At our School of Tomorrow, we use tomorrow's learning methods today. One of those methods: create a new Python3 Jupyter Notebook from scratch and start filling in (a) Markdown cells and (b) code cells about a topic you want to know more about.
The premise is, if you're learning Python, here's a excuse to practice. If you already know Python, might you still learn more matplotlib? I certainly fall into this category. I'm pretty deeply into Python, including metaclasses and asyncio, but the APIs for these data science objects are somewhat gargantuan, not to mention alien. However I wade in, armed with my Python knowledge.
Beyond the nuts and bolts of data visualization, what do I really want to know about. In The School of Tomorrow, we focus on syntropic versus entropic processes. Syntropy is a local phenomenon, against a backdrop of Expanding Universe.
We might credit the sun for Planet Earth's yearly funding. Give us this year our yearly solar gradient. In this Youtube I look at making food last longer, even though it's in the process of decay. I also look at irradiation as fomenting decay versus postponing it. It's all about dose and effective dose (which we're at pains to distinguish).
Again, if you're a student with the school, think about creating that Github account or something similar and start getting those Jupyter Notebooks out there. Or just serve them locally on localhost. Either way, making them yourself is the process to engage in. Treat my example as a model to imitate and improve upon.
