Data visualization - Techniques and tools

Module title:

Data visualization - Techniques and tools

Module overview:

The objectives of this module are to enable students to:
• Interpret the fundamental problems, concepts, and approaches in the design and analysis of data visualization systems.
• Interpret the stages of the visualization pipeline, including data modelling, mapping data attributes to graphical attributes, perceptual issues, existing visualization paradigms, techniques, and tools, and evaluating the effectiveness of visualizations for specific data, task, and user types.
• Explain basics of physiology of the human visual system, perceptual processing, human cognition framework for data visualization
• Interpret characteristics of the most relevant data visualization tools

Students learn visualization techniques, explorative data analysis and predictive modelling. It is a necessary theoretical and practical knowledge and skills for all business areas that are characterized by a large amount of data. Besides the technique, students are introduced to various visualization tools, exploratory data analysis tools and predictive modelling tools.

It is important for students to take this module in order to develop the appropriate and relevant visualization techniques and tools that has evolved into a discipline, drawing from such fields as computer graphics, human-computer interaction, perceptual psychology, and art. The emphasis of the module will be on exposing students to the current research issues and on identifying potential research topics in data visualization as it applies to large-scale data systems.

Literature:

Essential reading:
1. Lecture slides, Zagreb: Algebra University College
2. Ward, M., Grinstein, G. G., and Keim, D. (2015). Interactive Data Visualization Foundations, Techniques, and Applications, Second Edition (Vol. Second edition). Boca Raton: A K Peters/CRC Press, available at http://search.ebscohost.com/login.aspx?direct=trueandsite=eds-liveanddb=edsebkandAN=1763678, Boca Raton, Florida, United States

Recommended reading:
1. Kimball, R., Ross, M., Thornthwaite, W., Mundy, J., Becker, B. (2008) The Data Warehouse Lifecycle Toolkit, 2nd Edition, Hoboken: Wiley
2. Laberge, R. (2011) The Data Warehouse Mentor: Practical Data Warehouse and Business Intelligence Insights, New York: McGraw-Hill Education