Computational Literacies Lab

8. Data science tools

Week 8 (October 23)

Due: 2.4 Pokémon lab

Assigned: 2.6 Argument project

Introduction to Pandas and Jupyter notebooks.

Notes

Scatter lab

  • Experiences
    • Hard!
    • This was definitely the most difficult
      • I kept getting error messages and then realized I was just missing one word when scaling x and y.
    • Wow, what an adventure!
  • Strategies
    • Talking to many people
    • Getting help from Discord
    • Grace: Debugging: unlike a drawing project where you can try things step by step and get immediate visual feedback to identify mistakes, this task requires completing everything before checking if it works. This makes me feel less confident because I don’t get direct feedback during the process.
      • Print statements
      • Building your own tools...
  • Pedagogy
    • I do want groups to struggle.
    • You're going to run into hard problems--what's the experience going to be like?
  • Extensions
    • Seoyeon: What about box and whisker plots?
    • Stacy: Clustering algorithm (computational creativity)
      • Maybe using AI will be like this too.

Pandas

  • New interface; data science.
  • Literate programming.
  • It does for us what we've been doing.
  • Higher-level / lower level thinking

Argument project

The data science unit culminates with the argument project, in which you will analyze a data set of your choice, answer research questions, and present them in an argument. We have developed a teacher's guide for the argument project, focusing on the potential of this assignment for connecting to students' out-of-school interests and identies, and on critical learning in the classroom.

You may want to start thinking about which data set you want to analyze:

  • You could use your own generated data, using extraction tools we have developed. For example, you could work with your Google Maps location data, your Netflix or Spotify media consumtion data, or records of your emails or text messages.
  • You could use an external data set of your choice. I can help you find and evaluate external data sets.
  • You could use a research data set you are going to analyze anyway. (e.g. doctoral students might want to work on data they are already studying; teachers might want to study data from their classrooms.) If data privacy is a concern, we can work out an arrangement where the raw data is never shared beyond your control.