Data Science is no longer just the purview of academic conferences and scholarly papers, it is now a significant movement within Data Management as a whole and has numerous benefits that are now only being understood. According to Daniel Gutierrez at InsideBigData, “Eighty-nine percent of corporations believe that not leveraging big data will result in lost market share, a study from Accenture and General Electric found. So as more industries hire data scientists, they’ll be moving away from IT-focused positions and into specialized roles making everyday objects smarter and fine tuning cutting-edge technology. In fact, the infamous Google Self-Driving Car Project is powered by machine learning that allows autonomous cars to differentiate an exit from a ditch or a child from an adult. Similarly, data science applications will spread to industries like energy forecasting and geopolitics.”
He continues with, “Deep learning techniques will become integral to data science. Deep learning makes it possible to teach systems to recognize images or understand spoken language. It also provides multiple representations of underlying data, generating new ways of predicting and informing behaviors. That’s why this subset of machine learning is a natural addition to data scientists’ toolkits. Data scientists will use deep learning to automate the process of feature extraction and uncover patterns in data that might have gone unnoticed. Consequently, deep learning tools will become widely available as turnkey solutions. Case in point: In November, Google open sourced its artificial intelligence engine, TensorFlow, which features built-in deep learning support.”