Why does communication matter?

Being able to conduct experiments and manipulate data in a coding language is not enough to conduct practical and applied data science. This is because data science is, generally, only as good as how it is used in practice. For instance, a medical data scientist might be able to predict the chance of a tourist contracting malaria in developing countries with >98% accuracy; however, if these results are published in a poorly marketed journal and online mentions of the study are minimal, their groundbreaking results that could potentially prevent deaths would never truly see the light of day.

For this reason, communication of results is arguably as important as the results themselves. A famous example of poor management of the distribution of results is the case of Gregor Mendel. Mendel is widely recognized as one of the founders of modern genetics. However, his results (including data and charts) were not well-adopted until after his death. Mendel even sent...