective Courses (1. semester) (3. semester)
[FER3-HR] Electronic and Computer Engineering - profile
Elective Courses (1. semester) (3. semester)
[FER3-HR] Electronics - profile
Elective Courses (1. semester) (3. semester)
[FER3-HR] Information and Communication Engineering - profile
Elective Courses (1. semester) (3. semester)
[FER3-HR] Network Science - profile
Elective Courses (1. semester) (3. semester)
Elective Courses of the Profile (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

  1. explain the basic elements of a data visualization
  2. propose the design of a data visualisation
  3. create an effective data visualisation using appropriate tools
  4. 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 assignments

Students need to resolve independently practical tasks as preparation for laboratory exercises.

Laboratory

During 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

  1. Data and image models
  2. Graphical perception
  3. Visual coding
  4. Color, animation, interaction
  5. Visualization toolkits
  6. Multivariate data visualization
  7. Multivariate data visualization
  8. Midterm exam
  9. Temporal data visualization
  10. Hierarchical data visualization
  11. Textual data visualization
  12. Geo-spatial data visualization
  13. Graph and tree visualizations
  14. Network and linked data visualization
  15. 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

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Good
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