The most basic definition of data science is that it involves the collection, storage, organisation and analysis of massive amounts of data. Yet without a deeper understanding, one might think a data scientist sits in a dark room, huddled in front of a screen, pouring over streams of digital content.
That’s not the case at all. In fact, in recent years data science has become less and less about the data itself, and more about the technology and tools used to interact with it. Advanced solutions like AI, machine learning and robust analytics tools make it possible not just to process and understand massive stores of information but at unprecedented speeds. The tools are so powerful you don’t need to know how to code to use most of them — it always helps to have programming experience, however.
Today’s data scientists now take the data that’s been ingested and processed by an advanced analytics system and translate the information for the rest of an organisation. For example, they might point out trends to executives, who will use the information to make more informed decisions. They might pass details about customer behaviour on to marketing for building more targeted and successful campaigns. Or, they may decide what forms of data to filter into a storage system and, of course, there’s also the adverse — deciding what stored information to put to use.
