CISC 6745 L01 Data Visualization (Fall 2018)
Class Time: Tue 5:30-7:45 PM
Classroom: Leon Lowenstein (LL) 514
Instructor: Dr. Truong Huy Nguyen (email@example.com)
Office Hours: Tue 4:00-5:30 PM or by appointment via email
Office during office hours (in Lincoln Center): LL 610H
Data is essential and helpful to inform decision-making and impact public or corporate policies. Moreover, when visualized with proper context, data has the power to make a change in the world. This course explores the underlying theoretical concepts and psychological implications in creating visual representations of quantitative data, as well emphasizes on practical implementations of visualizations. It covers core topics in data visualization including: data representation, visual encodings and their effects, visualization guidelines, methods, and tools. This course will include practical assignments and a significant project component that will require programming.
Prerequisites: CISC 5500 Data Analytic Tools and Scripting
Students will gain a practical understanding of
- Design principles to tell compelling stories with visualizations
- The power of visualizations in data discovery and understanding, as well as in misleading audience
- Visualization techniques to deal with different kinds of data, such as quantitative data, high-dimensional data, and map data
- How to create visualizations using commercial and open-sourced toolkits
There is no required textbooks in this course. Materials will be announced on BlackBoard or in the Course Schedule before class when appropriate.
Some recommended textbooks
- "The Visual Display of Quantitative Information, 2nd Edition", Edward R. Tufte. Graphics Pr, 2001.
- "Information Dashboard Design: Displaying Data for At-a-Glance Monitoring, 2nd Edition", Stephen Few. Analytics Press, 2013.
- "Visualization Analysis and Design", Tamara Munzner. A K Peters/CRC Press, 2014.
Attendance and Class Participation
It is important to attend every class and to be prepared for every class. Being prepared means completing the assigned readings and homeworks on time and being ready to discuss the material. Active participation in class is also important as this will make the course more interesting for everyone. If you are going to miss class or will not have a homework completed on time, let me know in advance with a reasonable excuse. Otherwise, the policy for late submission will kick in (see below in Grading).
All work produced in this course should be your own unless it is specifically stated that you may work with others. You may discuss the homework problems with other students generally, but may not provide complete solutions to one another; copying of homework solutions is always unacceptable and will be considered a violation of Fordham's academic integrity policy. Violations of this policy will be handled in accordance with university policy which can include automatic failure of the assignment and/or failure of the course.
The percentages given below are guidelines for both students and instructor; minor changes may be made during the course (students will be informed promptly of any such changes).
Attendance and Participation 10% (participation: 10%) Homeworks 20% (HW1: 10%, HW2: 10%) Midterm Exam 20% Course Project 25% (Proposal: 5%, Final Report: 10%, Final Presentation: 10%) Final Exam 25%
To map a numerical grade to a letter grade, I use the following mapping (which is the default built into Blackboard). However, in some cases I may curve grades upward.
A 94-100 C+ 77-80 A- 90-94 C 74-77 B+ 87-90 C- 70-74 B 84-87 D 65-70 B- 80-84 F <65
Late Submission Policy
Assignments must be completed by the date specified in their respective descriptions and submitted according to the provided instructions. There will be a 20% point deduction each day after the due date. Assignment will not be accepted 3 days past the due date, unless under extraordinary circumstances and priorly approved by the instructor.
The course will include a course project. You may work in teams of up to 2 (special permission is needed to work in larger teams).
In the course project, you could either address a research question or analyze a real world data set.
- A research project in data visualization needs to have some fundamentally novel element that applies beyond just a single data set, such as a new visualization method.
- An application project on the other hand involves creating a story told through the use of visualization on some real-life data set. Usually the most important phase in the project is to come up with a question or real-life problem, then find an appropriate data set that may help to answer that question through the use of visualization.
If you are a student with a documented disability and require academic accommodations, please register with the Office of Disability Services for Students (ODS) in order to request academic accommodations for your courses. Please contact the main ODS number at 718-817-0655 to arrange services. Accommodations are not retroactive, so you need to register with ODS prior to receiving your accommodations. Please see me after class or during office hours if you have questions or would like to submit your academic accommodation letter to me if you have previously registered for accommodations.