CS

FF:VIKMB37 Data Visualization - Course Information

VIKMB37 Data Visualization

Faculty of Arts
Autumn 2014
Extent and Intensity
0/2. 4 credit(s). Type of Completion: k (colloquium).
Teacher(s)
Mgr. Jan Boček (lecturer)
Mgr. Tomáš Marek (lecturer)
Mgr. Jan Pospíšil (lecturer)
Mgr. Tomáš Bouda (seminar tutor)
Mgr. Matěj Málek (seminar tutor)
Supervisor
PhDr. Petr Škyřík, Ph.D.
Division of Information and Library Studies - Department of Czech Literature and Library Studies - Faculty of Arts
Contact Person: Mgr. Marie Hradilová
Supplier department: Division of Information and Library Studies - Department of Czech Literature and Library Studies - Faculty of Arts
Timetable
Thu 12:30–14:05 L11
Course Enrolment Limitations
The course is only offered to the students of the study fields the course is directly associated with.

The capacity limit for the course is 20 student(s).
Current registration and enrolment status: enrolled: 0/20, only registered: 1/20
Fields of study the course is directly associated with
Course objectives
Student will be able to: design effective and meaningful visualization, acquire and prepare the data and use basic tools to visualize them, evaluate and discuss a graphical representation of the data in terms of their effectiveness in different contexts. IOn the theoretical level students will be familiar with: the basis of visual perception, design and aesthetics in visualization.
Syllabus
  • 1. Introduction to the course • introduction to data visualization • basic terminology 2. Visualization history • dataviz essential milestones 3. Examples of good and bad practice • dataviz tricks and manipulation 4. The current state of visualization • data journalism, casual infoviz • semester project assignment 5. Visual perception • principles of perception • application of these principles to data visualization • color, design, etc. 6. The effectiveness of data visualization • basic principles of effective visualization • effective basic types of statistical graphs • dataviz and storytelling 7. Process of visualization • overview of the process of creating visualizations • identification of the type of data, forms, audiences, etc. 8. Consultation on semestral project: selection of topic 9. Sources of data • data types and formats • open data • data scraping basics • specifics of data acquisition in Czech environment 10. Data Analysis • overview of basic tools • statistics 11. Data Visualization basics • overview of basic tools 12. Geodata visualization and mapping 13. Comprehensive data visualization • an overview of programming languages for dataviz • R, D3.js and Processing 14. Consultation on semestral project: acquiring and processing data, its visualization
Literature
    recommended literature
  • Jeffrey Stanton. An Introduction to Data Science. Syracuse University, c2012.
  • WONG, Dona M. The Wall Street journal guide to information graphics: the dos and don'ts of presenting data, facts, and figures. 1st ed. New York: W.W. Norton, c2010, 157 p. ISBN 03-930-7295-9.
  • Jonathan Gray, Lucy Chambers, Liliana Bounegru. The Data Journalism Handbook. How Journalists Can Use Data to Improve the News. O'Reilly Media, c2012.
  • Fond Otakara Motejla. Příručka datové žurnalistiky. Nadace Open Society Fund Praha, c2013, ISBN 978-80-87725-10-8. Dostupná z www.osf.cz/publikace/prirucka-datove-zurnalistiky
  • FEW, Stephen. Now you see it: simple visualization techniques for quantitative analysis. Oakland: Analytics Press, c2009, xi, 327 s. ISBN 978-097-0601-988.
  • Scott Murray. Interactive Data Visualization for the Web. O’Reilly Media, c2013. ISBN: 978-1-449-33973-9
  • Paul Bradshaw. Scraping for Journalists. Leanpub, c2013.
  • MONMONIER, Mark S. How to lie with maps. 2nd ed. Chicago: University of Chicago Press, c1996, xiii, 207 p. ISBN 02-265-3421-9
  • CAIRO, Alberto. The functional art: an introduction to information graphics and visualization. San Francisco: Peachpit Press, 2012, 363 pages. ISBN 03-218-3473-9
  • YAU, Nathan. Visualize this: the FlowingData guide to design, visualization, and statistics. Indianapolis, Ind.: Wiley Pub., c2011, xxvi, 358 p. ISBN 11-181-4025-7
  • YAU, Nathan. Data points : visualization that means something. Indianapolis: Wiley, 2013. xiii, 300. ISBN 9781118654934. info
  • TUFTE, Edward R. The visual display of quantitative information. 2nd ed. Cheshire: Graphics Press, 2001. 197 s. ISBN 0-9613921-4-2. info
  • HUFF, Darrell. How to lie with statistics. Illustrated by Irving Geis. New York: Norton, 1993. 142 p. ISBN 0393310728. info
Teaching methods
Seminars, lectures and final project
Assessment methods
Students hand in weekly homeworks, 10 points for each, summing up to 120 points. During the semester, pairs of students work on a practical project for 100 points. They need to complete 70 % = 155 points.
Language of instruction
Czech
Further Comments
Study Materials
Teacher's information
http://www.dataviz.cz
The course is also listed under the following terms Spring 2014, Autumn 2015, Autumn 2016, Autumn 2017.
  • Enrolment Statistics (Autumn 2014, recent)
  • Permalink: https://is.muni.cz/course/phil/autumn2014/VIKMB37