Click to learn more about Kimberly Nevala. Emerging data solutions and trends have a common thread: the power of the collective in which the final product is bigger than the sum of its parts. Companies leading the data and analytics charge have recognized that the power of a collective applies to more than technology. They […]
The Evolution of Python for Data Science
Click here to learn more about author Steve Miller. I’ve been programming in Python for almost 15 years. When I started around 2001, I was doing most of my non-statistical work in C and Perl, the early days of Fortran, PL/I, and Pascal thankfully by then long gone. Just as with R for statistical computing, I […]
Paths, Patterns, and Lakes: The Shapes of Data to Come
Click to learn more about author James Kobielus. Data doesn’t exist outside your engagement with it. Or, rather, it may physically exist, but it’s little more than a shapeless mass of potential insights until you attempt to extract something useful from it. Drilling for actionable intelligence can take either of two approaches: query for it or […]
Big Data Analytics: Do Machines Have Biases?
Click to learn more about Jonathan Buckley. The prevailing opinion is that machines and computers are cold and calculating, not prone to the same knee-jerk reactions and passions that humans tend to have. In other words, they’re unbiased. They take a certain amount of input and produce a desired result based on their programming. That […]
Pros and Cons: Warehouse vs. Data Lakes
Learn more about Thomas Hazel. This column will not be the proverbial “Pros and Cons” article, weighing the good with the bad. One can find such content habitually year after year and month after month, all of which will outline the obvious advantages and disadvantage between any two things. This is particularly prevalent in the […]