This page is an archive. Visit Data visualization - Winter 2017

Data Visualization - Winter 2016

Instructor: Amit Chourasia, San Diego Supercomputer Center, UCSD

Teaching assistant: Thomas Andy Keller, Computer Science and Engineering, UCSD

Textbook Visualization Analysis and Design, Tamara Munzner (A K Peters Visualization Series, CRC Press, 2014)

Day 1 Jan 8 , Day 2 Jan 22 , Day 3 Feb 4 , Day 4 Feb 19 , Day 5 Mar 4 , Day 6 - Finals Mar 18

Schedule

Day 1 (Jan 8)

Download All Day 1 Lectures

Morning

Afternoon

Home work

  • Readings: Chapter 4
  • Exercise 2
  • Develop final project ideas: identify dataset, create tasks

Day 1 - Supplement (Jan 9 AM)

Day 2 (Jan 22)

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Morning

  • Guest Lecture - Applying Color Theory to Visualization. Thersa-Marie Rhyne, Computer Graphics and Visualization Consultant. Slides (PDF)

    Abstract: We examine the foundation of color theory and how these methods apply to building effective visualizations. We define color harmony and demonstrate the application of color harmony to case studies. The material presented is from upcoming book on “Applying Color Theory to Digital Media and Visualization” to be published by Taylor & Francis in 2017.

  • Colors suppliment - Slides (PDF)
  • Split activity
  • Cognition videos (skipped)
  • Tables - Slides (PDF)

Afternoon

  • Network and Trees - Slides (PDF)
  • Tools demo / tutorial - Bokeh (TA) (Download Demo Files)

  • 3:30 pm Guest lecture - Autonomous Systems Introduction and Visualization. Bradley Huffaker, Technical manager, CAIDA, San Diego Supercomputer Center, UCSD Sliders (PDF)

    Abstract: Topology maps of the Internet are indispensable for characterizing this critical infrastructure and understanding its properties, dynamics, and evolution. Most Internet mapping methods have focused on characterizing and modeling the network structure at the level of interconnected Autonomous Systems (ASes). In this talk we will introduce various ways to annotate ASes, go over available datasets, and present visualizations using that data.

Home work

Day 2 - Supplement (Jan 23 AM)

Day 3 (Feb 5)

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Morning

Afternoon

  • Student presentations : Final project proposal (5 min presentation, followed by 2 min discussion and change over)

  • 3:30pm Guest lecture - Dimensionality Reduction From Several Angles. Dr. Tamara Munzner, Professor, Department of Computer Science, Univ. of British Columbia. Slides (PDF)

    Abstract: I will present several projects that attack the problem of dimensionality reduction (DR) in visualization. Much of this work was informed by a two-year qualitative study of high-dimensional data analysts in many domains, to encapsulate the use of DR “in the wild” as a small set of abstract tasks. We used different methodological angles of attack in order to answer different kinds of questions, according to our Nested Model of visualization design and evaluation. First, can we design better DR algorithms? Glimmer is a multilevel multidimensional scaling (MDS) algorithm that exploits the GPU. Glint is a new MDS framework that achieves high performance on costly distance functions. Second, can we build a DR system for real people? DimStiller is a toolkit for DR that provides local and global guidance to users who may not be experts in the mathematics of high-dimensional data analysis, in hopes of “DR for the rest of us”. Third, how should we show people DR results? An empirical lab study provides guidance on visual encoding for system developers, showing that points are more effective than spatialized landscapes for visual search tasks with DR data. A data study, where a small number of people make judgements about a large number of datasets rather than vice versa as with a typical user study, produced a taxonomy of visual cluster separation factors. Fourth, when do people need to use DR? Sometimes it is not the right solution, as we found when grappling with the design of the QuestVis system for a environmental sustainability simulation. We provide guidance for researchers and practitioners engaged in this kind of problem-driven visualization work with a nine-stage framework for Design Study Methodology.

Home work

Day 4 (Feb 19)

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Morning

Afternoon

  • Student presentations : Case study - Presentation order
  • Student presentations : Case study - Presentation order

  • 3:30pm Guest lecture - Exploratory data analysis and visual analytics for public health. Dr. Nadir Weibel, Research Assistant Professor, Department of Computer Science and Engineering, UCSD. Slides (PDF)

    Abstract: Common data visualization techniques are a great tool to illustrate the results of scientific inquiries and history showed us how effective these visuals are in order to uncover specific questions. In the setting of public health this has been demonstrated many times from John Snow’s visualization of Cholera’s outbreak in London, to Florence Nightingale’s plots of mortality in the UK army, to many others. However, those techniques often only allow for analysis of a limited number of dimensions in terms of the available data, resulting in the reduction of the problem space to a single perspective, with outcomes quantifying the single variables rather then the relationship between them. Moreover, these methods require the definition of a clear a-priori model for analysis that often is hard to develop when many variables are at stake.

    In this lecture I will focus on an alternative approach based on the concept of Exploratory Data Analysis (EDA). As suggested by the mathematician and statistician John Tukey, often too much emphasis in statistics is placed on statistical hypothesis testing and more emphasis needs to be placed on using data to suggest hypotheses to test. I will use two use cases that build on my current research supporting HIV prevention to illustrate how the use of powerful interactive data visualization techniques can help uncover new important facts and potentially drive real-world data-driven interventions.

Home work

Day 5 (Mar 4)

Morning

Afternoon

  • 1:15pm Guest lecture - High performance visualization Dr. David Nadeau, Sr. Scientist, San Diego Supercomputer Center, UCSD. Slides (PDF)

    Abstract: Traditional plots work well to show detail and short trends when data is small. But as data grows larger, plot visual complexity and drawing times increase. New visual designs are needed to clearly show complex data, and new high performance techniques are needed to draw visualizations quickly. This talk introduces issues in high performance visualization, GPUs, OpenGL, and WebGL, and illustrates them using large 3D graph and volume visualizations.

  • Exercise 8 - Design visualizations

  • 3:30pm Guest lecture - Visual Analysis of Big Personal Health Data Dr. Jurgen Schulze, Associate Research Scientist, Qualcomm Institute, UCSD. Slides (PDF) Abstract: Modern analysis methods allow people to collect unprecedented amounts of information about the status of their health. While daily weight and body fat measurements are feasible for almost everyone, fewer people have their blood analyzed for biomarkers on a regular basis, or even their DNA and their microbiome sequenced. At Calit2, we have the luxury to work with one of the world’s most quantified individuals, its director Larry Smarr, who made available to us the medical data he collected about himself so that we could research and prototype visual and computational analysis tools. This presentation is going to report on our quest of distilling information such as patterns and relations out of Dr. Smarr’s data, in comparison with available data from hundreds of other people. We created a visual analysis tool for the 64 million pixel tiled display wall at the Qualcomm Institute, a system driven by 17 high end graphics PCs and networked with 10 Gbps.

Home work

Day 6 - Final exam (Mar 18)

Student presentations : Final presentations 20 minute presentation followed by 5 minute Q&A Presentation order

Course Grading

Since this is small class grading will be on absolute scale. - A (Excellent 4.0) >= 90% - B (Good 3.0) >= 80% - C (Fair 2.0) >= 70% - D (Barely passing 1.0) >= 60% - F (Fail) < 60%

Grade calculation will be as follows

Guest Lecturers

  1. VR Lab Tour - Dr. Jurgen Schulze, Associate Research Scientist, Qualcomm Institute, UCSD
  2. Applying Color Theory to Visualization - Thersa-Marie Rhyne, Computer Graphics and Visualization Consultant
  3. Network analysis - Bradley Huffaker, Technical manager, CAIDA, San Diego Supercomputer Center, UCSD
  4. Dimensionality Reduction From Several Angles - Dr. Tamara Munzner, Professor, Department of Computer Science, Univ. of British Columbia
  5. Exploratory data analysis and visual analytics for public health - Dr. Nadir Weibel, Research Assistant Professor, Department of Computer Science and Engineering, UCSD
  6. High performance visualization - Dr. David Nadeau, Sr. Scientist, San Diego Supercomputer Center, UCSD
  7. Visual Analysis of Big Personal Health Data - Dr. Jurgen Schulze, Associate Research Scientist, Qualcomm Institute, UCSD