CS 360-01 Data Visualization • Spring 2013
Monday, Wednesday, Friday 1:00pm – 2:05pm
This course will introduce students to the field of data visualization. Students will learn basic visualization design and evaluation principles, and learn how to acquire, parse, and analyze large datasets. Students will also learn techniques for visualizing multivariate, temporal, text-based, geospatial, hierarchical, and network/graph-based data. Additionally, students will utilize Processing, D3, R and ggplot2, and many other tools to prototype many of these techniques on existing datasets.
This course falls under the "Applications" category for Computer Science majors, and counts towards a minor in Computer Science.
Prerequisites: You must have completed CS 212 Software Development with a grade of C or better to take this course.
Please contact the instructor for lecture, project, or course-related questions.
Harney Science Center • Room 532
Monday, Wednesday, Friday • 3:30pm – 4:30pm
and by appointment
If you are unable to make these office hours, please contact the instructor to setup an appointment.
Please contact the teacher assistant for help with homework, or with questions regarding homework or participation grades.
The teacher assistant will hold office hours throughout the semester. Exact dates and times will be announced during the first week of class.
You can find announcements, a calendar, lecture notes, and contact information on this website. Please check the course website regularly.
Announcements will also be posted regularly on the course website. You can subscribe to these announcements via RSS:
You may also follow me @sjengle on Twitter. I will often post extra office hours on Twitter, but not all of the posts are related to this course.
At the end of this course, students should be able to:
Please see the following sections for additional details.
The following is an estimated list of topics and weekly schedule. Check the course website for the latest schedule.
The final exam will be held during the last week of lecture. The exam period during finals week will be used for final project presentations instead.
There are no required textbooks for this class. See the Resources page for recommended books.
Lectures will consist of slide presentations and code demonstrations. Students will be required to complete a mix of participation exercises, homework assignments, exams, and projects. See the following sections for additional details.
This category includes participating in peer evaluations and discussion exercises (both in-class and online). Graduate students will also be expected to read and present recent visualization papers on a specific topic.
will be several homework assignments, both written and programming,
assigned on a semi-weekly basis. This will include evaluating and reworking existing
visualizations, using existing tools to design visualizations, and prototyping custom visualizations in Processing or D3.
will be assigned a final visualization project near the end of the semester. For
the final project, students will select a data set and visualization
technique, develop a prototype, and rework the prototype based on peer
evaluations. Students will demonstrate their final projects during finals
The final grade for this course will depend on a mix of homework, projects, participation, and exams. The specific breakdown is as follows:
10% Midterm Prototype
15% Final Prototype
*Updated on 2/18/2013.
Letter grades will be assigned according to the following scale:
97% ≤ A+
For example, you will receive a C letter grade if your grade is greater than or equal to 70% and less than 77%. Please note this scale is subject to change.
The averages for undergraduates versus graduate students will be computed separately, and a separate curve will be applied at the end of the semester if necessary. Below are the grading policies for extra credit, late submissions, and cheating.
There may occasionally be extra credit opportunities throughout the semester. Unless otherwise noted, each grade category will be capped to 100%. As a result, homework extra credit will not affect the project grade (and visa versa).
All deadlines and exam dates are firm. No late homework, quizzes, exams, or projects will be accepted. Late submissions will receive an automatic 0%.
Exceptions to this policy are made only in the case of verifiable medical or family emergency. Extensions and makeup exams must be arranged prior to the original deadline unless in case of extreme emergency (such as an emergency room visit).
All students are expected to know and adhere to the University of San Francisco's Honor Code. Go to http://www.usfca.edu/catalog/policies/honor for details. The first violation of the Honor Code will result in an automatic 0 on the offending assignment, and repeat violations will result in an automatic F for the course.
Cheating includes copying code from the web or other students, collaborating too closely with other students on non-collaborative assignments, or in any other way misrepresenting work of others as your own.
This is a four credit course. Students are expected to spend approximately 10 hours per week outside of lecture working on homework and projects, as well as participating in discussions and preparing for exams.
The Learning and Writing Center provides assistance to students in their academic pursuits. Services are free to students and include individual and group tutoring appointments and consultations to develop specific study strategies and approaches. Please visit http://www.usfca.edu/lwc for more information.
If you are a student with a disability or disabling condition, or if you think you may have a disability, please contact USF Student Disability Services (SDS) within the first week of class to speak with a disability specialist. If you are determined eligible for reasonable accommodations, your disability specialist will send your accommodation letter to the instructor detailing your needs for the course. For more information, please visit http://www.usfca.edu/sds or call (415) 422-2613.