Data Science for All: What is it? Why care? How do I get it?
Organizations everywhere, from massive governments to the smallest start-ups, are in a race for the best-possible data expertise and tools. To help your team understand the data science journey, IBM created the Data Science for All webcast.
Rob Thomas, IBM VP of Analytics, and Katie Linendoll recently hosted the live broadcast to talk about the IBM approach to enterprise data science. The panel within the broadcast discussed the problems faced by businesses trying to find solutions around consuming data, and practicing data science with that data. What was clear was that enabling data science is becoming increasingly vital as an asset to every company, and in every industry. FiveThirtyEight’s Nate Silver spoke about that importance, the value that data science provides in informing his own writing; where he covers topics across sports and politics.
Insights from renowned data science experts
Why data science? Rob and Katie spoke at length about the fact that the need for everyone to become data literate has already arrived. The leaders of what Galvanize Director of Data Science Nir Kaldero dubs the "Fourth Industrial Revolution" will be those who have embraced data as an asset and data literacy as a practice. But, with more than 80% of consumable data stuck behind the firewall, the enterprise machine learning space needs help. Data analysts, machine learning engineers and data scientists need a multi-cloud workbench solution. These separate functions need to work together, and data science needs to become a team sport. IBM Data Science Experience is the workbench that enables that collaboration across all teams, all data, all tools and all deployments.
Data science for all with IBM Data Science Experience
How do you get data science into your analytics pipeline? The IBM Data Science Experience release is the team-wide solution to collaborative machine learning. It’s a tool set designed to appeal to data scientists, with its open-source project compatibility and ease of deployment, but also designed to enable the non-coder; who doesn’t know languages like Python, Scala, or R. It is designed for all tools, and all teams. Integrating data science practices becomes easy with IBM Data Science Experience.
Also featured within the release announcement: IBM has pushed out tight integration with the leader in the big data and enterprise Hadoop — Hortonworks. Now there is an enterprise solution for utilizing those vast lakes or dark data. With IBM Data Science Experience, not only can companies explore their on-premises data, and build their machine learning models behind their firewall, they also can publish those models for consumption on a public cloud. It’s built for all data, and all deployments.
It’s an exciting time to start pushing your company into the data science field. See the panel’s in-depth discussion of the importance of organically integrating data science into company culture, and Rob’s demo of the latest release of IBM Data Science Experience at the IBM Data Science for All event page, or visit our homepage, datascience.ibm.com.