How to build R Shiny apps that update themselves

It is now possible to build Shiny apps that can update themselves on a regular basis, pulling in refreshed data so that people are always looking at the most up to date analysis. So with one up front development sprint, an analyst can reduce their ongoing analytic workload on a particular topic by close to 100%.

Keep calm and do more testing

As data scientists, we are all familiar with what happens when a process, package or application breaks. We dive into it with interest to try to diagnose where the unanticipated error occurred. Did something unexpected occur in the raw data? Did our code not anticipate a particular permutation of inputs?

Trash or treasure — how to tell if a classification algorithm is any good

In this installment of the series, we review some common measures and considerations when assessing the effectiveness and value of a classification algorithm.

Decision makers need more math

Decision makers in the year 2020 will be facing many more data driven documents and charts than they did 10 or 20 years prior. But are those decision makers any better equipped to make accurate decisions in such a data rich environment?