I do not like "steep learning curve" the way people use it. It raises my probability estimate that person using it has done no study of learning. This reduces my probability estimate that they have sound insight about learning.
Sometimes I work with learning curves for machine learning or human learning. These curves are plots of a measure of learning (say, correct score, or number of mistakes) versus time or number of trials or number of training examples or number of iterations. When topic is hard, curve is shallow! It takes more learning to improve score or reduce mistakes. Steep learning curve means topic is easy. Mastery comes with very few repeats or little time or little data.
Does anyone know why so many use it wrong, to mean topic is hard and progress is slow? I believe Wikipedia article is right and "learning curves" must come from scientific study. How did meaning get reversed? Wikipedia article says "Arguably, the common English use is due to metaphorical interpretation of the curve as a hill to climb." but has no citation.
The first post didn't use it only to mean progress is slow. Version control software is no subject that takes much time to learn. The problem is that if you want to use it for the first time, it takes time to wrap your head around it before you can use it productively.
I don't think the progress on learning R is much slower than the progress on learning Excel but learning R is harder. You can use Excel while have relatively little skill at using Excel.
Climbing a steep mountain takes more effort than climbing a mountain that isn't step. It doesn't necessarily take more time.
This is the question asked by John Cook on Twitter. He lists responses from different people:
Mine are: quantum mechanics, Python, cooking, the language of philosophy.
What learning curve do you wish you'd climbed sooner? Give reasons and stories if you feel like it. Do you think other people should climb the same curves?