The world’s most valuable resource is no longer oil, but data. As a result, the skills to convert data into insights that generate revenues are high in demand. Unfortunately, one of the biggest hurdles companies face when trying to capitalize on their data sets is a lack in the supply of Data Scientists and Machine Learning practitioners. These are professions which require a wide array of specialized skills ranging from statistics, probability, and linear algebra to computer science.
As markets struggle to supply the necessary talent, a few companies are working on a new paradigm: automated data science. The gist of the concept is that many or all aspects of data science and analytics can be automated. Algorithm selection, for example, no longer requires that one understands the difference between logistic regression and random forest, a machine can simply figure out which type of algorithm fits the data best. The rise in automation has even led to the development of a new type of data professional: “the citizen data scientist”. An employee who can perform analytic tasks that would previously have required the expertise of a highly skilled data scientist.
This session will cover some key questions regarding automated data science: which parts of data science benefit the most of automation, can all of data science be automated, and will there be a place for data scientists in the future as well as how companies can benefit the most from automation.