Global climate data is massive, diverse and often internally inconsistent. Researchers who attempt to use data science to understand, predict and control global warming find themselves challenged by methodological limitations that frustrate their attempts to fathom this sprawling mosaic. Chief
Prioritizing data mining projects is a delicate art, equivalent to the decisions that R&D managers face every single day. How should you prioritize your data mining efforts and allocate your limited resources most effectively? Most important, how do you decide what NOT to work on?
Data science is extremely important in today’s data-driven world, but is only effective if it can be efficiently executed in a production environment. Find out about an essential best practice to make your data science effective.
Real-world experimentation of a very personal and hyper-analytical nature is what the quantified-self (QS) movement is all about. QS practitioners are playing with approaches that behavioral scientists have traditionally applied to third-party subjects within controlled laboratory experiments. The
A data scientist uses machine learning (ML) to find heretofore unknown correlations and other patterns in fresh data. ML is adept at finding both the "known unknowns" and the "unknown unknowns" through the power of supervised learning and unsupervised learning methodologies, respectively.
There is a human tendency to unthinkingly and uncritically accept the spurious correlations that big data reveals. Detangling the truth from the spurious demands critical thinking and relentless skepticism.