Class Description
Data visualizations provide a visual setting in which to explore, understand, and explain data. This class surveys the mathematical and perceptual principles, methods, and applications of data visualization. Visualizations will be web-based, using D3.js, and possibly other systems built on top of D3.Prerequisites: One of CMSC 12200, CMSC 15200 or CMSC 16200.
People
| Instructor | Gordon Kindlmann (office hours: Tuesdays 2:30-4pm JCL 249) |
| grad TA: | Zhuokai Zhao (office hours: Mondays 2:30-3:30pm, JCL 207) |
| undergrad TA: | Zoa Katok (office hours: Wednesdays 5:30-6:30pm, JCL 207) |
Grading and Assignments
The following items determine the class grade, according to their percentages:- 5% each: 7 homeworks. These are completed individually. Homeworks involve questions about: readings, math, or programming.
- 12% each: 4 projects. These can be done individually or in a pair.
- 6%: In-lab coding midterm (April 18).
- 11%: Written final exam (May 31).
- When averaging these to find your final numerical grade, some percentage of your lowest-graded work will be dropped (not included in the weighted average of all your work). For students that are not graduating, 10% will be dropped. For graduating seniors, 16% will be dropped.
Late Policy
We expect you to finish your work on time (for work handed in via svn, we will grade work committed by the deadline). Throughout the quarter, you may get up to six 24-hour extensions (“late-chips”) on any of the assignments. Only one extension may be used per assignment (they may not be "stacked"). You request late-chips at work-groups.cs.uchicago.edu, but must do so before the original (non-extended) assignment deadline. Exceptional circumstances may warrant additional consideration, at the instructor's discretion (post a private note in the per-assignment category on EdStem).Meetings, Communication, and Resources
- In-person class meetings: Tue/Thu lectures are in-person, but a Zoom meeting (not recorded) will be created for those that must be remote. PDFs of slides shown will be made available after class, but being engaged and involved during class is always better than trying to catch up afterwards.
- Labs: Monday lab sessions will used mainly to ensure that you aren't having problems with the technical issues in finishing programming class work. Some (non-graded) programming exercises may be given as well. One lab meeting time will be used for in-lab coding midterm exam.
- There is no single required textbook. There will be assigned readings from papers in the visualization literature. Try to do the reading prior to the date indicated.
- Announcements to the class will be sent via the class EdStem page. Questions about assignments should also be posted on EdStem. Everyone enrolled on March 29 was enrolled on EdStem, and class enrollment will be periodically re-synched after that.
- You should ask questions on EdStem, so that everyone can benefit from seeing the answer. To ask questions directly to the professor or TAs, post a private question on EdStem, rather than emailing us.
- Email to students will be addressed to their CNetID@uchicago.edu address.
- The SVN for DataVis page describes how svn will be used for getting files related to the projects (such as datasets), and for handing in homework and projects. Nothing will be done on paper (except the written final exam).
Academic Honesty
In this course, as in all your courses, you must adhere to the University-wide Academic Honesty policies of the Student Manual. These are also described by the College under Academic Integrity & Student Conduct; expand the "Academic Integrity" section at the page bottom. To paraphrase:
- Never copy work from any other source and submit it as your own.
- Never allow your work to be copied.
- Never submit work identical to another student's.
- Document all collaboration
- Cite your sources.
Please note that sharing your work publicly (such as posting it to the web) definitely breaks the second rule. With respect to the third rule, you may discuss the general idea of how to solve a particular problem with another student (in which case, you must document it per the fourth rule), but you may not share your work directly. When it comes time to sit down and start writing or typing, you must do the work by yourself (or with your partner for that project). Discussion of class work must be entirely voluntary and never transactional. If you have any questions or concerns about this policy, or about the behavior of another student with respect to it, please ask your instructor (via email or private note on EdStem) as soon as possible. This statement of Academic Honesty is based on that of Adam Shaw.
Sexual Misconduct
Our school is committed to fostering a safe, productive learning environment. Title IX and our school policy prohibits discrimination on the basis of sex. Sexual misconduct – including assult, harassment, stalking, and domestic and dating violence — is also prohibited. Harassment can take the form of, for example, any repeated unwelcome comments of a sexual nature, or any sexual advance associated with seeking help on class work. Review the Policy on Harassment, Discrimination, and Sexual Misconduct, in particular the Sexual Misconduct and Definitions, so you understand what this covers.
Our school encourages anyone experiencing sexual misconduct to talk to someone about what happened, so they can get the support they need and our school can respond appropriately.
If you wish to speak confidentially about an incident of sexual misconduct, want more information about filing a report, or have questions about policies, procedures, or support services, please contact our Title IX Coordinator; see the Reporting Options section of the policy. Our school is legally obligated to investigate reports of sexual misconduct after a formal complaint is filed or signed by the Title IX Coordinator, but a request for confidentiality will be respected to the extent possible. As a faculty member, I am required to report any harassment that I learn about to the Title IX Coordinator.