SDS 136 - Communicating with Data


Course Number SDS 136
Semester Fall 2016
Hours TH 9:00-10:20
Schedule G Block
*New* Location Ford 240

Instructor R. Jordan Crouser
Email jcrouser at smith (dot) edu
Office Ford 344
Office Hours W 10am - noon

Grader Aditya Shastry
Email ashastry at umass (dot) edu

Teaching Assistants Subashini Sridhar & Grace Zhan '18
Email ssridhar || gzhan at smith (dot) edu
Office Hours Th 4pm - 8pm
Burton 209

Discussion: Piazza

Course Description
Schedule
Assignments
Labs
Resources
Grading
Accommodation
Acknowledgement

Course Description

The world is growing increasingly reliant on collecting and analyzing information to help people make decisions. Because of this, the ability to communicate effectively about data is an important component of future job prospects across nearly all disciplines. In this course, students will learn the foundations of information visualization and sharpen their skills in communicating using data. Throughout the semester, we will explore concepts in decision-making, human perception, color theory, and storytelling as they apply to data-driven communication. Whether you're an aspiring data scientist or you just want to learn new ways of presenting information, this course will help you build a strong foundation in how to talk to people about data.

Prerequisite: None.

Assignments and Deliverables

The first half of this course will be focused on building up intuitions around the foundations of information visualization, as well as the relationships between perception and sensemaking. Several (short) assignments will help you get comfortable using the various techniques we discuss in class. In the second half of the course, we'll shift our focus to using these techniques to tell stories with and about data. We'll look at some ways to map the techniques we learned in the first half of the course to real world data. We'll also explore the role of animation and interaction. For the semester's final deliverable, students will apply what they've learned about visualization to a dataset of their choosing. This project will have several (graded) milestones along the way, and we will hold a demonstration session on the final day of class.


In-Class Labs

To help students gain hands-on experience in communicating with data, this course will include 10 in-class lab sessions. The labs will be conducted primarily in Tableau, with some supplemental exercises in other platforms at the instructor's discretion. Students are encouraged to work in pairs during these labs.


Schedule

Date Topic Lab Guest Assignments
09-08 Introduction to Data Visualization
09-13 Visualization Fundamentals pt. 1
09-15 Lab 1: Getting Started w/ Tableau
09-20 Visualization Fundamentals pt. 2 Activity A1 out
09-22 SCMA SPECIAL SESSION - "Critical Looking: Deconstructing Visual Images"
09-27 Lab 2: Bar Charts and Line Charts
09-29 Lab 3: Scatterplots A2 out A1 due
10-04 Perception and Color Mini-lab: Good/Bad Visualizations
10-06 Design Principles Mini-lab: What's Wrong with this Picture? A3 out A2 due
10-11 NO CLASSES - FALL BREAK
10-13 Visualizing Multiple Variables Lab 4: SPLOMs and Parallel Coordinates A4 out A3 due
10-18 Interactive Visualizations Lab 5: Coordinated Multiple Views
10-20 Geographic Data Lab 6: Maps FP1 out A4 due
10-25 SCMA SPECIAL SESSION - Curating A Collection of Visual Media
10-27 SCMA SPECIAL SESSION - The Guerrilla Girls: Artists Mining Data
11-01 Storytelling with Data Lab 7: Tableau Stories FP1 due
11-03 Visualizing Change Lab 8: Animation and Movement
11-08 The Right Tool for the Job Mini-Lab: Developing User Personas FP2 out
11-10 Details Mini Lab: Icons, Images, and Filters in Tableau
11-15 Final Project Workshop 1 FP3 out FP2 due
11-17 Visualization in the Wild Presentations
11-22
11-24 NO CLASSES - THANKSGIVING
11-29 Text as Data Lab 9: Text Visualization FP3 due
12-01 SCMA SPECIAL SESSION - Text/Image: Mining Museum Labels
12-06 Visual Analytics
12-08 Final Project Workshop 2
12-13 Ongoing research
12-15 Final Project Demonstrations

Resources

There are no required textbooks for the course. However, there are several on reserve that you may find helpful.


Recommended Reading
R1 Visualization Design and Analysis (Tamara Munzner) (Amazon)
R2 Visual Thinking for Design (Colin Ware) (Amazon)
R3 Tableau Your Data (Dan Murray) (Amazon)

Grading

Assignments 40%
Labs 30%
Final Project 20%
Class Participation 10%
Total 100%
Note that the final grade is based on my judgment of your work. Although the grade will be largely based on the percentages shown to the left, I will be giving out extra credit for excellent work and out-of-the-box thinking. Similarly, while "class participation" is somewhat subjective and is not one-size-fits-all, I will take note of contributions in class which demonstrate intellectual curiosity or clear understanding of a topic, as well as comments which help others in class to learn a difficult concept.

Late policy: -10% for each day the assignment or final project deliverable is late. Submissions more than 10 days late will not be eligible for credit without notification from the student's dean. Students may request a no-penalty/no-questions-asked extension of 48 hours on any assignment or deliverable. Such requests must be made in writing at least 24 hours in advance of the due date; retroactive requests to extend due dates must be made through the dean.


Accommodation

Smith is committed to providing support services and reasonable accommodations to all students with disabilities. To request an accommodation, please register with the Disability Services Office at the beginning of the semester. To do so, call (413) 585-2071 to arrange an appointment with Laura Rauscher, Director of Disability Services.

Acknowledgement

Some of the materials used in this course are derived from lectures, notes, or similar courses taught elsewhere. Appropriate references will be included on all such material.