The job opportunities for data scientists are numerous because the data science world keeps on growing. It’s not surprising that most ambitious data scientists who have just graduated or they have changed their career are getting recruited by many companies.
With amateur data scientists taking up the roles, there is an inflow of mistakes happening in businesses as they are adjusting to the new workplaces. Here are 10 common mistakes that amateur data scientists are always doing. 1. Focus on theory Amateur data scientists have a great grasp of a theory which provides a good foundation when you are working. However, most of them don’t know how to apply this theory resulting in people who have plenty of information which has no use.
Author: Kurt Walker