The debate over which statistical platform sits premiere over the others for data science applications rages on. The discussion often turns to the popular R and SAS environments. But to focus the dialog on performance only, a new benchmark study was just completed by commercial R provider Revolution Analytics.
Saffron Technology has been on a quest since 1999 to replicate the way the human brain learns using associative memory. Saffron is now commercially available as a cognitive computing platform following beta testing for real-time operational risk intelligence and decision support in defense, energy, healthcare and manufacturing applications.
I am really looking forward to attending my first useR! conference – coming to Los Angeles June 30-July 3. I’ll be at the big show to cover all the late breaking R news and report back to you all here. This annual conference jumps around the globe, alternating between the U.S. and Europe (last year’s conference was held in Spain). This year the conference will take place on the campus of the University of California at Los Angeles (UCLA).
Deep learning is a new force in the field of data science – a set of algorithms in machine learning that attempt to model high-level abstractions in data by using architectures composed of multiple non-linear transformations. The talk below breaks the ice for those not familiar with this technology. This talk was presented at the SF Neural Network Aficionados Discussion Group hosted by NextSpace in San Francisco.
“Some c-suite executives have quickly discovered the power of analytics, while others were more reluctant to embrace it. We see companies that embrace analytics across the enterprise as being more successful than their competitors. These observations have also been supported by academic studies that conclude that analytic oriented and metric driven organizations outperform those that do not embrace analytics holistically.”
Seventy-five percent of businesses have yet to successfully deploy big data analytics solutions to gain business-impacting insights, despite 65 percent increasing their investment in analytic services and technologies in 2014. These findings are part of “Analytics 2014,” Lavastorm’s second annual survey on analytic usage, trends, and future initiatives.
A very hot topic in data science these days is the ability to discern “sentiment” by analyzing text from social media sources. In the talk below, Ryan Rosario presents some of his work at Facebook – “Sentiment Classification Using scikit-learn.” Ryan is a Quantitative Engineer at Facebook.
“AgilOne is a cloud-based predictive marketing platform that makes advanced analytics and predictive targeting easy for all marketers large and small. We believe all marketers have the right to deliver happiness. Unlike offerings from large vendors such as IBM and Adobe, AgilOne is easy to install and easy to use. It is the only integrated platform that combines data integration (omni-channel customer profiles) with advanced predictive analytics and out of the box predictive marketing campaigns (actions).”
For up-and-coming data scientists who need to get up to speed on Hadoop architectures, here is another in a long line of compelling Big Data & Brews episodes. In the video below we hear from three Hadoop luminaries about the Hadoop projects they’ve worked on – Erich Nachbar on Spark, Michael Stack on Hbase and Ari Zilka (from Hortonworks) on Stinger. Great insider’s perspective!