The truth shall make you mad: Things I've observed learning data analytics

 Easter 2021

Data Analytics experiments and exploration

I feel like this is a good direction for me career-wise: right on the border between smart and creative. I'm enjoying making graphs and chumming data into things we can learn from.

I've been particularly interested in wealth disparity and it's effect on our economy overall. I made this with Flutter:

 

Making it as a desktop app, too. Flutter 2 is pretty exciting at this point.

And I made this with matplotlib via Python, with pandas handling the data:

ETA: the above uses GDPs as a comparison to wealthy individuals, but it was recommended I use the Credit Suisse/IMF estimate of actual wealth in nations as listed here. And so:

ggplot2 is great, too. For some reason, when I make the graphs it ignores the specified order. ETA seems to be fixed with the revision.

R is mainly for true scientific use, though, while Python is for everyone. So there's that.

The nations' GDP info is readily available as a free download. To get the billionaires' list, I had to copy/paste (via lots of scrolling) into the Atom editor, regex the giant horrible blob of text into comma separated data, transport that to a JSON conversion tool online, clean with regex more...hmm, wonder why they don't do that for us?

But both are available for you tor review and learn from. You know where it is.

Fun facts from the command line:

The cumulative wealth of the 659 American billionaires is $4.3 trillion USD.

The cumulative wealth of the 478 American multi-billionaires is $4.034599 trillion USD.

The cumulative wealth of Africa is $4.7 trillion USD.

The cumulative wealth of the richest 1000 human beings on earth is $9.839 trillion USD.

The cumulative wealth of all of Latin America is $9.8 trillion.

What data interests you the most? I would love to be smart enough to handle the tons of data that can help us combat climate change or help our vulnerable get off the streets and live with dignity. I think those are good reasons to be in data.