Data science professionals have a difficult, yet rewarding job: analyzing multiple databases of structured and unstructured data to help create a product and improve services. If you’ve ever worked at an organization with multiple shared folders for collaboration, you have an idea of how messy data can become. Now multiply your internal database by a factor of 1,000, and you have an idea of what data science professionals need to digest, reorganize, and package to build a product or service. One of the most important tools in the arsenal of a data science professional in dealing with large datasets is SQL.
