Don't know about 2022, but in 2020 the fast.ai video series was a good way to get started. It has been revised a few times since then, so chances are it is still good.
If you want something more theoretical there is a book by Hopcroft et al. that was released in draft form a number of years ago. It appears to be out for real now: Foundations of Data Science, by Avrim Blum, John Hopcroft, and Ravindran Kannan. Blurb and video lectures: https://www.microsoft.com/en-us/research/publication/foundat... I just found these so haven't looked at them yet. The book draft (2014, wow) is here: https://www.cs.cornell.edu/jeh/book11April2014.pdf I didn't stick with it long enough to make much progress, unfortunately.
Kaggle.ai problems are a good set of practical projects even if you're not aiming to be competitive at them (which takes a lot of effort and resources). The Fast.ai vids are ok as preparation for them.
If you want something more theoretical there is a book by Hopcroft et al. that was released in draft form a number of years ago. It appears to be out for real now: Foundations of Data Science, by Avrim Blum, John Hopcroft, and Ravindran Kannan. Blurb and video lectures: https://www.microsoft.com/en-us/research/publication/foundat... I just found these so haven't looked at them yet. The book draft (2014, wow) is here: https://www.cs.cornell.edu/jeh/book11April2014.pdf I didn't stick with it long enough to make much progress, unfortunately.
Kaggle.ai problems are a good set of practical projects even if you're not aiming to be competitive at them (which takes a lot of effort and resources). The Fast.ai vids are ok as preparation for them.