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Information Visualization for Doctoral Students

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PhD Course on Information Visualization, 7,5 Hp


This 7,5 Hp course is aimed for Computer Science and Communication PhD students aiming to understand Information Visualization theory and practice, including development tools, to forward their ability to present the data emerging from their research. It will teach students how to think systematically about data models, visual structures, and view transformations. The aim is to provide the students with the tools to present their research through interactive visualizations, improving their ability to produce figures and presentations of their research. The course will have a reading and discussion forum, it will review past projects, and it will allow the students to develop and constructively critizise visualizations for their own research, using their own data.The work towards interactive visualizations will be individual and it will be based on the actual research data of the PhD students.

Credits: 7,5 Hp

Grading: Pass/Fail

To pass, students must read the assigned papers, build interactive visualization systems using existing packages, develop new visualization systems using visualization libraries, prepare three presentations (one literature review and two live interactive visualization demos), participate in the discussions, and provide each other critical constructive feedback.

Intended Learning Outcomes

The students will be able to:

  1. Understand the Visualization Pipeline applied to their own research data
  2. Use state-of-the-art visualization packages to meet their general visual analytics needs
  3. Develop visualization tools using state-of-the-art development packages to meet their specific visual analytics needs
  4. Communicate their own research through improved printed figures on papers, interactive visual structures online, and live during conference talks

Schedule (Period 3 - 4: January - June)

  1. Meeting 1: The Visualization Pipeline, Data Types, Tasks (4 hours)
    • Discuss the readings:
      1. Information Visualization, in Handbook of Human Factors and Ergonomics, ch43 - Chris North –  pdf (through KTH BIB)
      2. The Eyes Have It – Ben Schneiderman – pdf
      3. Low-Level Components of Analytic Activity in Information Visualization – Robert Amar, James Eagan, and John Stasko – pdf
      4. A knowledge task-based framework for design and evaluation of information visualizations – Bob Amar and John Stasko – pdf
  2. Meeting 2: Students' research data, data models, visualization examples (4 hours)
    • Students present a visual literature review of their own field
  3. Meeting 3: Data models, Data Mappings, Visual Structures, View Transformations (4 hours)
    1. Workshop where students build interactive visual structures from their own research data using state-of-the-art visualization packages (QlickView, Spotfire, Tableau, MS Power BI, Others)
  4. Meeting 4: Presentations of systems from Meeting 3 (4 hours)
    1. Students present the resulting interactive visualizations from the previous workshop. They present live interactive demos and static paper-style figures in slide format
  5. Meeting 5: Development in D3.js (4 hours)
    1. Workshop where students develop tailor-made interactive visual structures from their own research data using Data Driven Documents, a javascript library for rapid interactive information visualization prototyping online.
    2. Suggested Readings:
      1. Interactive Data Visualization for the Web, Scott Murray - Online.
      2.  D3 Tips and Tricks v3.x, Malcolm Mclean - Online.
  6. Meeting 6: Presentations of systems from Meeting 5 (4 hours)
    1. Students present the resulting interactive visualizations from the previous workshop. They present live interactive demos online and static paper-style figures in slide format
  7. Meeting 7: Discussing Evaluation and Conclusding Remarks (4 hours)
    1. Students discuss the readings:
      1. Plaisant, Catherine. "The challenge of information visualization evaluation."Proceedings of the working conference on Advanced visual interfaces. ACM, 2004. LINK
      2. Shneiderman, Ben, and Catherine Plaisant. "Strategies for evaluating information visualization tools: multi-dimensional in-depth long-term case studies." Proceedings of the 2006 AVI workshop on BEyond time and errors: novel evaluation methods for information visualization. ACM, 2006. LINK