How do we design easy-to-use systems for exploring, understanding, and communicating insights from large, complex datasets? We will review recent research across data science as well as a wide range of commercial and open-source tools. We will read, present on, and discuss relevant research papers together and complete team-based final projects. Prior exposure to data visualization research, HCI research, or data management research (such as through a previous course) is encouraged but not required.

Lectures

Tu/Th 11:30am-12:50pm
Room CSE2 G04

Course Management

Course Communications

We will be using Ed discussion board for our course communications. Please ask your questions on Ed or in office hours. Assignment submissions and grading will be managed through Canvas.

Grading Criteria

  • Lectrure Scribe: 25%
  • Lecture Presentations: 35%
  • Project: 50%

Assignment Submission: All assignments must be submitted on Canvas. Note that the scribe and presentation assignments are graded mainly for best-effort participation.

Late Policy: We do not assign late days for any assignments. For lecture presentations, we drop the two lowest grades. We want to work with every student to make sure they can do their best work. Please reach out to us if you have any concerns about submitting an assignment on time. We are happy to work with you. In the absence of communication regarding a late assignment, we will take off 10% for each day late.

Plagiarism Policy: Assignments should consist primarily of original work. Building off of others' work—including 3rd party libraries, public source code examples, and design ideas—is acceptable and in most cases encouraged. However, failure to cite such sources will result in score deductions proportional to the severity of the oversight.

Religious Accommodation: Washington state law requires that UW develop a policy for accommodation of student absences or significant hardship due to reasons of faith or conscience, or for organized religious activities. The UW’s policy, including more information about how to request an accommodation, is available here: Religious Accommodations Policy. Accommodations must be requested within the first two weeks of this course using the Religious Accommodations Request form.

Assignments

Lecture Scribe (25% of grade)

Each student in the course will contribute to a detailed and organized document of lecture notes for at least one lecture. The notes are due one week from the lecture date. Please have one team member submit the lecture notes on Canvas (if done in a team, only one team member needs to submit). Please make the lecture topic the title of the lecture notes document. Please make sure all team members' names and uw emails are included at the top of the lecture notes. Otherwise, you are welcome to structre the lecture notes however you like.

Lecture Presentations (35% of grade)

Most lectures, students will contribute to a brief (~5min) presentation of one paper as part of a team of 2+ students. Presentation slides are required. A presentation template is available here. Please make sure the names and UW emails of all team members are on the first slide. Please submit the slides on Canvas (only one team member needs to submit). This is not meant to be a difficult assignment. It is only meant to give the class a high level overview of the paper, and will hopefully not take very long to make (perhaps 1 hour or less).

Final Project (45% of grade)

All students will complete a team-based final project. The final project deliverables are a proposal for the intended project, a presentation of the final project outcomes, and a report detailing the final project outcomes. An example report is available here (Note that this is the published version of this final project. It was updated after the class ended for publication).

UPDATE 1/5/2024: Suggested team sizes: 2-3 members.

Lecture Schedule

UPDATE 1/5/2024: Assigned readings per lecture are available on this page.

Tentative Lecture Topics

  • How are artifacts/results curated by data scientists? (and what can we learn from them?)
  • How do people use data science tools to collaborate?
  • How does bias manifest in data science work?
  • What role should interactive analysis systems play in practicing ethical data science?
  • What are the strengths/weaknesses of different analytics environments? Examples:
    • Computational notebooks/programming environments
    • Visualization environments
    • Natural language/Conversational environments
  • How do we measure the efficacy of interactive analysis systems?
  • What does it mean to optimize interactive analysis systems?
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