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Creating new tools to bring together the data science community | #BigDataNYC
Data science is a land with strange and shifting borders. It’s hard to say what makes a data scientist, as the skills required vary from one project, and one company, to the next. Further, the tools and technology involved are changing as quickly as anything else in the computer world. Bringing some definition and stability to the data science community is a necessary step in the evolution of the field.
To gain some insight on the world of data science, Dave Vellante (@dvellante) and Jeff Frick (@JeffFrick), cohosts of theCUBE, from the SiliconANGLE Media team, visited the BigDataNYC 2016 conference in New York. There, they talked with Armand Ruiz Gabernet, lead product manager, IBM Data Science Experience, at IBM.
Introducing the Data Science Experience
The conversation started with a look at a new tool developed by IBM, the Data Science Experience. Gabernet explained that IBM had seen a big gap in the tools used by data scientists, and difficulties in getting those tools to work together. The company thought to create something new, a system with a clean, nice UI based on open-source code. This became the Data Science Experience.
The Data Science Experience features a big community component. Gabernet pointed out that a big part of data science work was going online to find the most recent information and solutions. Now, IBM brings it all in through this new tool. He stated users can start working and collaborating with one click.
Collaboration, science and community
Gabernet mentioned the big question surrounding the field: What is data science? The concept is evolving. Companies have teams of data scientists with different skills. IBM is trying to bring them all into one platform.
“We had the feeling we were doing the right thing, but we’ve had it confirmed by the community,” he said.
To be a data scientist today is hard because you have to learn new stuff every week, Gabernet explained. It’s hard to keep up. There is automation, but the machine learning process is never-ending. Data scientists are always coming back to improve their accuracy. The process is endless, and they’re never finished in their work, he added.
Watch the complete video interview below, and be sure to check out more of SiliconANGLE and theCUBE’s coverage of BigDataNYC 2016.
Photo by SiliconANGLE
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