At Spark Summit 2016, in San Francisco, IBM unveiled its Data Science Experience offering, which aims to educate and bring together the growing community of data scientists.

So far so good. In the two days after the announcement, nearly 700 people signed up according to Ritika Gunnar, VP of Offering Management, Data and Analytics, at IBM. Gunnar sat down with John Walls and George Gilbert (@ggilbert41), cohosts of theCUBE, from the SiliconANGLE media team, during Spark Summit 2016.

Why would IBM create this Experience? “We wanted to create a place where those professionals can come and understand what it means to be a better professional and to allow data to work for them,” says Gunnar.

“We found that the number one gap was in skills. If you look at the way tools have transformed from proprietary to open source, the number of languages, tools, and capabilities that you need to have has grown enormously. We want to help existing scientists learn about new technology and help aspiring data scientists learn what it means to get better.”

Learning is a key component of the Data Science Experience. And many data scientists need to broaden their skills to stay relevant.

Gunnar says there are four skills necessary for any data project:

• Data scientist
• Data engineer
• Business analyst
• Application developer

Gunner says that today’s data scientist may take on more than one of those roles. “To be efficient, you need to have multiple skills.”

Watch the full interview below, and be sure to check out more of SiliconANGLE and theCUBE’s coverage of Spark Summit 2016.

Photo by SiliconANGLE