A few years back, when I started my journey in data science, I was fascinated by Kaggle. I used to devote my time there. I was overwhelmed by the knowledge and problems. I wanted to be competitive and kaggle shows their leaderboard. You win and make a name. I used to have casual discussions with my manager on a variety of topics. One day I asked my manager which things are most important in data science and what’s his opinion about Kaggle.
He said “Problem Solving is one of the most important skills in data scientist. As far as kaggle is concerned I dont think Kaggle helps you to become a good problem solver.If you know Xs and Y then almost anyone can solve the problem. Most important part is defining your Xs and Y“
After some time I gave a thought about it and he was right. In my experience, most of the problems I solved was the result of the way problem defined. Most often, Its almost never a question to build the most accurate model, rather it is solving the problem. Kaggle comes in handy to know different approaches to solve the problems. But its not critical to your success in data-science career.
Read the below blog to get more perspective on this
Stitchfix: comfortable with ambiguity and successfully framing problems
d4datascience.com 