"I almost feel that folks in data science [excluding statisticians] are suddenly realizing that this kind of work is not new and are desperately looking for ways to justify a distinction." — Thomas Speidel
[Machine Learning is simply a] “loose confederation of themes in statistical inference (and decision-making)” — Michael Jordan
"Without a grounding in statistics, a Data Scientist is a Data Lab Assistant." — Martyn Jones
Myth #3: Data mining, machine learning, Big data analysis, business analytics, and data science are distinct from statistics
Repackaging statistics with complementary fields has the potential to create new synergies. E.g., econometrics is the marriage of economics and statistics. This repackaging has been extremely successful; surpassing Six Sigma's mixed results as we discussed in Blog 2. Econometrics has embraced statistics. Applied econometricians have helped develop best practice and some identify as applied statisticians. They are with the science.
