Supplemental Notebook for Unsupervised Machine Learning Using Linked SED and UMETRICS Data

This Jupyter notebook introduces unsupervised machine learning through the lens of clustering. It demonstrates how k-means clustering can be employed to better understand the types of PhD students based on funding history by utilizing the linked Survey of Earned Doctorates (SED)-Universities: Measuring the Impacts of Research on Innovation, Competitiveness, and Science (UMETRICS) data. This supplemental notebook was developed for the Fall 2021 Applied Data Analytics training facilitated by the National Center for Science and Engineering Statistics (NCSES) and Coleridge Initiative.