Jupyter notebooks and the intersection of data science and data engineering

David Schaaf explains how data science and data engineering can work together in cross-functional teamswith Jupyter notebooks at the center of collaboration and the analytic workflowto more effectively and more quickly deliver results to decision makers.

In today’s world, data science has become a catch-all for much of the work done in business analytics, statistics, and data engineering. In addition, the problems data scientists face continue to grow in scope and complexity, making it ever more challenging to deliver business value with agility. As a result, companies increasingly expect a lot of their data scientists. However, the reality is that there are few people with both the deep modeling and analytics skills and the engineering expertise required to deliver advanced analytics in production. So how can companies solve for this? David Schaaf explains how data science and data engineering can work together in cross-functional teams—with Jupyter notebooks at the center of collaboration and the analytic workflow—to more effectively and more quickly deliver results to decision makers.