In this special guest feature, Rick Chavie, CEO of EnterWorks discusses several areas where all businesses can make marked improvements and avoid the big data pitfall of having great insights that can’t be used in a tactical way.
Published spend transparency data requirements were introduced via the Open Payments program, as mandated in the Affordable Care Act, to raise awareness of the financial influence that drug and device manufacturers have on the so-called covered recipients (physicians and a defined set of teaching hospitals).
WPI Receives U.S. Dept. of Education Funding to Address Shortage in Big Data Computing Professionals
Addressing a critical need for enhancing the nation’s capacity for computer science research and teaching, the U.S. Department of Education has granted Worcester Polytechnic Institute (WPI) $885,834 through its Graduate Assistance in Areas of National Need (GAANN) program. The funding will provide six needs-based fellowships for computer science PhD students who will study big data computing.
In this special guest feature, Eric Tilenius, CEO of BlueTalon, highlights the four main reasons why companies should adopt data-centric security.
This article is the third in a series that explores a high-level view of how and why many companies are deploying Apache Spark as a solution for their big data technology requirements.
The talk below by CTO Bart Peintner of Loop AI Labs was presented at the Deep Learning Summit in Boston on May 26, 2015 and coincides with the launch of the Loop Cognitive Computing Platform.
IBM (NYSE: IBM) today announced that its machine learning technology –SystemML –has been accepted as a project by the Apache Incubator open source project. Originally developed by IBM Research, and now used in IBM’s BigInsights data analytics platform, SystemML is a machine learning algorithm translator.
In this talk, Xiangrui Meng of Databricks shares his experience in developing MLlib. The talk covers both higher-level APIs, ML pipelines, that make MLlib easy to use, as well as lower-level optimizations that make MLlib scale to massive data sets.
In this special guest feature, Bobby Koritala, Chief Product Officer of Infogix, discusses data management best practices and why data quality without data integrity is no match for today’s business demands.
When one of my favorite independent tech book publishers, No Starch Press, notified me about their new title “Doing Math with Python,” I was energized to review what potentially could be a good new resource for budding data scientists.