Applying dimension reduction in modern data science and quantitative analysis

Abstract

Abstract Researchers in applied settings are often confronted with high dimensional data spaces. While more data is generally considered a good thing, researchers may also be interested in learning the structure that characterizes the data space. In this sense, the goal of the researcher is to make the complex data space simpler, and thus reduce and project it onto a lower dimensional subspace. This paper highlights the impact of the code from my manuscript forthcoming with Cambridge University Press, to bring researchers into the applied, modern landscape of dimension reduction for solving real problems using open-source code.