Data Analyst vs Data Scientist: Industry Perspectives

Both “Data Analyst” (DA) and “Data Scientist” (DS) are titles that vary greatly between industries and even amongst individual organizations within industries. As the roles behind titles change over time, it is natural for some teams to ask themselves the following questions: should we have distinct roles or just stick to one? How would we differentiate the roles in a way that fulfills our organization’s needs and is generally consistent with similar organizations?

Probabilistic programming languages for statistical inference

Introduction This post was inspired by a question about JAGS vs BUGS vs Stan: right, that’s what got me confused! so they.. do the same thing? — Andrew MacDonald, January 10, 2017 Explaining the differences would be too much for Twitter, so I’m just gonna give a quick explanation here. 2020-05-18 update: Coming from a background of statistical inference in the context of academia and research using R, where these have been the prevalent PPLs for quite some time, I admittedly have a bit of a blind spot for PyMC3.

Mostly-free resources for learning data science

In the past year or two I’ve had several friends approach me about learning statistics because their employer/organization was moving toward a more data-driven approach to decision making. (This brought me a lot of joy.) I firmly believe you don’t actually need a fancy degree and tens of thousands of dollars in tuition debt to be able to engage with data, glean insights, and make inferences from it. And now, thanks to many wonderful statisticians on the Internet, there is now a plethora of freely accessible resources that enable curious minds to learn the art and science of statistics.