Developing a predictive model is only one part of a larger journey. Data scientists have to access and transform data, and engineer features, before exploratory modeling happens. A model doesn't do anything until it's applied to data, productionised and deployed.
3 Things to Learn About:
How to uplevel your existing analytics stack with a collaborative environment that supports the latest open source languages and libraries.
How to get better use of your core data management investments while opening up new supported tools for data science.
How to expand data science outside of silo’d environments and enable self-service data science access.