A Data Science Central Community
Over 100 years ago, the great science fiction writer H. G. Wells was credited with saying, "Statistical thinking will one day be as necessary for efficient citizenship as the ability to read or write." It is clear that this statement is probably more true today than ever, as Big Data and Analytics are paraded before every aspect of life, business, government, and social media experience. Statistical thinking is the bedrock of data science as statistics is a core methodology for many disciplines, including experimental science, operations research, decision sciences, and marketing research. Yet many appear to have forgotten this (or maybe have let it "slip their mind") -- see the recent article by the American Statistical Association (ASA) President, Dr. Marie Davidian: "Aren't We Data Science?" As we read this, we need to remember also that Data Science includes several core methodologies (disciplines): machine learning (data mining), visualization, data management (including data structures, indexing, modeling, taxonomies), applied mathematics, semantics (ontologies), and application-specific discipline science, as well as the original core "data science" of statistics!
Consequently, it is wise for us to avoid the pitfalls that await us if we ignore the tenets and truths of statistics. Some of these "truisms" include:
Read more about these specific examples in the full article "Statistical Truisms in the Age of Big Data" at http://www.statisticsviews.com/details/feature/4911381/Statistical-...