PV251 Visualization

Faculty of Informatics
Autumn 2022
Extent and Intensity
2/1. 3 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
Taught in person.
Teacher(s)
doc. RNDr. Barbora Kozlíková, Ph.D. (lecturer)
RNDr. Katarína Furmanová, Ph.D. (lecturer)
RNDr. Jan Byška, Ph.D. (lecturer)
RNDr. Pavol Ulbrich (seminar tutor)
Guaranteed by
doc. RNDr. Barbora Kozlíková, Ph.D.
Department of Visual Computing – Faculty of Informatics
Supplier department: Department of Visual Computing – Faculty of Informatics
Timetable
Wed 10:00–11:50 A217
  • Timetable of Seminar Groups:
PV251/01: Wed 14. 9. to Wed 7. 12. each odd Wednesday 16:00–17:50 A215, K. Furmanová
PV251/02: Wed 21. 9. to Wed 30. 11. each even Wednesday 16:00–17:50 A215, K. Furmanová
PV251/03: Mon 12. 9. to Mon 5. 12. each odd Monday 8:00–9:50 A215, K. Furmanová
PV251/04: Mon 19. 9. to Mon 28. 11. each even Monday 8:00–9:50 A215, K. Furmanová
Prerequisites
No additional prerequisites.
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
The capacity limit for the course is 60 student(s).
Current registration and enrolment status: enrolled: 35/60, only registered: 0/60, only registered with preference (fields directly associated with the programme): 0/60
fields of study / plans the course is directly associated with
there are 46 fields of study the course is directly associated with, display
Course objectives
The goal is to provide students with the overview of the field of visualization and its principles and methods. The course includes basic concepts of visualization and its application to different input data sets. Students also will be acquainted with various interaction techniques for data manipulation and with practical applications of visualization, such as in medicine, art etc. An important part of this course contains practical exercises performed on various visualization tools. At the end of this course, students should be able to design and develop their own effective visualizations.
Learning outcomes
After passing this course, the students will be able to: - evaluate the suitability of existing visualization techniques for a given task - determine the basic mistakes of existing visualization solutions - design appropriate visualizations for given tasks - implement an optimized solution of a selected visualization
Syllabus
  • Introduction, history of visualization, visualization today, human perception and information processing
  • Color, types of input data
  • Visualization foundations
  • Visualization techniques for spatial data
  • Visualization techniques for geospatial data
  • Visualization techniques for multivariate data
  • Graphs and trees, networks
  • Text and document visualization
  • Interaction concepts and techniques
  • Designing effective visualizations, comparing and evaluating visualization techniques
  • Visualization tools and systems
  • Specific applications of visualization - medical visualization, NPR, scientific visualization
Literature
    recommended literature
  • WARD, Matthew, Georges G. GRINSTEIN and Daniel KEIM. Interactive data visualization : foundations, techniques, and applications. Natick: A K Peters, 2010, xvii, 496. ISBN 9781568814735. info
Teaching methods
Theoretical lectures covering fundamentals, methods, and algorithms for visualization. Lab work focused on usage of various visualization tools and design of visualizations. Short HW assignments demonstrating usage of methods discussed in lectures. Study materials: Slides, study materials and lectures video, textbooks, and journals on visualization.
Assessment methods
Homework assignments must be completed before the final examination. The final assessment is based on the result of the written exam which consists of 5 theoretical as well as practical questions.
Language of instruction
English
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
Listed among pre-requisites of other courses
The course is also listed under the following terms Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Spring 2019, Autumn 2019, Autumn 2020, Autumn 2021, Autumn 2023, Autumn 2024.
  • Enrolment Statistics (Autumn 2022, recent)
  • Permalink: https://is.muni.cz/course/fi/autumn2022/PV251