CS 7295: Information Visualization  

(CRN:        37033)

Spring 2018 - Prof. Michelle Borkin

Mondays and Thursdays, 11:45 am - 1:25 pm (Snell Library 125)

http://www.ccs.neu.edu/home/borkin/courses/2018Spring/CS7295/ 

This course will count towards the Human-Centered Computing Breadth Course requirement for the PhD in Computer Science degree program.

Description

Introduces foundational principles, methods, and techniques of visualization to enable creation of effective information representations suitable for exploration and discovery. Covers the design and evaluation process of visualization creation, visual representations of data, relevant principles of human vision and perception, and basic interactivity principles. Studies data types and a wide range of visual data encodings and representations. Draws examples from physics, biology, health science, social science, geography, business, and economics. Emphasizes good programming practices for both static and interactive visualizations. Creates visualizations in Tableau as well as Python and open web-based authoring libraries. Requires programming in Python, JavaScript, HTML, and CSS. Requires writing including a final project report, code documentation, design critique essays, journal paper reviews, and discussions of the findings from the data analyses and the visualizations.

After completion of the course students should be able to:

• Choose appropriate visualization methods for a given data type
• Assess the quality and effectiveness of a visualization
• Design an effective visualization using design and human perception principles
• Implement both static and interactive visualizations
• Apply and implement basic interactivity functions to enable data exploration
• Implement visualizations in Python
• Implement web-based visualizations in JavaScript/HTML/CSS
• Create visualizations in Tableau
• Present and be knowledgeable about contemporary visualization topics
• Effectively communicate technical material in written form
• Effectively communicate technical material in oral presentation form
• Constructively critique and assess a visualization in written form

Topics Covered

• Visual encodings and data types: Introduction to visualization types and how pick an appropriate visualization in 2D and 3D for a given user, their tasks, and the appropriate dataset. Covers data types and appropriate visualizations including map, timeline, tree, network, and basic statistical plots.
• Human perception and cognition: Understanding the basics of how people perceive shape, color, size, Gestalt groups and how this affects visualization design.
• Color: Appropriate choice of colors, color blindness, color scales, color spaces.
• Design thinking: A holistic view of aesthetics, readability, layout; user capabilities and tasks; ideation and design processes.
• Data simplification techniques: Tools for reducing data complexity as well as visual complexity. On the data side these include filtering & queries, pivoting, aggregation. Visual simplification techniques include visual aggregation, dimensionality reduction, and interaction techniques.
• Interaction: Designing multiple coordinated views with brushing and linking, navigation through large datasets (overview + context | overview, filter, details on demand).
• Visual Storytelling & Animation: Animated transitions and evaluating effective vs. superfluous transitions for preserving the user’s mental map of the underlying data and its relationships. In particular, dealing with time-varying data and basic storytelling techniques.
• Evaluation methodology: User experience, human subjects, and quantitative techniques for evaluating visualization and interaction techniques, algorithms, systems, and human perception and vision.
• Cutting edge research:  Based on recent publications and interests of the class, additional topics including, for example, time series visualization, visualization on mobile/tablet devices, visualization for network science, and visualization for and of machine learning and AI more broadly.

Prerequisites

There are no formal prerequisites for this course.  Topics such as HCI, web design, databases, and data mining all are very useful for the course but not required prerequisites.  Students should be comfortable with programming and learning new programming languages.

Note: The assignments and projects in this course will expose students to a variety of programming languages including Javascript (i.e., D3) and Python (e.g., matplotlib) as well as tools including Tableau for visualization design and implementation.  Although very helpful to have, no prior experience with these tools (or web design in general) is required.

Textbooks

Required

This course will utilize two textbooks:  one to cover basic visualization theory and methodology, and one to cover basic perceptual theory as applied to visualizations.

Visualization Analysis and Design by Tamara Munzner

(**Available for free online through the library.)

Visual Thinking for Design by Colin Ware

Recommended

Although not required, the following books are recommended as additional references to help expand on the visualization theory and skills discussed in class.  These books will be cited and referred to during lecture as appropriate to help direct the student to the appropriate resource.

Design for Information by Isabel Meirelles

The Functional Art: An introduction to information graphics and visualization by Alberto Cairo

Information Visualization: Perception for Design by Colin Ware

Interactive Data Visualization for the Web (free online version) by Scott Murray

The Visual Display of Quantitative Information by Edward Tufte

Course Components

Lectures

Each class will generally be split into two parts: lecture and class activity.  The lecture component will be ~45-60 minutes in duration covering foundational topics, and the remainder of the class will be devoted to an in-class activity to further explore topics and skills relevant to the course topics.  (Sometimes the activity may occur in smaller portions interspersed in the lecture.)  Activities will include such exercises as programming tutorials, design critiques, journal paper discussions, in-class mini-hacks, and student-led final project update presentations.  

Class Participation

Students are expected to participate both in-class as well as online through the course’s Piazza discussion group.  A student’s participation grade (see Grading section below) will be composed of their in-class participation as well as activity on Piazza.

Readings

Assigned reading will be given to read in advance of each lecture and listed in the Course Schedule online.  Readings will be drawn from the required textbooks as well as online supplemental material (e.g., journal papers).  As part of the homework assignments (section below), students will also be required to read journal papers relevant to the lecture material and write reviews/summaries.

Homework

There will be regular homework assignments over the course of the semester.  Each assignment will require the student to apply the concepts discussed in the readings and in-class lectures to both programming assignments for the actual building and implementation of static and interactive visualizations as well as short writing assignments (e.g., design critiques, journal paper reviews, etc.).  The homework assignments are an individual assessment and should not be completed in groups.

Final Project

For the semester-long final project, students will work in groups of 2-3 people to create an interactive visualization incorporating the concepts discussed in the course.  Throughout the semester there will be mandatory final project progress deadlines.  As part of the final project, in addition to the final interactive web-based visualization, students will be required to write a final project report as well as produce a website to host the visualization, a demo video, and an in-class short presentation.  

Student groups can pick any topic of their choosing and will have their project idea approved by the teaching staff through a formal project proposal process with feedback.  (A formal set of requirements for the final project will be provided in a separate document.)  PhD students are encouraged to pick a topic or visual encoding method relevant to their dissertation research.

Grading

Grades will be broken down as follows:

• Homework Assignments (45%)
• Class Participation (15%)
• Final Project (40%)

Course Policies

Late Policy
All homework and project related due dates are final and provided in the course schedule.  No assignments will be accepted for credit after the deadline.

If you have a verifiable medical condition or other special circumstances that interfere with your coursework or meeting a deadline, please email (m.borkin@northeastern.edu) as soon as possible.

Academic Integrity Policy
A commitment to the principles of academic integrity is essential to the mission of Northeastern University.  The promotion of independent and original scholarship ensures that students derive the most from their educational experience and their pursuit of knowledge.  Academic dishonesty violates the most fundamental values of an intellectual community and undermines the achievements of the entire University.

For more information, please refer to the Academic Integrity Web page.