Shortly after graduating college in 2012, I decided to learn violin, which is a very demanding instrument. My hobbyist knowledge of guitar and the piano did help, but not much. I used to go to a class for 2 hours every week, which seems very little. It seemed as if it’ll take forever to learn anything decent to play.
Fast-forward about 2 years, by which time I had won proficiency awards and had done a group public performance during festival season. All this while, I was also learning the flute, in which I also got several proficiency awards and got to do a group public performance.
Was our training intense? Well, violin does require intense practice. However, our lessons were to the point. Once you have a great teacher, he will guide you to practice things that really are important, and ignore a million other things that are doable but not worth doing. Prioritization was the name of the game.
The intensity was not because we did a lot of things under serious time constraints, which is what we irrationally keep doing nowadays when we want to learn anything new. But it was the deliberate, consistent, concentrated, and reasonably manageable efforts that we put every day towards an exercise.
This syndrome of trying to cram in a lot of material is especially visible in this “machine learning & data science” age. You see tutorials and courses that will teach you everything under the sun about ML, but you will actually not understand any of it, nor will you able to adapt these learnings to your situation.
I recently read The Compound Effect by Darren Hardy and it gives you a lot of examples into this core idea of doing small things consistently, rather than going after a windfall.