Notice that the schedule is under construction and subject to change before the first day of class.

Course Introduction – Visual Data Analysis

Jan. 6: Course introduction, Project Description, Expectations, Group Presentation Description

Jan. 8: Overview of InfoVis & Visual Analytics

  • “Information Visualization for Scientific Discovery”, Miriah Meyer, 2011 video
  • “Why Visual Analytics”, NVAC, 2007 video
  • “Visual Analytics: Mastering the Digital Age”, VisMaster, 2010. video

Jan. 13: Overview of InfoVis & Visual Analytics

  • “Precision Information Environments” video
  • “Semantic Interaction for Sensemaking”, Alex Endert, TEDxVirginiaTech, 2012 video

Jan. 15: (rest of Overview / Defining Insight)

  • Chang, R., Ziemkiewicz, C., Green, T.M. and Ribarsky, W., 2009. Defining insight for visual analytics. IEEE Computer Graphics and Applications29(2), pp.14-17.   PDF

Jan. 20: (no lecture / MLK Holiday)


Visual Analytics: Data Analytics + User Interaction + Vis

Jan. 22: Sensemaking

  • Discussion leaders: Group -2 (Endert)
  • Pirolli, Peter, and Stuart Card. “The sensemaking process and leverage points for analyst technology as identified through cognitive task analysis.” Proceedings of International Conference on Intelligence Analysis. Vol. 5. McLean, VA: Mitre, 2005. PDF

Jan. 27: Coupling Computation with Cognition & Promoting “Flow”

  • Discussion leaders: Group -1 (Endert)
  • Elmqvist, N., Moere, A. V., Jetter, H. C., Cernea, D., Reiterer, H., & Jankun-Kelly, T. J. (2011). Fluid interaction for information visualization. Information Visualization10(4), 327-340. PDF
  • Discussion leaders: Group -1 (Endert)
  • Endert, A., Chang, R., North, C. and Zhou, M., 2015. Semantic interaction: Coupling cognition and computation through usable interactive analytics. IEEE Computer Graphics and Applications35(4), pp.94-99.   PDF

Jan. 29: Analytic Provenance Overview

  • Discussion leaders: Group 0 (Endert)
  • Ragan, E.D., Endert, A., Sanyal, J. and Chen, J., 2015. Characterizing provenance in visualization and data analysis: an organizational framework of provenance types and purposes. IEEE transactions on visualization and computer graphics22(1), pp.31-40.  PDF

Feb. 3: no lecture — Project Part 1 workday

Feb. 5 : Analysis Aids and Techniques

  • Discussion leaders: Group 1
  • Heuer, Psychology of Intelligence Analysis Chapter 1 & 8 PDF

Feb. 10: Knowledge Generation with Interactive Visual Analytics

  • Discussion leaders: Group 2
  • Sacha, Dominik, et al. “Knowledge generation model for visual analytics.” IEEE transactions on visualization and computer graphics 20.12 (2014): 1604-1613.   PDF

Feb. 12: In-class activity

Capturing and Visualizing the Analysis Process; Analytic Provenance

Feb. 17: Capturing Interaction

  • Discussion leaders: Group 3
  • Cowley, Paula, Lucy Nowell, and Jean Scholtz. “Glass box: An instrumented infrastructure for supporting human interaction with information.” System Sciences, 2005. HICSS’05. Proceedings of the 38th Annual Hawaii International Conference on. IEEE, 2005. PDF
  • DUE: HW1 (submit to Canvas before the start of class)

Feb. 19: How well do people remember their analysis process?

  • Discussion leaders: Group 4
  • Lipford, Heather Richter, et al. “Helping users recall their reasoning process.” Visual Analytics Science and Technology (VAST), 2010 IEEE Symposium on. IEEE, 2010. PDF

Feb. 24: Tracking the analysis process

  • Discussion leaders: Group 5
  • Lee, Eric, et al. “The CZSaw Notes Case Study. PDF

Feb. 26: Tracking and visualizing the process

  • Discussion leaders: Group 6
  • Silva, Cláudio T., et al. “Using vistrails and provenance for teaching scientific visualization.” Computer Graphics Forum. Vol. 30. No. 1. Blackwell Publishing Ltd, 2011. PDF

Mar. 2: Can we predict how well people will perform on their task? 

  • Discussion leaders: Group 7
  • Brown, E.T., Ottley, A., Zhao, H., Lin, Q., Souvenir, R., Endert, A. and Chang, R., 2014. Finding waldo: Learning about users from their interactions. IEEE Transactions on visualization and computer graphics20(12), pp.1663-1672. PDF

Mar. 4: Predictive Interaction

  • Discussion leaders: Group 8
  • Heer J, Hellerstein JM, Kandel S. Predictive Interaction for Data Transformation. In CIDR 2015. PDF


Project Presentations (student project group presentations)

DUE: (this week) Project Part 1 for all teams

Mar. 9

  • Part 1 presentations day 1 (link to sign up posted to Canvas)

Mar. 11

  • Part 1 prsentations day 2 (link to sign up posted to Canvas)

Mar. 16 – no class (spring break)

Mar. 18 – no class (spring break)


Interactive Model Steering

Mar. 23

  • Discussion leaders: Group 9
  • Brown, Eli T., et al. “Dis-function: Learning distance functions interactively.” Visual Analytics Science and Technology (VAST), 2012 IEEE Conference on. IEEE, 2012. PDF
  • DUE: HW2 (submit to Canvas before the start of class)

Mar. 25

  • Discussion leaders: Group 10
  • Kim, H., Choo, J., Park, H. and Endert, A., 2016. InterAxis: Steering scatterplot axes via observation-level interaction. IEEE transactions on visualization and computer graphics22(1), pp.131-140. PDF

Mar. 30

  • Discussion leaders: Group 11
  • Gratzl, S., Lex, A., Gehlenborg, N., Pfister, H. and Streit, M., 2013. Lineup: Visual analysis of multi-attribute rankings. IEEE transactions on visualization and computer graphics19(12), pp.2277-2286. PDF

Apr. 1

  • Discussion leaders: Group 12
  • Wall, E., Das, S., Chawla, R., Kalidindi, B., Brown, E.T. and Endert, A., 2018. Podium: Ranking data using mixed-initiative visual analytics. IEEE transactions on visualization and computer graphics24(1), pp.288-297. PDF

Studying analysts and the analytic process; Cognitive Models and Bias

Apr. 6

  • Discussion leaders: Group 13
  • Kandel, Sean, et al. “Enterprise data analysis and visualization: An interview study.” Visualization and Computer Graphics, IEEE Transactions on 18.12 (2012): 2917-2926. PDF

Apr. 8

  • Discussion leaders: Group 14
  • Pohl, Margit, Michael Smuc, and Eva Mayr. “The User Puzzle: Explaining the Interaction with Visual Analytics Systems.” Visualization and Computer Graphics, IEEE Transactions on 18.12 (2012): 2908-2916. PDF
  • DUE: HW3 (submit to Canvas before the start of class)

Apr. 13

  • Discussion leaders: Group 15
  • Wall, E., Blaha, L.M., Paul, C.L., Cook, K. and Endert, A., 2017. Four perspectives on human bias in visual analytics. In DECISIVe: Workshop on Dealing with Cognitive Biases in Visualizations. PDF


Project Presentations

Due: Project Part 2 (for all teams). Upload report to Canvas by Apr. 16. Signup link posted to Canvas.

Apr. 15 — Project Part 2 presentations

Apr. 20 — Project Part 2 presentations