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.
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?
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
Many make out the data scientist to be a Renaissance woman or man who can single-handedly elevate the organization’s analytics savvy. However, preparing students for corporate roles in data science means training them for many positions on a team. At Arizona State University, we work closely with
With big data financial and transactional data no longer in silos, we can now look at them together. Vince Walden, Ernst and Young partner, says that big data technologies allow them to look at data from all angles.
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.
The organization that can quickly extract insight from their data AND leverage the data achieves an advantage. Rick Clements, IBM's director of marketing for Big Data says, "we are moving from the notion of big data to fast data, where what really matters is speed...and real-time actionable insight
Ernst & Young (EY) uses IBM BigInsights platform to leverage big data and analytics to combat fraud. By running test queries across multiple transactions they can identify fraudulent transactions and mitigate risk for its customers.
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.
Open sourcing of all climate data would give humanity a continuously updated baseline environmental intelligence metric that could be used to track deterioration or improvements in key areas (air, water, pollution, soil) over time.
The need for a data scientist is all the rage right now. At every marketing conference I go to, companies are clamoring for their skills, but the supply and demand is not coming to the needed equilibrium. We are faced with the choice of continuing to wait, or to employ a solution that is already at
The gap between the demand for analytics talent globally and the supply of talent is one of the key obstacles to big data implementations across all organizations. In a global study of business and IT leaders conducted by IBM Institute for Business Value, 1/3 of respondents cited the lack of
Even the experts can have a tough time spotting the early stages of many cancers and other illnesses. That difficulty can cost lives, because the earlier symptoms are detected, the sooner these conditions can be eradicated through drugs, surgery and other treatments.
The advanced medical imaging