HS19: Data Visualization Concepts (BINF4234)
Interactive visual data analysis is becoming increasingly critical to the modern day information technology world. In this course we will cover the fundamental concepts of interactive data visualization, including brief reviews of important preliminary or complementary techniques such as digitization, color models, visual perception as well as data analysis techniques. The aim is to learn the fundamental principles and techniques of interactive data visualization, and in particular identify and categorize the different visualization techniques available for the various types of data to be displayed.
This module primarily focuses on visual data representation and basic visualization concepts. The lecture is targeted to students with an assessment level BSc in computer science or similar basic knowledge of computer science, programming, data structures, algorithms and math. It is recommended for BSc students in the 3rd or higher semester.
Tentative list of topics to be covered (book [x] chapter)
|Introduction and history, digitization  Ch.1|
|Color and perception  Ch.3 &  Ch.4|
|Quantitative data visualization  Ch.12|
|Data and visualization foundations  Ch.2, 4|
|Spatial data visualization  Ch.5, 6 &  Ch.14, 15|
|Multivariate data visualization  Ch. 7|
|Trees, graphs and network visualization  Ch.8|
|(Interaction  Ch.10, 11)|
Main course textbooks:
 Interactive Data Visualization: Foundations, Techniques and Aplications by Ward, Grinstein and Keim, AK Peters, 2010.
 Mathematical Principles for Scientific Computing and Visualization by Farin and Hansford, AK Peters, 2008
Selected book chapters from:
 Information Visualization: Perception for Design by Colin Ware, Morgan Kaufmann, 2013.
As a standing homework assignment you are expected to review the corresponding book chapters matching the lectures.
As a standing homework assignment you are expected to read the corresponding book chapters before the lectures and to review the material thoroughly after the lectures covering them.
To complete the lecture, students must also complete any exercises given in class or distributed on OLAT. Programming projects must be completed and submitted exactly in the appropriate form as indicated in the exercise requirements via OLAT to the assistant leading the exercises.
The lecture will be completed with a written exam at the end of the semester. The exam is scheduled according to the standard UZH/OEC/IFI regulations. See also the course catalogue link at the top of the page.
- Python: https://www.python.org/
- Anaconda (open data science platform with Python IDE): https://www.continuum.io
- HoloViews (Python interactive visualization library): http://holoviews.org/index.html
- Bokeh (Python interactive visualization library): http://bokeh.pydata.org/en/latest/
- VisPy (Python interactive visualization library): http://vispy.org/index.html
- PIL (Python Imaging Library): http://www.pythonware.com/products/pil/
- Seaborn (Python visualization library): http://stanford.edu/~mwaskom/software/seaborn/
- Matplotlib (Python 2D plotting library): http://matplotlib.org/index.html
- Pygal (Python charting library): http://www.pygal.org/en/stable/
- Pandas (Python data analysis library): http://pandas.pydata.org/