We are working to catalyze a new era of research that enables interdisciplinary approaches to data-intensive discovery.
Changes in academia are required to more fully support data scientists who enable new data-driven discoveries. We aim to create new types of institutional environments in which these discoveries can take place.
Moore-Sloan Data Science Environments
Many other institutions have an interest in promoting and facilitating data-driven discovery in academia. Below is a list of those that align closely with the objectives of the Data Science Environment Partnership.
Note: Although certificate and undergraduate or master’s degree programs in data science are a critical component of academic data science, we focus here on programs that emphasize the use of data-driven approaches to active research. For international data science educational programs, see this curated list: github.com/ryanswanstrom/awesome-datascience-colleges.
|Institution||Center or Program|
|Caltech||Center for Data Driven Discovery|
|Columbia University||Data Science Institute|
|Johns Hopkins University||Institute for Data Intensive Engineering and Science|
|Michigan State University||Computational Mathematics, Science and Engineering|
|Princeton University||Center for Statistics and Machine Learning|
|Rensselaer Polytechnic Institute||Data Science Research Center|
|Stanford University||Stanford Data Science Initiative|
|University of Chicago||Computation Institute|
|University of Massachusetts Amherst||Center for Data Science|
|University of Michigan||MIDAS: Michigan Institute for Data Science|
|University of Rochester||Goergen Institute for Data Science|
|University of Virginia||Data Science Institute|
|University of Washington at Tacoma||Center for Data Science|