Data science is a team sport. It requires well-trained, skilled individuals collaborating efficiently to play well and ultimately win. Unlike most team sports though, the equipment that is used to do analytics is all but uniform nowadays. Each player often uses what makes them perform best as an individual. When required to collaborate in teams, there is inherent tension between code-based data science, often performed by highly skilled data analytics professionals leveraging coding languages like R or Python, and code-free visual approaches being used by citizen data scientists. For enterprises, this is both an opportunity and a challenge.
RapidMiner provides excellent support for both data scientists and citizen data scientists to develop and deploy machine learning using a visual, code-free approach. RapidMiner also embraces the co-existence of code-free and code-based data science and supports collaboration across both approaches. This whitepaper discusses RapidMiner’s current capabilities in this regard and outlines our future plans to provide a closer integration of code-free and code-based data science approaches. This integration will empower the individual users, make them more productive, and ensure compliance to organizational needs in governing data science initiatives at the same time.