In the last post that I wrote about the Dreyfus model, I’d promised I would come back here and tell you about some techniques to evolve in the model scale and here I am to do that, but firstly I would like to thank the people that gave me very good feedback in a direct and indirect way - by commenting and sharing my post. For an advanced beginner at blogging, the feedback is amazingly rewarding. Thank you!

Before going into deliberate practice I would like to state something: scientific research shows that expertise in any field comes from hard work and practice, not from innate talent. In almost every case of famous human expertise, at least a decade of hard work was needed in order to reach world-class skills. However, hard work isn’t enough - the journey to expertise requires smart work, aka deliberate practice.

“Learning is not compulsory… neither is survival.” (W. Edwards Deming)

I was inspired by the book “Pragmatic Thinking and Learning” by Andy Hunt that I’ve read not so long ago and so I decided to write this post which you may say it is almost a review of this book. My intention with this is to show you how you could improve your ability of learning, which is required specially to software developers considering that we need to learn constantly not only technical stuff but also problems domains, and doing this is extremely hard and tiring. I personally think that the common developer (and may not be only developers) overlook this topic. Learning is a skill as programming and riding a bicycle is and so it deserves attention, training, reflection, etc.

It’s simple: because it might make you a better programmer.

I’m attending the Algorithms 1 course on Coursera, from professors Robert Sedgewick and Kevin Wayne, and so far it’s been great. It’s nice to remember and improve my knowledge in algorithms while coding solutions to unusual problems like the 8-puzzle problem (solved using the A* algorithm), and all of that for free! :-) This post is inspired on one of the applications of priorities queues: event driven simulations.