The domain of data science has been at the focal point of discussion for quite a few years now and there are no signs of it slowing down. As more and more businesses, organizations, and companies are waking up to the importance of extracting important insights from the pile of data that they are sitting on, the demand for data scientists, data engineers, and other experts in the field has increased significantly. No wonder that while there’s an increased focus on bringing such data science talent onboard, a whole new set of data science titles and roles too have been created to address the needs of the market.
These days, the business world runs entirely on data and none of the companies can survive without data-driven strategic plans and decision making. The field of data science is quite broad and contains a significant number of job positions including data scientist and data engineer. If you want to step into the data science field, it’s crucial to understand the differences between a data scientist and data engineer to identify whether it’d be possible for you switch positions without investing much effort and time.
Though having a computer science background or a degree would help you become a data scientist, it’s not a must-have thing if you’ve got your eyes set on a career in data science. According to DJ Patil (who became the first chief data scientist of the US in 2015), data science doesn’t care about if you’ve got a degree or what you majored in. The thing that really matters is what you do with data.