IS590DVO - Data Visualization

This is the course website for IS590 Data Visualization in Spring 2019 (Online)


  • Spring, 2019
  • online, Thursday, 5:30-7:30pm
  • 4 Credit Hours

  • Instructor: Jill P. Naiman
    • Email:
    • Office: 2040 NCSA Building
    • Office Hour: TBD
    • Preferred Contact Method: email

Course Description

Data visualization is crucial to conveying information drawn from models, observations or investigations. This course will provide an overview of historical and modern techniques for visualizing data, drawing on quantitative, statistical, and network-focused datasets. Topics will include construction of communicative visualizations, the modern software ecosystem of visualization, and techniques for aggregation and interpretation of data through visualization.Particular attention will be paid to the Python ecosystem and multi-dimensional quantitative datasets.

Course Overview

This course is designed to give practical advice to students on communicating data through visualization. This will involve a considerable amount of programming, and typically this programming will be done in Python. For the most part, our data will be quantitative and multi-dimensional. The course will aim to provide both an understanding of what data visualizations communicate and a set of tools for constructing them yourself.

The course will follow a common pattern within each three-hour instructional session. The first 60-90 minutes will be focused on lecture, where concepts and tools will be introduced; typically, each class will focus on one type of visualization or class of visualization. The remaining time will include exploration of a dataset, which may be independent or in groups, and then a wrap-up session at the end.

Students are expected to have laptops with them, as well as access to Python installations, and will be encouraged to participate in class. Homework will be assigned and collected utilizing the Jupyter nbgrader extension or through other methods specified at time of submission.

Pre- and Co-requisites

None, although basic Python programming experience is assumed. A brief introduction to Python will be presented during the course.

Land Acknowledgment

As a land-grant institution, the University of Illinois at Urbana-Champaign has a responsibility to acknowledge the historical context in which it exists. In order to remind ourselves and our community, we will begin this event with the following statement. We are currently on the lands of the Peoria, Kaskaskia, Peankashaw, Wea, Miami, Mascoutin, Odawa, Sauk, Mesquaki, Kickapoo, Potawatomi, Ojibwe, and Chickasaw Nations. It is necessary for us to acknowledge these Native Nations and for us to work with them as we move forward as an institution. Over the next 150 years, we will be a vibrant community inclusive of all our differences, with Native peoples at the core of our efforts.

More information can be found on the Chancellor’s Website.

Course Overview

The central themes of the course are:

  1. What are the components of an effective visualization of quantitative data?
  2. What tools and ecosystems are available for visualizing data?
  3. What systems can be put in place to generate visualizations rapidly and with high-fidelity representation?

Course Materials

There is no textbook for this course. All course materials will be posted to the GitHub repository at .

As the course progresses, a list of recommended readings will be generated for each class. These will be included in the course materials repository, and students are encouraged to fork that repository and issue pull requests to add suggested readings.

About Your Instructor

Jill Naiman’s background is in theoretical and computational astrophysics with a current research emphasis on scientific data visualization. She is currently a Visiting Scholar at the Advanced Visualization Lab at the National Center for Supercomputing Applications. She is currently involved in projects related to increasing access to industry-standard special effects software for scientists - more info can be found here and here. Information about her astrophysical research and outreach efforts can be found here.

Library Resources

| | | Phone: (217) 300-8439

Writing and Bibliographic Style Resources

The campus-wide Writers Workshop provides free consultations. For more information see The iSchool has a Writing Resources Moodle site and iSchool writing coaches also offer free consultations.

Academic Integrity

Please review and reflect on the academic integrity policy of the University of Illinois, to which we subscribe. By turning in materials for review, you certify that all work presented is your own and has been done by you independently, or as a member of a designated group for group assignments. If, in the course of your writing, you use the words or ideas of another writer, proper acknowledgment must be given (using APA, Chicago, or MLA style). Not to do so is to commit plagiarism, a form of academic dishonesty. If you are not absolutely clear on what constitutes plagiarism and how to cite sources appropriately, now is the time to learn. Please ask me! Please be aware that the consequences for plagiarism or other forms of academic dishonesty will be severe. Students who violate university standards of academic integrity are subject to disciplinary action, including a reduced grade, failure in the course, and suspension or dismissal from the University.

Statement of Inclusion

Inclusive Illinois Committee Diversity Statement

As the state’s premier public university, the University of Illinois at Urbana-Champaign’s core mission is to serve the interests of the diverse people of the state of Illinois and beyond. The institution thus values inclusion and a pluralistic learning and research environment, one which we respect the varied perspectives and lived experiences of a diverse community and global workforce. We support diversity of worldviews, histories, and cultural knowledge across a range of social groups including race, ethnicity, gender identity, sexual orientation, abilities, economic class, religion, and their intersections.

Accessibility Statement

To obtain accessibility-related academic adjustments and/or auxiliary aids, students with disabilities must contact the course instructor and the Disability Resources and Educational Services (DRES) as soon as possible. To contact DRES you may visit 1207 S. Oak St., Champaign, call (217) 333-4603 (V/TTY), or e-mail a message to

Assignments and Evaluation

Students will be graded based on a combination of assignments (70%) and a final project (30%). The final project will be a capstone to the course, and will have greater flexibility in software packages and data sources. This project will be introduced in Week 8.

Assignments in this course will be a mixture of coding/visualization work and written work. These two may not be distinct assignments; students will be asked to describe their code and justify choices for making decisions with respect to visualizations.

Students are expected, unless otherwise instructed, to be the principal authors of their code. This does not mean they may not investigate resources such as StackOverflow, package documentation, etc; however, they must cite when resources (especially StackOverflow and other “recipe” sites) are utilized.

Assignments will take two forms, and will be given at the end of each class. Students will have until the following class to turn these in; assignments will be collected electronically.

The submission process for homeworks will be described by example during class before any homeworks are to be submitted.

Each assignment will be 50% “correctness” and 50% the narrative description of the process. “Correctness” in this case indicates that the code runs without issue, results are produced, and each component of the assignment is completed. The narrative description of the process will be graded on grammar and completeness.

Grading Policy

All assignments are required for all students. Completing all assignments is not a guarantee of a passing grade. All work must be completed in order to pass this class. Late or incomplete assignments will not be given full credit unless the student has contacted the instructor prior to the due date of the assignment (or in the case of emergencies, as soon as practicable).

Grading Scale:

94-100 = A
90-93 = A-
87-89 = B+
83-86 = B
80-82 = B-
77-79 = C+
73-76 = C
70-72 = C-
67-69 = D+
63-66 = D
60-62 = D-
59 and below = F


Students must request an incomplete grade from the instructor. The instructor and student will agree on a due date for completion of coursework and the student must file an Incomplete Form signed by the student, the instructor, and the student’s academic advisor with the School’s records representative. More information on incompletes is available here:

Late Assignments

Students are required to attend each class, and if they are unable to do so much notify the instructor and TA in advance and request an excused absence. Participation in class – in the form of comments, questions, and discussion – is expected.

Semester Calendar

This is the first semester that this course is being taught. As such, the course outline below is subject to some flexibilty; students will be encouraged to provide feedback on the topics covered, particularly toward the end. Topics that are of particular interest will be emphasized.

Emergency Response: Run, Hide, Fight

Emergencies can happen anywhere and at any time. It is important that we take a minute to prepare for a situation in which our safety or even our lives could depend on our ability to react quickly. When we’re faced with any kind of emergency – like fire, severe weather or if someone is trying to hurt you – we have three options: Run, hide or fight.


Leaving the area quickly is the best option if it is safe to do so.


When you can’t or don’t want to run, take shelter indoors.


As a last resort, you may need to fight to increase your chances of survival.

Please be aware of persons with disabilities who may need additional assistance in emergency situations.

Other resources