Since the dawn of data mining (beginning with Bayes Theorem in the 1700s), there have been many successes and failures, even by the top experts in the field. No matter what job function or industry you work in, it is generally agreed on that on-the-job training is a far better learning tool than any classroom lecture. Learning from our mistakes is one of the ways we move forward and accomplish our goals. The same goes for data mining practitioners and data scientists; hands-on experience (or lack thereof) results in victories and blunders that set the foundation for advancements in the field.
The author asked a few data scientists who know to offer examples of their own do’s and don’ts from their real-world experience working with data, consulting, product development, and professionalism in the field.Don’t forget to share them with your colleagues who are committing the DON’TS of data mining!
Reference: The Do’s and Don’ts of Data Mining