XzosBcdhnDW15JHR+dckJw==2025-05-21T07:39:43Zspring 2025
Data and Visual AnalyticsI took this class alongside the Deep Learning course, and the difference was very noticeable. Deep Learning introduced a new homework this semester (Spring 2025), and most of the assignments felt fresh and up-to-date. In comparison, this course feels stuck in 2017. The lecture videos, homework, and tools all seem outdated.
Deep Learning has an enrollment of about 150 students and the professor holds regular weekly office hours. In this course, with over 1,000 students, the professor is MIA until the very end when he asks for reviews. The whole experience felt more like a generic online course. The TA office hours are text-based, which makes the course feel even more impersonal.
I had high hopes for this class. I took the recommended Data Visualization courses by Curran Kelleher and was excited to make creative visualizations and learn about design. Instead, most of the work involved recreating outdated D3 charts. There's little focus on good design. Only one assignment asks you to apply basic design ideas to a table, and it's worth just five points. The rest is just copying and submitting through GS.
Many students just rely on GPT to complete the assignments and project. Some even brag about not learning D3 at all and just using gpt or cursor. The first homework was basic SQL and Python, similar to LeetCode Easy problems. If you couldn’t solve them, GPT could do it for you without any issues. I was also surprised by the low level of questions on the forum—some students didn’t even understand object-oriented programming.
The final project was a total letdown. I thought we’d be making something informative and creative like a NYT or Economist-style visualization. Instead, it was just a checklist. As long as you answered the Hellmeier questions, you could turn in the ugliest chart imaginable and still get full credit.
Group work was a disaster. People didn’t know how to use Git, pushed API keys, uploaded giant CSVs and parquet files, and even dumped raw ChatGPT output (emojis, instructions, everything ) straight into the report. They didn't even bother to check the GPT output, like how do these people get in and how are they allowed to graduate?
I did hear of people who got their grade reduced for not contributing to the final project though, so that was a redeeming quality.
Homework Summary:
Homework 1 and 2: Basic SQL and Python, some D3 and Tableau. Mostly just copying old visualizations.
Homework 3: Simple data cleaning with PySpark and Scala. Claims to teach Docker and cloud platforms like AWS and Azure, but in reality, you just follow instructions in a pdf to make an account and complete exercises in a jupyter notebook. Add it to iCDA?
Homework 4: Basic machine learning with sklearn and some algorithms from scratch. It was a good assignment but felt out of place in a data visualization class. Move it to CDA instead?
Suggestions for Improvement:
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Add a short proctored design quiz that helps students recognize good and bad visualizations.
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Include a homework on deploying visualizations so students can share their work with classmates
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Replace the final poster with the actual deployed visualization. The poster and stringent rubric really made me feel like I was in middle school.
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Include a simple proctored assignment using Tableau or D3 to confirm people can actually program.
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Emphasize teaching design principles so students can create clear, effective visualizations.
Rating: 1 / 5Difficulty: 1 / 5Workload: 12 hours / week