> - Arithmetic w/ `assert`: addition/subtraction, multiplication/division, exponents/logarithms; and then again with {NumPy, SymPy, Lean Mathlib}

"How I'm able to take notes in mathematics lectures using LaTeX and Vim" https://news.ycombinator.com/item?id=19452752 ...

"How should logarithms be taught?" https://news.ycombinator.com/item?id=28519356

Any good recommendations for starting points to learn how to work with these? I'm not a programmer, but do have programming ability, and use excel all the time. Having a more flexible system with better inputs (tying in web scraping, data pipelines, etc) sounds like a great idea.

Kaggle learn: https://www.kaggle.com/learn

"Python Data Science Handbook" (O'Reilly) http://jakevdp.github.io/PythonDataScienceHandbook

awesome-python#web-content-extracting: https://github.com/vinta/awesome-python#web-content-extracti...

https://learnxinyminutes.com/docs/python/

"What is the best way to learn to code from absolute scratch?" https://news.ycombinator.com/item?id=16299880

Try https://colab.research.google.com/

> - Growth curve fitting; data modeling with linear and better models

Input: at least a two-column table

Output: a symbolic (or NN) model with minimal error in predicting the test half of the data after a `test_train_split()`-like routine.