Playing Dominoes In Data Science

#artificialintelligence 

The growing amounts of data that are being generated due to such trends as the Internet of Things (IoT) and cloud computing have naturally beget the need for data scientists who can collect, analyze and, most importantly, interpret these massive stockpiles of complex information to help their companies more quickly and accurately make better business decisions to give them a competitive edge over competitors and to improve their operations and make them more efficient. That in turn has created something of a land rush in what's become a rapidly expanding data science platform market of more than a dozen vendors that range from established companies like IBM, Google, Microsoft and SAS to an array of smaller, younger pure-plays. The goal of all of these companies is to give these data scientists a single place to develop and run algorithms, use machine learning to help build predictive models and then deploy those models into their businesses' operations. IBM offers such products as SPSS Modeler and SPSS Statistics as well as its two-year-old Data Science Experience, a set of tools around such aspects as machine learning via the vendor's Watson cognitive computing technology and the R programming language, through the open-source RStudio offering. SAS has its Visual Suite for data visualization, prep, analytics and model building, while Microsoft offers its Azure Machine Learning platform as part of the cloud-based Cortana Intelligence Suite and Microsoft R for those who want to code in R. Other names in the space include H2O, RapidMiner, Angoss, Knime and Dataiku.