Data Visualization

Amidst the information flood in which we are currently immersed, visualizations can be a well-placed treetop. The rise of big data has the potential to inform decisions, and visual representations can play a crucial intermediate role in our daily information consumption. This course is designed to the interdisciplinary and emerging field of data science. It will cover techniques and algorithms for creating effective visualizations based on principles from graphic design, visual art, perceptual psychology, and cognitive science to enhance the understanding of complex data.

Objective

This course particularly pays attention to the applied techniques to data visualization narratives and also takes an interest in data visualization challenges. We will draw on case studies from current hot topics, news media to visualization research. In addition to participating in class discussions, students will have to complete several scripting, data analysis and visualization design assignments as well as a final project. Students will be expected to write up the results of the project in the form of a conference paper submission.

Evaluation

Participation: 15%; Assignments: 50%; Final Project 35%

  • Students will be expected to active participate in class, including class discussion, paper summaries, show & tell through course blog.
  • Students will also be tasked to complete 5 assignments in two categories visualization designs and analyses, from data viz theoretical background, technology and actual implementation. Each assignment should be an individual effort.
  • Final project is expected to integrate all the topics covered in this course. Students will practice collecting data, cleaning data, conducting data analysis,prototyping visualization, implementing visualization, demonstrating the implementation in report and presentation to the entire class / online. It is highly encouraged to incorporate each individual’s thesis. However, if you do not have a specific topic for program thesis yet, you are also encouraged to form a group of 2-3, submit a final project proposal before the first assignment due. The written final project report must follow ACM SIG double columns format (http://www.acm.org/sigs/publications/proceedings-templates). The results will be highly encouraged to submit to relevant conferences.

  • Final Project Requirement

    Final deliverables:

  • 8-12(min-max) pages paper, unlimited extra pages for references. Paper should include: Introduction, Literature review, Motivation, Research questions, Dataset, Visualization Design (implementation), Methodology(Clearly state why&how can your data visualization be used to solve the research questions), Evaluation, Results, Discussion, Future Work, References.

  • your data visualization implementation should also be submitted separately, along with your source codes or executable files. zip all the files together.

  • presentation slides should also be submitted. You will have to present this work (demo/explain it) in class.

  • Evaluation: 20-30% collecting data, cleaning data, conducting data analysis; 45% prototyping visualization, implementing visualization (clarity, consistency, aesthetic, originality); 25% demonstrating the implementation in report and presentation (technically sound?appropriate and sufficient references?) (if it's completed as a group 10% of the average group peer review )

    Strict project submission deadline: May. 13th 6pm