There is a vast array of predictive analytics tools, but not all are created equal. Software differs widely in terms of capability and usability — not all solutions can address all types of advanced analytics needs. There are different classes of analytics users — some need to build statistical models, others just need to use them.
For this segment of insideBIGDATA Data Science 101, we have a very compelling Google Tech Talk “Building Brains to Understand the World’s Data” presented by Jeff Hawkins, co-founder of Numenta and who also founded Palm and Handspring.
This article is the third in an editorial series that will review how predictive analytics helps your organization predict with confidence what will happen next so that you can make smarter decisions and improve business outcomes.. It is important to adopt a predictive analytics solution that meets the specific needs of different users and skill sets from beginners, […]
The need for predictive analytics in the enterprise is clear, as it can provide smarter analysis for better decision making, increased market competitiveness, a direct path in taking advantage of market opportunity and threats, a way to reduce uncertainty and manage risk, an approach to proactively plan and act, discovery of meaningful patterns, and the means to anticipate and react to emerging trends.
R is a widely used statistical programming language but its interactive use is typically limited to a single machine. To enable large scale data analysis from R, SparkR was announced earlier this year in a blog post. SparkR is an open source R package developed at U.C. Berkeley AMPLab that allows data scientists to analyze large data sets and interactively run jobs on them from the R shell.