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Visualizing Data

CSSS 569

Good visual displays uncover patterns quantitative scientists might otherwise miss, and can make or break a paper. This course takes the design of graphics and tables seriously, and surveys a variety of visual techniques for exploring data and summarizing statistical models. Emphasis on principles of effective visualization, novel visual displays, examples from the social sciences, and implementation of recommended techniques in R.

CSSS 569

Visualizing Data

Offered every Winter at the
University of Washington

Syllabus  

Readings  



Winter 2016

Class meets:
TTh 4:30-5:50 pm
Loew Hall 102

Lectures           PDFs of slides are best viewed in Adobe Acrobat, rather than in your browser.

Short Course

Visualizing Model Inference and Robustness

   

This is a 9-hour short course version of the full Data Visualization course; the lectures for the full term course are below. Students taking the short course will also need these additional resources:

Topic 1

Course Introduction

   

Topic 2

Principles for the Visual Display of Scientific Information

   

Topic 3

Cognitive Issues in Visualization

   

Topic 4

Graphical Programming in R

   

We will examine two R scripts: a script using the base R graphics to show confidence intervals around a regression line, and another script using grid R graphics to accomplish the same task. A third, more advanced grid graphics script replaces ticks with gridlines. All three scripts require this dataset.

Interested students can find detailed instructions for downloading, installing, and learning my recommended software for quantitative social science here. Focus on steps 1.1 and 1.3 for now, and then, optionally, step 1.2.

Topic 5

Exploratory Data Analysis: Between Data & Model

   

Topic 6

Visualizing Inference

   

Download instructions for the tile package can be found under the Software tab at left. We will discuss three examples in detail:

Gallery 1

Scales and Storytelling

   

Gallery 2

Maps as Visual Displays of Information

   

Gallery 3

Time Series as Narrative

   

Gallery 4

Grayscale Images of Continuous Data

   

Gallery 5

Turning Tables into Graphs

   

Gallery 6

Heatmaps for Visualizing Continuous Dyadic Data

   

Gallery 7

Ternary Plots for Compositional Data Analysis

   


Student Assignments

Problem Set 1

Due in class 19 January 2016

Supplementary material: You will need these data.

Problem Set 2

Due in class 9 February 2016

Problem Set 3

Due in class 11 March 2016

Breakout Group

Individual memo due before group meets; Group memo due by 22 February

Students will join a small group to discuss a visual display problem of common interest; creation and organization of these groups to be coordinated through the web. Students will write a 1-2 page memo before the first group meeting, and each group will write a 5+ page essay for the class on what they have learned, to be distributed by 22 February. Groups will answer questions from the class during the week of 22 February. See the syllabus for further details.

Final Poster

Presented during the final five classes

On an assigned day during the last two or three weeks of the course, each student will present a poster applying the tools learned in class to their own research. Alternatively, students can take an article published in their field and show how better visuals would either more clearly convey the findings or cast doubt on them. The final presentation may address problems raised in the breakout session or problem sets, but it is usually more fruitful for students to tackle a new problem.



University of Washington link

CSSS Center for Statistics and the Social Sciences link

Design:
Chris Adolph & Erika Steiskal

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