While the debate rages about a reproducibility crisis in academia, there’s a much more common one right under our noses
Whether your model is meant to be explanatory or predictive has profound implications for its design
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.
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.
This year, South Korea’s fertility rate is expected to drop below 1.00.
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?
This is the third in a series of articles explaining the principles of networks for those who may use them in a data science context.
I strongly believe that the field of data science could benefit greatly from a more frequent use of analogies in communication of key concepts.
This is the second in a series of articles explaining the principles of graph theory for those who may use it in a data science context.