Maybe the business world has jumped the gun with all the talk about a looming skills shortage in big data and advanced analytics. There’s mounting evidence that it doesn’t take much to turn a novice programmer or statistician into a perfectly capable data scientist. Maybe all it takes is just some cheap cloud computing servers, or a few weeks studying machine learning with Stanford professor Andrew Ng on Coursera.
Much of this evidence comes via Kaggle, a platform where companies and organizations award prizes for the best solutions to their predictive-modeling needs. In September, for example, I covered a first-time Kaggle user and admitted data science neophyte named Carter S. who won a competition using a simple but effective method he dubbed “overkill analytics.”
To read the original article: Why becoming a data scientist might be easier than you think — Data | GigaOM
