A data scientist uses machine learning (ML) to find heretofore unknown correlations and other patterns in fresh data. ML is adept at finding both the "known unknowns" and the "unknown unknowns" through the power of supervised learning and unsupervised learning methodologies, respectively.
A living subject domain is conceptual territory that must be scouted continually. Even if you're long familiar with the domain's heartland (from study, reading, work history, the School of Hard Knocks or other firsthand experience) a subject's frontiers may have shifted while you weren't paying
It’s hard to have a conversation about big data without talking about Hadoop. Sure, it can be done. You can discuss how big data is all data, how big data without analytics is just “same ol’ data”, or how the implications of governing big data are even more severe than in a traditional environment
This is our sixth post in a series of seven presenting the findings from the IBM Institute for Business Value and University of Oxford’s Big Data study, “Analytics: the real world use of big data in financial services.”
Analysis of the findings by my IBM colleagues David Turner, Michael Schroeck
This is our fifth post in a series of seven presenting the findings from the IBM Institute for Business Value and University of Oxford’s big data study, “Analytics: the real world use of big data in financial services.”
As part of this recently published global research study, my colleagues David
In June, close to 80 developers in the Silicon Valley area attended the free IBM Big Data Developer Day event. It was a great chance for developers to learn about the latest IBM technologies for addressing big data challenges. Not only did they gain valuable information from the sessions, but they
Jimmy Kimmel pulled off an incredible prank during last Sunday night's Primetime Emmy award show. He got comedian Tracy Morgan to lie flat onstage and asked his audience members to tweet “OMG Tracy Morgan just passed out onstage at the #Emmys. Turn on ABC now.”
There was something fascinating about
USC Annenberg Innovation Lab is using an IBM big data solution to conduct sophisticated social media analytics and natural language recognition to gauge positive and negative opinions shared in millions of public tweets. The project has been applied to political debates, sporting events, movies and
Text analytics is a key feature of IBM's big data platform. In part one of this two-part series, Vijay Bommireddipalli, a Big Data Solution Architect, gives us an introduction to text analytics and IBM's capabilities in this area.