Class Description

Data visualizations provide a visual setting in which to explore, understand, and explain data sets. This class describes mathematical and perceptual principles, methods, and applications of "data visualization" (as it is popularly understood to refer to primarily to tabulated data). A range of data types and visual encodings will be presented and evaluated. Visualizations will be primarily web-based, using D3.js, and possibly other higher-level languages and libraries.
Prerequisite(s): CMSC 12200 or CMSC 15200 or CMSC 16200

People

Instructor	Gordon Kindlmann Office hours (Ryerson 161B): Wednesdays 11am-Noon, Thursdays Noon-1pm
TA	Stephen Fitz Office hours (Young 404): Thursdays 1pm-2pm
TA	Kai Li Office hours (Ryerson 162): Wednesdays 3pm-4:30pm
TA	Nicholas Seltzer Office hours (Ryerson 176): Mondays 12:30pm-1:30pm

Grading

Your grade will be determined from the following components:

• 25%: 5 Homeworks (completed individually), to test understanding of lectures and readings, and to introduce or exercise some elements of the projects.
• 55%: 5 Programming Projects (completed individually or pairing with one other student), to implement things in working code. You may partner with the same person up to 3 times.
• 10%: In-lab coding final exam (completed individualy in a lab session, likely May 22), to ensure that you can implement some basic things on your own.
• 10%: In-class written final exam, May 30, to demonstrate understanding of all material covered in lectures and readings, and familiarity with what the projects covered.

The lowest 5% of your grade will be dropped when determining the final numerical average. Deadlines will be announced with assignments. Required readings will posted on the Syllabus page, and are due on the indicated day. The readings are a collection of papers and book chapters; there is no single textbook.

You can get up to five 24-hour extensions (aka late chips) on the programming projects and homeworks, obtained from the Work-groups server. Only one extension per assignment.

Communication and Resources

Come to class; you'll learn something. Participating in class discussion cannot be replaced by looking at slides posted afterwards, or questions on Piazza.

Class announcements will be made via Piazza (link at top left). Sign up with your CNETID@uchicago.edu address. If you need to write to write to the instructor or the TAs about some circumstances particular to you, write a private note on Piazza, in the "general" folder.

To distribute and collect files: we will use Phoenixforge. All registered students should have a per-student repository named CNETID-datavis17; that link will not work until you place CNETID with your CNetID. The per-class datavis17 project may be used to distribute datasets or other resources that need not be copied to all individual student repositories. The Work-groups server is how you create joint repos for projects, and to get give late chips for programming projects.

Academic Honesty

Academic Honesty The University of Chicago is a community of scholars. Students must understand and internalize the ethics of their academic community. A good place to start is the Cadet Honor Code of the US Military Academy: "A Cadet will not lie, cheat, or steal, or tolerate those who do." The kind of property that matters most to academics is ideas, and to pass someone else's ideas off as your own is to lie, cheat, and steal. The University has a two-paragraph policy on Academic Honesty and Plagiarism, which you should read and understand. Student interactions are an important and useful means to master course material. We recommend that you discuss the material in this class with other students, and that includes the homework and programming assignments. So what is the boundary between acceptable collaboration and academic misconduct? First, while it is acceptable to discuss homework, it is not acceptable to turn in someone else's work as your own. When the time comes to write down your answer, you should write it down yourself from your own understanding. Moreover, you should cite any material discussions, or written sources, e.g., "Note: I discussed this exercise with Jane Smith." You may feel there is a slippery slope from sanctioned discussions to cheating, but a basic principle holds: present only your ideas as yours and attribute all others.

The University's policy says less than it should regarding the culpability of those who know of misconduct by others, but do not report it. If one student "helps" another by giving them a copy of their assignment, only to have that other student copy it and turn it in, both students are at fault. If you have any questions about what is or is not proper academic conduct, please ask the instructor. (This description of Academic Honesty is derived from those of Stuart Kurtz and John Reppy).