This is the course website for IS590DV in Spring 2020
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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.
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.
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 like Moodle.
The central themes of the course are:
None, although basic Python programming experience is assumed. A brief introduction to Python will be presented during the course.
There is no required textbook for this course. All course materials will be posted to the GitHub repository at https://github.com/UIUC-iSchool-DataViz/spring2020 .
A list of Python libraries week-by-week and tips on how to install them can be found by clicking this link.
Optional textbook Visualization Analysis and Design by Tamara Munzner
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.
Below is an approximate outline of the course and optional reading for each week. This course is always under development and the course outline below is subject to some flexibility; students will be encouraged to provide feedback on the topics covered, particularly toward the end. Topics that are of particular interest will be emphasized.
Optional texts:
Acronyms for books:
Course Outline and Optional Reading List
| Week 1 | Introduction, syllabus, examples, and some basics | 1. VAD, Ch. 1: What’s Viz, and Why Do It? 2. FDA, Ch. 1: Introduction & FDA, Ch. 17: The principle of proportional ink 3. Same Data, Multiple Perspectives |
| Week 2 | Data storage & Operations | 1. VAD, Ch. 2: What: Data Abstraction 2. FDA, Ch. 2: Visualizing data: Mapping data onto aesthetics 3. IS452’s intro to CSV files (bottom of page) 4. IS452’s Intro to Dictionaries 5. Pandas Docs & NumPy Docs |
| Week 3 | Types of Viz and color, colormaps | 1. VAD, Ch. 10: Map Color and Other Channels 2. FDA, Ch. 4: Color scales 3. Perception in Visualization (pay attention to the parts about color) 4. Palettable Docs |
| Week 4 | Beginning Interactivity | 1. Intro to ipywidgets 2. Example Widgets Notebooks 3. VAD Ch. 7: Arrange Tables 4. FDA, Visualizing distributions: Histograms and density plots |
| Week 5 | Distributions, Engines | 1. Video about bqplot 2. An introduction to Grammar of Graphics 3. ipywidgets Docs; Traitlets Docs; bqplot Docs |
| Week 6 | Dashboards & Maps with bqplot | 1. VAD Ch. 8: Arrange Spatial Data 2. VAD Ch. 11: Manipulate View 3. FDA, Ch. 15: Visualizing geospatial data |
| Week 7 | More with maps - bqplot, cartopy, ipyleaflet, geopandas | 1. VAD Ch. 8: Arrange Spatial Data 2. FDA, Ch. 15: Visualizing geospatial data 3. Cartopy docs; ipyleaflet docs; Geopandas Docs |
| Week 8 | Break | None |
| Week 9 | Network Viz & Word cloud Viz | 1. VAD Ch. 9: Arrange Networks and Trees |
| Week 10 | Designing for the web with Python & Javascript (JS) | 1. Iodide Docs - in particular: key concepts & IOMD format 2. Intro to Javascript 3. FDA, Ch. 5: Directory of visualizations |
| Week 11 | Designing for the web with Python & Javascript, Web dev | 1. Same Data, Multiple Perspectives 2. Iodide Docs 3. vega-lite docs - in particular: Vega-lite transformations & Vega-lite selections 4. Idyll Docs |
| Week 12 | More javascript & web dev Guest lecture about scientific & cinematic viz from AVL |
1. Idyll Docs - in particular: Built in/npm installed components |
| Week 13 | Scientific visualization | 1. yt docs |
| Week 14 | Volume rendering for scientific viz, more with Idyll, Publishing Viz | 1. yt docs 2. yt Volume Rendering Tutorial 3. Idyll Docs |
| Week 15 | Idyll + d3.js, course wrap up! | 1. d3.js docs |
Jill Naiman’s background is in theoretical and computational astrophysics with a current research emphasis on scientific data visualization and the digitization of historical scientific images and text. 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.
| http://www.library.illinois.edu/lis/ |
| lislib@library.illinois.edu |
| Phone: (217) 300-8439 |
The campus-wide Writers Workshop provides free consultations. For more information see http://www.cws.illinois.edu/workshop/ The iSchool has a Writing Resources Moodle site https://courses.ischool.illinois.edu/course/view.php?id=1705 and iSchool writing coaches also offer free consultations.
Please review and reflect on the academic integrity policy of the University of Illinois, http://admin.illinois.edu/policy/code/article1_part4_1-401.html 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.
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.
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 disability@illinois.edu.
Students will be graded based on a combination of assignments (70%: 40% standard prose/code assignments and 30% weekly visualization reports) 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.
In summary, your grades consist of:
| 40% | Standard assignments in prose or code form |
| 30% | Weekly visualization reports |
| 30% | Final project |
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 (less so) and completeness (more so).
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: http://webdocs.ischool.illinois.edu/registration/incomplete_grade_form.pdf
Students are required to attend each class, and if they are unable to do so must 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.
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.