Data Analysis and Data Physicalization is a research seminar that explores the analysis and communication of data through statistical analysis and physical artefacts.
Data can bring clarity and insights into otherwise chaotic problems and phenomena around us yet at the same time, can be deceiving or blinding without the knowledge to handle them skilfully. This skill is often referred to as data literacy that is the ability for one to carry out statistical analysis and to appreciate and critique information made available by others. Along with data literacy, the aptitude for communication with data is becoming ever more important. Data visualization is a discipline that aims to augment human understanding of data. The primary challenge of visualization design is to develop techniques that transform abstract data into representations that are easy to perceive and interpret. Successful data visualization using graphs, charts, timelines, and diagrams are extremely helpful in prompting visceral comprehension of data, nevertheless, many of the two-dimensional representations of complex data are difficult to be felt and digested. Physicalization of data is one of the approaches to extend the cognition and communication of data.
In this class, we use python, commonly used in the data science community to understand the fundamentals of data analysis. Though advanced data analysis methods are beyond the scope of the class, we will cover basic concepts and techniques through a series of exercises and assignments. Simultaneously, we will explore approaches to materialize the information obtained from data analysis. Students will work on a final design project combining data analysis and data physicalization approaches introduced in the class.
Up to five seats will be held for MDes students, with priortiy given to Mediums Domain and Technology Area students.
This course will be taught online through Friday, February 4th.