ltj

- teaching: https://docs.leontoddjohnson.com/docs/teaching/
- philosophy: https://docs.leontoddjohnson.com/docs/teaching/philosophy/
- courses: https://docs.leontoddjohnson.com/docs/teaching/courses/
- python: https://docs.leontoddjohnson.com/docs/teaching/courses/h501/
- stats: https://docs.leontoddjohnson.com/docs/teaching/courses/h510/
- data vis: https://docs.leontoddjohnson.com/docs/teaching/courses/h517/
- math: https://docs.leontoddjohnson.com/docs/teaching/courses/h611/
- methods: https://docs.leontoddjohnson.com/docs/teaching/methods/
- metacognition: https://docs.leontoddjohnson.com/docs/teaching/methods/metacognition/
- assignments: https://docs.leontoddjohnson.com/docs/teaching/methods/assignments/
- ungrading: https://docs.leontoddjohnson.com/docs/teaching/methods/ungrading/
- coding: https://docs.leontoddjohnson.com/docs/coding/
- moments: https://docs.leontoddjohnson.com/docs/coding/moments/
- movements: https://docs.leontoddjohnson.com/docs/coding/movements/
- swons: https://docs.leontoddjohnson.com/docs/coding/swons/
- tutti: https://docs.leontoddjohnson.com/docs/coding/tutti/


# intro to data science programming

My aim with this course is to give students experience with Python in the context of Data Science.

\[Link to course webpage in progress as IU migrates its online assets to an updated platform.\]

## tools

Tool | Note
-- | --
[Jupyter](https://jupyter.org/) | Weekly labs are hosted on Jupyter notebooks, and students are encouraged to use these to test out code for their projects, or just try out/learn from the code in the weekly notebook.
[Miniconda](https://www.anaconda.com/docs/getting-started/miniconda/main) | Since the Anaconda distribution gets bloated easily, students build pip environments using Miniconda for this class.
[GitHub](https://github.com/) | Give students real-world experience with version control. This is useful for project group work, weekly exercises, and it's helpful for me and TAs to track *individual* students' progress.
[Streamlit](https://streamlit.io/) | Introduce students to end-to-end development for data science models.
[Gradescope](https://www.gradescope.com/) | This allows me to autograde weekly exercises a bit easier. Students turn in their GitHub repositories — each week, they see a new way to incorporate what they learn into the "data science development pipeline".
[Docker](https://www.docker.com/) | Right now, this just provides the framework needed for the exercise autograders (e.g., Gradescope). Though, in the future, I intend to incorporate this into the course curriculum[^fn:docker].

[^fn:docker]: Docker is a very widely used tool in the tech industry, and thus an incredibly valuable skill to have as a data scientist. But, from what I can tell, it's often undervalued in higher-ed.