COMP 150-04 (VAN) Topics in Visual Analytics

Course Number COMP150-04
Semester Spring, 2014
Hours MW 6-7:15
Schedule M+ Block
Location Halligan 102

Instructor R. Jordan Crouser
email jordan (dot) crouser at
gmail (dot) com
Office Halligan E005
Office Hours By appointment

Discussion: Piazza

Course Description

Course Description

Visual analytics is the science of combining interactive visual interfaces and information visualization techniques with automatic algorithms to support analytical reasoning through human-computer interaction. People use visual analytics tools and techniques to synthesize information and derive insight from massive, dynamic, ambiguous, and often conflicting data, and to communicate their findings effectively for decision-making. This course will serve as an introduction to the science and technology of visual analytics and will include lectures on both theoretical foundations and application methodologies. The goals of this course are for students to (1) develop a comprehensive understanding of this emerging, multidisciplinary field, and (2) apply that understanding toward a focused research problem in a real-world application or a domain of personal interest.

Prerequisite: Prerequisite: COMP 15; Some experience with user interface development would also be helpful but not required (e.g., COMP 106, COMP 175, COMP 150-VIS)


Date Topic Tutorial/Demo Guest Speaker Assignments Notes
01-14-15 Course Overview
01-19-15 NO CLASSES - Martin Luther King Day
01-21-15 Mental and Visualization Models
02-02-15 CANCELLED DUE TO SNOW Data Wrangling w/ Python A1 out iPython Notebook | PDF
02-04-15 Data Collection and Models Introduction to R Maja Milosevjevic, MITLL R Markdown File | PDF
02-09-15 CANCELLED DUE TO SNOW Intro. to Text Analytics A1 due iPython Notebook
02-11-15 Real World Problems Various A1 due
02-16-15 NO CLASSES - Presidents' Day
02-18-15 Introduction to Visualization
02-19-15 Crash Course in D3.js Lane Harrison, Tufts A2 out Note: class held Thursday
02-23-15 Dealing w/ Large Data Data Projections Rajmonda Caceres, MITLL
02-25-15 Interaction
03-02-15 Storytelling with Visual Analytics EventFlow Megan Monroe, IBM A2 due
03-04-15 Student Presentations: Visual Analytics Systems in the Wild
03-23-15 Brainstorming Session: Sketching and Early Prototyping
03-25-15 Conducting a Needs Assessment Diane Staheli, MITLL
03-30-15 LAB: Final Projects
04-01-15 Interaction pt. 2: Methods
04-06-15 Analytic Provenance
04-08-15 Small-Group Discussion: Self-Critique and Feedback
04-13-15 Evaluation Techniques
04-15-15 Open Research Topics Kris Cook, PNNL
04-20-15 NO CLASSES - Patriots' Day
04-22-15 Large-Group Discussion: Final Project Reflections and Lessons Learned
04-27-15 Final Project Demonstrations and Reception

Assignments and Deliverables

The first half of this course will be focused on building up intuitions around the relationships between data, perception, and interaction that support sensemaking. To this end, four (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 attention to student-driven projects. Various industry partners (VIPs) will come in to pitch potential datasets and/or problems, and you're are also welcome to propose a dataset you care about. We'll look at some ways to map the techniques we learned in the first half of the course to these problems, and you'll start building your own VA systems to address them. The project will have several (graded) milestones along the way, and we will hold a demonstration session on the final day of class.


Python is useful for data ingest, cleaning, formatting, and general wrangling.

RStudio is great for statistical analysis.

Tableau's data visualization software is provided through the Tableau for Teaching program.

Required Reading
R1 Illuminating the Path: The Research and Development Agenda for Visual Analytics
James J. Thomas and Kristin A. Cook. IEEE Computer Society, 2005. ISBN: 0-7695-2323-4
Free (pdf)
Recommended Books
R2 Psychology of Intelligence Analysis
Richard J. Heuer. Central Intelligence Agency, 1999. ISBN: 1-9296-6700-0
Free (pdf)
R3 Interactive Data Visualization: Foundations, Techniques, and Applications
Matthew Ward, Georges Grinstein, Daniel Keim. AK Peters, 2010. ISBN: 1-5688-1473-9
Additional Reading Material
R4 Visual Analysis for Everyone: Understanding Data Exploration and Visualization
Tableau Software (Pat Hanrahan, Chris Stolte, Jock Mackinlay), 2007.
R5 A Tour Through the Visualization Zoo
Jeffrey Heer, Michael Bostock, Vadim Ogievetsky, 2010


Class Participation 20%
Assignment 1 10%
Assignment 2 10%
Assignment 3 10%
Presentation on Research Paper 10%
Final Project 40%
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 curisoity or clear understanding of a topic, as well as comments which help others in class to learn a difficult concept.


Tufts is committed to providing support services and reasonable accommodations to all students with documented disabilities. To request an accommodation, you must register with the Disability Services Office at the beginning of the semester. To do so, contact the Student Services Desk at (617) 627-2000 to arrange an appointment with Linda Sullivan, Program Director of Disability Services, or send an email to


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.