I’m happy to write you a recommendation letter, but I will not write a letter for you simply based on your course performance: transcripts already exist as indicators of course performance. If you intend me to write a letter because of a course I taught you, please talk to me at the beginning of the semester, and you should read this section on my advice page.
Also, if you want to use me as a reference in a job application somewhere, that is completely ok, but talk to me about it first. I need your written permission to be able to discuss your performance and participation in the course.
In this course, you will learn how, and why, to create data visualizations.
A “visualization” is simply a visual representation of an object of our interest. It’s visual: we consume them with our eyes, and so it is essential that we know how our eyes work — and, more importantly, the parts of our brains connected to our eyes. It’s also a representation; we get to choose what this representation will be, and different choices lead to different pictures, some good and some bad. We will learn how to tell those apart, and how to make pictures that are more good than bad.
Good data visualization involves perceptual psychology, mathematics, and computer science. This makes our subject uniquely challenging: sometimes the way our eyes work stands in way of applying some beautiful result from computer science. Sometimes it’s the other way around: something deep about the math in the data will help guide the design process and let us make a picture that is beautiful, informative, and truthful.
The content of the course is split roughly in three distinct aspects: mechanics, principles, and techniques.
Although there are no specific prerequisites to this regard, we will write most of our code using the web stack. This means we are targeting modern web browsers, and writing our programs in a combination of HTML, CSS, and JavaScript. If you don’t know these technologies, you will be expected to learn them.
Lectures, individual projects and assignments, in-class discussions.
Principles: You will learn about perceptual psychology, and how it constrains the ways in which we design algorithms for displaying data effectively and efficiently.
Mechanics: You will learn how the modern web stack enables performant and portable data visualization programs. You will learn to use some of the most popular data visualization libraries, you will learn how they are implemented, and their limitations. For some of the methods, you will need to know some linear algebra. You will learn about the importance (and intuition, I promise!) of eigenvalues and eigenvectors of real symmetric matrices.
Techniques: You will learn the fundamental algorithms behind many of the techniques created to display data effectively.
UA’s policy concerning Class Attendance, Participation, and Administrative Drops is available here.
UA’s policy regarding absences for any sincerely held religious belief, observance, or practice will be accommodated where reasonable.
Absences preapproved by the UA Dean of Students (or dean’s designee) will be honored.
If you register late for this class, contact me as soon as you do. You will be expected to submit all missed assignments within a week of your registration. It is your responsibility to catch up to the class content.
We will use Piazza for communications and discussion.
There is no required textbook. All material will be available online, including lecture slides.
There are many good visualization textbooks, all optional:
As mentioned above, you will be assessed based on your performance on programming assignments, one midterm exam and one final exam, and in-class participation.
I will grade your assignments, midterms, and final exam on a scale from 0 to 100, with respective weights of 60%, 20% and 20%. In addition, I will give class participation 5% weight. This will give you a score from 0 to 105. Your final grade in the course of be the best of a per-class grading curve and overall performance:
Overall performance:
By October 30th (your last day to withdraw), you will know more than 40% of your grade by weight.
The class participation grading is discretionary. I will give you feedback on class participation on request.
Grades for assignments, midterm and final exam will be posted on D2L as soon as we have them. The grading for each assignment will be provided one week after the assignment is due.
There will be a total of 11 programming assignments paced at around one assignment per week, skipping weeks for the midterm, spring break, and final. Each assignment will be due at least one week after it is posted.
Assignments will be submitted through Github classroom. This means each student will need to have a Github account.
If you wish to dispute your grade for an assignment, midterm or project, you have two weeks after the grade has been turned in. In addition, even if only you dispute one portion of the grading for that unit, I reserve the right to revisit the entire unit (assignment, midterm, or project).
Students wishing to contract this course for Honors Credit should e-mail me to set up an appointment to discuss the terms of the contact and to sign the Honors Course Contract Request Form.
| Date | Topic | Materials | |
|---|---|---|---|
| Intro | 08/27 | Introduction | slides |
| Mechanics | 09/03 | HTML/CSS/SVG Basics | no slides |
| 09/05 | Javascript Basics | no slides | |
| 09/10 | Javascript + DOM, SVG | no slides | |
| 09/12 | d3 intro | no slides | |
| 09/17 | d3 joins and scales | no slides | |
| Principles | 09/19 | Color vision | slides |
| 09/24 | Color vision | slides | |
| 09/26 | Other perceptual channels | slides | |
| 10/01 | Other perceptual channels | slides | |
| 10/03 | Interaction | slides | |
| 10/08 | Design Criticism, Algebraic Design | slides | |
| Techniques | 10/10 | Basic Spatial Arrangements | slides |
| 10/15 | High-Dimensional Data | slides | |
| 10/17 | Hierarchies | slides | |
| 10/22 | MIDTERM | ||
| 10/24 | Graphs | slides | |
| 10/29 | Graphs+Spatial Data | slides | |
| 10/31 | Spatial Data | slides, slides 2 | |
| 11/05 | Spatial Data | slides 2 | |
| Topics | 11/07 | Cartography | slides |
| 11/12 | Large Data | slides | |
| 11/19 | Putting it all together | ||
| 11/21 | The Human Side of Data | ||
| 11/26 | Retrospective, Review | slides | |
| Catchup | 11/28 | ||
| 12/04 |
The Department of Computer Science is committed to providing and maintaining a supportive educational environment for all. We strive to be welcoming and inclusive, respect privacy and confidentiality, behave respectfully and courteously, and practice intellectual honesty. Disruptive behaviors (such as physical or emotional harassment, dismissive attitudes, and abuse of department resources) will not be tolerated. The complete Code of Conduct is available on our department web site. We expect that you will adhere to this code, as well as the UA Student Code of Conduct, while you are a member of this class.
To foster a positive learning environment, students and instructors have a shared responsibility. We want a safe, welcoming, and inclusive environment where all of us feel comfortable with each other and where we can challenge ourselves to succeed. To that end, our focus is on the tasks at hand and not on extraneous activities (e.g., texting, chatting, reading a newspaper, making phone calls, web surfing, etc.).
Students are asked to refrain from disruptive conversations with people sitting around them during lecture. Students observed engaging in disruptive activity will be asked to cease this behavior. Those who continue to disrupt the class will be asked to leave lecture or discussion and may be reported to the Dean of Students.
All people have the right to be addressed and referred to in accordance with their personal identity. In this class, we will have the chance to indicate the name that we prefer to be called and, if we choose, to identify pronouns with which we would like to be addressed. I will do my best to address and refer to all students accordingly and support classmates in doing so as well.
The UA Threatening Behavior by Students Policy prohibits threats of physical harm to any member of the University community, including to oneself. See http://policy.arizona.edu/education-and-student-affairs/threatening-behavior-students.
This course will, at times, contain material of a mature nature, which may include references to historical violence as collected and depicted in datasets and data visualizations. The instructor will provide advance notice when such materials will be used. Students are not automatically excused from interacting with such materials, but they are encouraged to speak with the instructor to voice concerns and to provide feedback.
At the University of Arizona, we strive to make learning experiences as accessible as possible. If you anticipate or experience barriers based on disability or pregnancy, please contact the Disability Resource Center (520-621-3268, https://drc.arizona.edu/) to establish reasonable accommodations.
Students are encouraged to share intellectual views and discuss freely the principles and applications of course materials. However, graded work/exercises must be the product of independent effort unless otherwise instructed. Students are expected to adhere to the UA Code of Academic Integrity as described in the UA General Catalog. See http://deanofstudents.arizona.edu/academic-integrity/students/academic-integrity.
The University Libraries have some excellent tips for avoiding plagiarism, available at http://www.library.arizona.edu/help/tutorials/plagiarism/index.html.
The University is committed to creating and maintaining an environment free of discrimination; see http://policy.arizona.edu/human-resources/nondiscrimination-and-anti-harassment-policy.
Our classroom is a place where everyone is encouraged to express well-formed opinions and their reasons for those opinions. We also want to create a tolerant and open environment where such opinions can be expressed without resorting to bullying or discrimination of others.
UA Academic policies and procedures are available at http://catalog.arizona.edu/policies
Student Assistance and Advocacy information is available at http://deanofstudents.arizona.edu/student-assistance/students/student-assistance
Campus Health information may be found here: http://www.health.arizona.edu/counseling-and-psych-services
OASIS Sexual Assault and Trauma Services: http://oasis.health.arizona.edu/hpps_oasis_program.htm
Please see http://www.registrar.arizona.edu/personal-information/family-educational-rights-and-privacy-act-1974-ferpa?topic=ferpa for information on confidentiality of student records. This has concrete consequences for you if you give my name as a reference! In other words, if you intend to give my name as a reference, please contact me ahead of time so we can discuss.
Information contained in the course syllabus, other than the grade and absence policy, may be subject to change with advance notice, as deemed appropriate by the instructor.
The University of Arizona sits on the original homelands of indigenous peoples who have stewarded this land since time immemorial. Aligning with the university’s core value of a diverse and inclusive community, it is an institutional responsibility to recognize and acknowledge the people, culture, and history that make up the Wildcat community. At the institutional level, it is important to be proactive in broadening awareness throughout campus to ensure our students feel represented and valued.