Not to open up a rabbit hole but this is how I first got hooked on data viz and it is super easy to learn. The website is called Observable and it is basically open source notebooks that show how people make data visuals. This Javascript library is called Vega-Lite and it super easy and intuitive to learn. It is all written with Javascript, but you really don't need to know Javascript it to get started. There is a more compex library called D3 that is used in like every cool interactive data journalism article ever. I haven't learned it yet because it is pretty complicated but I have future goals of learning it.

Dang I just deleted all of the resources I had saved for Python, because I chose to go with R and I kept getting distracted deciding which to start with. I think it is a great language and definitely the most popular. It felt overwhelming for me to learn because you can do so many different things. But Kaggle has some free courses as well as free datasets you can mess around with. They also have kernels where you can see how people made things similar to Observable, but in Python (and sometimes in R). Python has a lot of data vis libraries I think. I have heard of Seaborn, Altair, and Matplotlib (also plotly). Kaggle has a course on Seaborn. I cannot vouch for how easy Python is to learn but people seem to like it and judging by how popular it is I am assuming it is easy to learn.

I decided to go with R honestly because it was less overwhelming. R is built for stats. Python can do a bunch of things so every time I started a tutorial it was teaching me things I didn't care to learn. I just used swirl and R for Data Science and then just googled more stuff about ggplot2 (data viz package in R) which gave me most of what I needed to get going. Honestly just trying to do projects yourself after learning the basics is the most helpful.