Kategori: Big Data & Data Science

Statisticians have developed a remarkably powerful set of tools for analyzing normally distributed data. Too bad real data is never normally distributed. Fortunately for us, most of the time “close enough” is all we really need. But how are we to know? One quick and effective method is a look at a Q-Q plot. The […]

It’s impossible to imagine a data scientist who does not have to randomly sample datasets on a regular basis. Most employ the useful and easy function sample( ), defined in R’s base namespace. Let’s take a closer look at sample( ) and then take a look at a flexible alternative that is just as easy […]

Great! You’ve created a promising machine learning model using AzureML Studio. How do you make it available to colleagues for practical use? In Microsoft-speak, how do you “operationalize” it? The AzureML Studio makes it very easy to convert an existing experiment into an Azure web service that can be accessed from any application that supports […]

I, like many others, am constantly frustrated by the sensationalist use of terminology in data science. Twitter user @xaprb neatly summed up my own feelings when he tweeted When you’re fundraising, it’s AI [Artificial Intelligence]. When you’re hiring, it’s ML [Machine Learning]. When you’re implementing, it’s linear regression. When you’re debugging, it’s printf(). As someone […]

Anyone who has ever done a linear regression in R has seen an R formula. R formulae are examples of the Wilkinson notation, sometimes called the Wilkinson-Rogers notation. This same notation is used by other applications including Matlab and Octave to indicate relationships between variables. In most cases, R users have seen the Wilkinson notation […]