One thing a lot of beginning programmers stress about is deciding which programming language to learn. Today the big choice in working with data comes to Python or R
My Books teach you Python, along with libraries Pandas (manipulating data), BeautifulSoup (for scraping), seaborn (data visualization), and statsmodels and scikit learn (modeling and machine learning).
R is a statistical programming language and an alternative to Python. You can basically think of it as Pandas + a few other statistical libraries. It's a fine option. I started out in R, and am still moderately fluent. It's been around longer and has a solid MLB/football analytics following. Like Python, it's open source (e.g. free).
When it comes to data analysis, Python and R are a lot alike. Python probably has the edge in machine learning with scikit-learn, and Pandas is excellent (and getting even better all the time). R has the tidyverse, which a lot of people like.
For non-data applications Python is definitely better. People use Python to build their own APIs and websites, run their own servers, control their computers, robots, whatever.
That's probably why until about 5 years ago the R/Python split was about 50/50, but since then Python is the much more popular option (search for "R vs Python market share").
Also interesting is this study of programmer migration patterns by programmer blogger apenwarr. He found that R users are much more likely to jump to learning Python next than the other way around.
As apenwarr explains, nodes in red below are "currently the most common 'terminal nodes' — where people stop because they can't find anything better".
Python 3 (which what we learn in LTCWFF) is a terminal node. R users (and Matlab, Fortran, etc) tend to migrate to it over time.
So if you're going to end up moving from R -> Python anyway (or, in my case SAS -> Stata -> R -> Python), it might make sense to save time and start directly with Python? If this sounds intriguing definitely check out the books: