*3.2.4 Data science*

In recent years, Data Science has been turning business analytics upside down. In general, data science is understood as the science of learning from data, a seemingly perfect fit to our objectives here. Yet Data Science has seen much slower adoption in Academia, perhaps owing to the fact that the only part of Data Science that fits with the traditional epistemological approach to research is that part of Data Science which uses traditional NHST statistics.

A hallmark of Data Science is the use of machine learning. However, many of the methods of machine learning are methods that typically benefit from large datasets: those with hundreds of columns and millions of rows. Nevertheless, machine learning does have techniques that can be of use in the small-n world. Techniques for data reduction, data visualization, data mining, and simulation all are powerful tools that can often be applied in the domain of small-n research. Perhaps of particular interest to space medicine, researchers are able to use these methods for exploratory data analysis and hypothesis generation, tasks at which unsupervised machine learning excels.
