Data Science with its myriad of statistical, mathematical and computing tools, ranging from support vector machines to convex optimization, presents enormous promise to solve business problems, subject to substantive financial, personnel and temporal investments. However, implementing tools not designed for the problem at hand will be futile at best and detrimental at worst. The contribution of this study is a prescriptive framework to guide the selection of data science tools and techniques based on (a) the nature and type of the problem and (b) the constraints on the underlying data. The implication is to provide a practical guide to make astute decisions about the choice of tools to identify an optimal business solution. Furthermore, this study embeds an opportunity for a data science project to implement the framework. This paper also lays the foundation for
Bhattacharya, Prithvi, "‘Horses for Courses’ in Data Science: Towards a Cross- Sectional Framework for Optimal Modeling for Business Problems" (2021). PACIS 2021 Proceedings. 198.
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