Odds and probability are different, and too many people make decisions without knowing that.
Constantly manually executing SQL queries for your clients? Here’s a way to get them to help themselves.
The analytics value lifecycle is a framework that can help with the design and structure of an analytics team
An aspect of mathematics that I believe to be sorely lacking in the business world, and which can lead to seriously erroneous decisions being made on a regular basis: the concept of significance when studying data.
How survival analysis was born out of the battle against smoking
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%.
The fifth and final part of the series on Machine Learning for beginners.
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?
This is the fourth in a series of articles explaining the principles of networks for those who may use them in a data science context.
In this installment of the series, we review some common measures and considerations when assessing the effectiveness and value of a classification algorithm.