In his towards data science article, “What is Data Science,” the author, Jeff Hale describes three major facets of data science: data analysis, statistics, and machine learning. In the field of chemistry, I know that all three of these disciplines have been applied to varying extents. For starters, data analysis plays a major role in being able to determine the value of different constants. For example, in acid-base chemistry, determining the pKa of a weak acid requires the analysis of data from a titration curve. A titration curve is a graph that displays changes in the pH of a solution as more titrant is added. By definition, the pKa of a weak acid is the midpoint between two equivalence points (inflection points) of a titration curve, so the pH data must be analyzed for such. In chemistry, as well as in all of the sciences, statistics is a requirement for determining the significance of experimental results, because it is important to know whether or not laboratory results are a mere fluke. Finally, in recent times, machine learning has been applied in studying chemical reactions. Specifically, Google’s AlphaFold deep learning network underwent training in order to predict how proteins would fold. Proteins are highly complex, emergent structures which can contain hundreds, if not thousands of amino acids. Because of the countless number of intermolecular forces at play, predicting how proteins fold together is difficult to say the least. Thanks to AlphaFold, we were provided with knowledge within a field of chemistry which had previously been nigh-impossible to study. For what I’m aware, in the study of chemistry, machine learning has only recently been applied, whereas data analysis and statistics have been used for over a century. Nonetheless, all three disciplines of data science described by Hale have found their place.