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Are you interested in clean tech and sustainability? Do you like working with data or plan to manage data scientists? Do you want to find a socially impactful job? If so, Data Science for Environmental Business is for you. Each week, we'll have a guest speaker from a utility, venture capital firm, clean tech startup, renewable energy developer, or some other sustainability-related business. We'll do a quantitative case study of one of the speaker's business problems, such as carbon footprint measurement, supply chain decarbonization, techno-economic analysis, where to site renewable energy facilities, how to value electricity storage, or predicting demand for electric vehicles. Then in the next class, we'll discuss the analytical decisions you made on the case study and the business implications of your results. We aim to draw a mix of students from the GSB, engineering, sustainability, data science, computer science, economics, math, and other fields. Students registering through the GSB should expect a roughly standard MBA class workload. Students registering through non-GSB course numbers should expect a serious data science course where you'll learn and apply new methods. We hope to develop a pipeline of students working for the guest speakers and similar firms. Prerequisites: You must know basic statistics and regression analysis (e.g., ECON 102 or 108, CS 129, EARTHSYS 140, HUMBIO 88, POLISCI 150C, or STATS 60 or 101). You should also have at least some experience with data analysis in R, python, Stata, MATLAB, or something similar. If you plan to take microeconomics (e.g., ECON 1, 50, or 51) or empirical environmental economics ( ECON 177), we recommend you take those either beforehand or concurrently.