Many of those who call themselves statisticians just won't admit that data science heavily relies on and uses (heretical, rule-breaking) statistical science, or they don't recognize the true statistical nature of these data science techniques (some are 15-year old), or are opposed to the modernization of their statistical arsenal. They already missed the train when machine learning became a popular discipline (also heavily based on statistics) more than 15 years ago. Now machine learning professionals, who are statistical practitioners working on problems such as clustering, far outnumber statisticians. Many times, I have interacted with statisticians who think that anyone not calling himself statistician, knows nothing or little about statistics; see my recent bio published here, or visit the LinkedIn profiles of many data scientists, to debunk this myth. Any statistical technique that is not in their old books are considered heretical at best, or non-statistic at worst, or most of the time, not understood.

Data Science Central, LLC, creates the first research lab entirely focused on modern data science, big data and business analytics. Issaquah, Washington (PRWEB) June 13, 2014 - Data Science Central has created the first research lab focused entirely on modern data science, big data and business analytics. The research is developed by co-founder Dr. Vincent Granville, a former post-graduate from Cambridge University with more than 20 years of cross-industry experience in large and small companies including eBay, Wells Fargo, Visa, and Microsoft, and a long list of publications and start-ups. The research center, also called data science research lab and abbreviated as DSRC produces intellectual property (open patents that anyone can use), designs API's, machine learning algorithms prototyped on real data, and publishes articles related to Map-Reduce, robust scoring techniques for big data, clustering and creation of large taxonomies with natural language processing, detection of spurious correlations, new types of noise-resistant regression, new synthetic metrics to re-define correlations, variance and other statistical indicators in a way that is more robust, especially in the context of big data. The DSRC's mission is also to automate data science and statistical analyses, by producing efficient, scalable techniques that can be used as black boxes, in batch mode, by non-experts, or integrated to existing platforms.

Many of those who call themselves statisticians just won't admit that data science heavily relies on and uses (heretical, rule-breaking) statistical science, or they don't recognize the true statistical nature of these data science techniques (some are 15-year old), or are opposed to the modernization of their statistical arsenal. They already missed the train when machine learning became a popular discipline (also heavily based on statistics) more than 15 years ago. Now machine learning professionals, who are statistical practitioners working on problems such as clustering, far outnumber statisticians. Many times, I have interacted with statisticians who think that anyone not calling himself statistician, knows nothing or little about statistics; see my recent bio published here, or visit the LinkedIn profiles of many data scientists, to debunk this myth. Any statistical technique that is not in their old books are considered heretical at best, or non-statistic at worst, or most of the time, not understood.