When you hear the term “data scientist,” you probably think of finance or meteorology or maybe a sports-managing savant like Jonah Hill in Moneyball. But that’s all about to change. It’s no longer just baseball recruiting or even Wall Street that are wooing the analytical geniuses. We’re seeing people who used to live in academia
Gaffer a graph database “optimized for retrieving data on nodes of interest” developed by the notorious UK spy agency GCHQ is available for download. You can now run and improve it because they’ve posted it to Github...
Big Data analytics is now being applied at every stage of the retail process – working out what the popular products will be by predicting trends, forecasting where the demand will be for those products, optimizing pricing...
We’ve organized data science blogs across eight different categories: Machine learning, Hadoop, R, Python, business intelligence, data visualization, statistics, and a general data science bucket. Data about their reader count comes from Feedly. This is a work...
Given this enormous volume of social media data, analysts have come to recognize Twitter as a virtual treasure trove of information for data mining, social network analysis, and information for sensing public opinion trends and groundswells of...
As we move to year 3 of the Predictive Analytics World – Manufacturing annual conference, we are continuing to build on the momentum from the first two years. Our primary focus areas have remained more or less the...
The amount of healthcare data continues to mound every second, making it harder and harder to find any form of helpful information. In the healthcare industry, what could be more important than having better healthcare outcomes? Each...
Choosing the right vendor for managing Big Data requires each organization to consider its own needs, including preferences related to data storage, scripting language, and preferred usage tools. USE CODE PATIMES16 for 15% off Predictive Analytics World...
The value of data is measured by what you do with it, and organizations are relying on data scientists to extract that value. I recently conducted a survey of data professionals to better understand what it means...
Contrary to common belief, the hardest part of data science isn’t building an accurate model or obtaining good, clean data. It is much harder to define feasible problems and come up with reasonable ways of measuring solutions....
