index.md

DSCI 532: Data Visualization II

This module continues with analysis, design, and implementation considerations for creating interactive visualizations including multiple linked views, in contrast to the focus of DSCI 531 (Data Visualization I) on the creation of single static figures.

Slack channel: https://ubc-mds.slack.com/messages/532_viz-2

Schedule

Summary

  • Lectures: 11am-12:20pm Tue/Thu, Jan 2 - 25 2018, DMP 301
  • Labs: 2-4pm Thu, Jan 4-25, ESB 1042
  • Instructor office hours: Wed 5-6, Jan 3-24, ICICS/CS X661

Lecture Schedule

Lect Date Day Topic
1 2018-01-02 Tue Introduction and interaction design: reactivity, responsiveness, and selection.
2 2018-01-04 Thu Interactive navigation: panning, zooming, and other changes of viewpoint.
3 2018-01-09 Tue Multiple view coordination: juxtaposing, partitioning and layering.
4 2018-01-11 Thu Data reduction: filtering and aggregation of items.
5 2018-01-16 Tue Perceptual principles, color theory.
6 2018-01-18 Thu Rules of thumb, usability testing.
7 2018-01-23 Tue Design and justification exercises.
8 2018-01-25 Thu Beyond R.

Lab Schedule

Lab Date Topics Material
1 Thu Jan 4 2018 Overview of assessments; Introduction to shiny tutorial html, tutorial md
2 Thu Jan 11 2018 shiny tutorial: intermediate tutorial html, tutorial md
3 Thu Jan 18 2018 shiny tutorial: advanced tutorial html, tutorial md
4 Thu Jan 25 2018 Peer feedback session instructions html, instructions md

Assessments

This is a project-based course involving four assessments.

Week Assessment Weight Due
1 html, 1 md Lab Assignment 1 20% Sunday January 7, 2018, at 15:00
2 html, 2 md Project Milestone 1: Proposal 10% Sunday January 14, 2018, at 15:00
3 html, 3 md Project Milestone 2: First submission 30% Sunday January 21, 2018, at 15:00
4 html, 4 md Project Milestone 3: Final submission 40% Sunday January 28, 2018, at 15:00

Solutions

Teaching Team

Position Name Slack Handle GHE Handle
Lecture Instructor Tamara Munzner @tmm @munzner
Lab Instructor Vincenzo Coia @vcoia @vcoia
Lab Instructor Tiffany Timbers @tiffany @timberst
Teaching Assistant Sam Hinshaw @samhinshaw @hinshaws
Teaching Assistant Ana Crisan @Ana Crisan @acrisan
Teaching Assistant Vaden Masrani @vaden @vadmas

Reference Material

Learning Outcomes

By the end of the course, students are expected to be able to:

  1. Analyze interactive visualizations in terms of approaches to handling complexity: dynamic change over time, partitioning into multiple views, data reduction within a single view, and the derivation of new data.
  2. Design new interactive visualizations for complex datasets.
  3. Implement interactive visualizations using existing toolkits and libraries.
  4. Explain the trade-offs of using animation vs juxtaposed views vs derived data.
  5. Explain and justify methods to validate visualization design effectiveness including computational benchmarks, field studies on deployed software, and qualitative discussion of visual results.

Lecture Learning Objectives

  1. Interaction design: reactivity, responsiveness, and selection.

By the end of the lecture, students are expected to be able to:

  • Explain costs and benefits of interactively changing views.
  • Reason about the design choices involved in selection and highlighting.
  • Characterize human response to latency into categories relevant to the design of responsive software.
  1. Interactive navigation: panning, zooming, and other changes of viewpoint.

By the end of the lecture, students are expected to be able to:

  • Explain the tradeoffs between constrained and unconstrained navigation.
  • Explain the tradeoffs between slicing, cutting, and projection.
  1. Multiple view coordination: juxtaposing, partitioning between views, and visual layering.

By the end of the lecture, students are expected to be able to:

  • Analyze the use of multiple views in terms of shared versus different visual encoding and data shown.
  • Discuss the tradeoffs between a single view that changes over time and multiple linked views.
  • Explain the costs and benefits of different view coordination strategies.
  • Reason about the order in which to select attributes to partition a dataset into multiple views according to the target user's task.
  • Analyze the scalability of superimposed layers for static and dynamic layering
  1. Data reduction: filtering and aggregation.

By the end of the lecture, students are expected to be able to:

  • Discuss the tradeoffs of filtering vs aggregation for reducing data.
  • Analyze static aggregation techniques in terms of what data was derived to support them
  • Analyze dynamic aggregation techniques in terms of derived data and interactive selection.
  1. Perceptual principles.

By the end of the lecture, students are expected to be able to:

  • Discuss the perceptual issues involved in decisions about aesthetics and geoms.
  • Analyze visual encodings according to whether the combination of channels is integral or separable.
  • Apply the principles of expressiveness and effectiveness to choose appropriate visual channels for specific data types.
  • Distinguish between contexts where visual popout will occur versus those where serial search must be used to find specific items.
  1. Colour theory.

By the end of the lecture, students are expected to be able to:

  • Analyze the use of colour in terms of luminance, saturation, and hue.
  • Choose effective strategies to accommodate colour-blind users.
  • Explain the discriminability limits on the use of categorical colours and discuss appropriate alternative designs.
  • Explain the drawbacks of rainbow colourmaps for ordered data and discuss appropriate alternative designs.
  1. Rules of Thumb, Usability Testing.

By the end of the lecture, students are expected to be able to:

  • Discuss the costs and benefits of 3D visual representations for both spatial and nonspatial data
  • Apply basic graphic design principles about spatial layout.
  • Discuss the tradeoffs between resolution and immersion.
  • Conduct basic usability testing.
  1. Design & Justification Exercises.

By the end of the lecture, students are expected to be able to:

  • Synthesize across and reason about tradeoffs between the four major approaches to handling complexity.
  • Explicitly justify design choices in terms of all visualization theory concepts previously covered in module.