1 - 8 of 8 results for: HUMBIO 129

Are you interested in environmental and energy policy? Do you want to improve your data science skills? If so, Empirical Environmental Economics is for you. In the first few weeks of class, you'll use data and microeconomic modeling to quantify the harms from pollution, including estimating the social cost of carbon emissions. For the rest of the quarter, you'll use more data and microeconomic modeling to evaluate major environmental policies such as pollution taxes, cap-and-trade programs, and subsidies for clean technologies. You will consider overall benefits and costs as well as the distributional equity, which can inform discussions of environmental justice. You will learn and practice useful data science skills, including applied econometrics/causal inference methods (e.g., difference-in-differences, instrumental variables, and regression discontinuity) and equilibrium modeling. The class has weekly problem sets involving data analysis in R, plus a final paper. Class sessions feature active learning, discussions, and small-group case studies. You should only enroll if you expect to attend regularly and complete the problem sets on time. Prerequisites: You must have experience with regression analysis (e.g., ECON 102 or 108, CS 129, EARTHSYS 140, HUMBIO 88, POLISCI 150C, or STATS 60 or 101). If you plan to take microeconomics (e.g., ECON 1, 50, or 51), we recommend you take those either beforehand or concurrently. If you have no economics background, you may still be comfortable in class if you are strong in math, statistics, and/or computer science. If you have not used R before, that is OK: we will guide you from the beginning. If you have used R before, you can still learn a lot in this class through the applications.

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.

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.

Are you interested in environmental and energy policy? Do you want to improve your data science skills? If so, Empirical Environmental Economics is for you. In the first few weeks of class, you'll use data and microeconomic modeling to quantify the harms from pollution, including estimating the social cost of carbon emissions. For the rest of the quarter, you'll use more data and microeconomic modeling to evaluate major environmental policies such as pollution taxes, cap-and-trade programs, and subsidies for clean technologies. You will consider overall benefits and costs as well as the distributional equity, which can inform discussions of environmental justice. You will learn and practice useful data science skills, including applied econometrics/causal inference methods (e.g., difference-in-differences, instrumental variables, and regression discontinuity) and equilibrium modeling. The class has weekly problem sets involving data analysis in R, plus a final paper. Class sessions feature active learning, discussions, and small-group case studies. You should only enroll if you expect to attend regularly and complete the problem sets on time. Prerequisites: You must have experience with regression analysis (e.g., ECON 102 or 108, CS 129, EARTHSYS 140, HUMBIO 88, POLISCI 150C, or STATS 60 or 101). If you plan to take microeconomics (e.g., ECON 1, 50, or 51), we recommend you take those either beforehand or concurrently. If you have no economics background, you may still be comfortable in class if you are strong in math, statistics, and/or computer science. If you have not used R before, that is OK: we will guide you from the beginning. If you have used R before, you can still learn a lot in this class through the applications.

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.

Are you interested in environmental and energy policy? Do you want to improve your data science skills? If so, Empirical Environmental Economics is for you. In the first few weeks of class, you'll use data and microeconomic modeling to quantify the harms from pollution, including estimating the social cost of carbon emissions. For the rest of the quarter, you'll use more data and microeconomic modeling to evaluate major environmental policies such as pollution taxes, cap-and-trade programs, and subsidies for clean technologies. You will consider overall benefits and costs as well as the distributional equity, which can inform discussions of environmental justice. You will learn and practice useful data science skills, including applied econometrics/causal inference methods (e.g., difference-in-differences, instrumental variables, and regression discontinuity) and equilibrium modeling. The class has weekly problem sets involving data analysis in R, plus a final paper. Class sessions feature active learning, discussions, and small-group case studies. You should only enroll if you expect to attend regularly and complete the problem sets on time. Prerequisites: You must have experience with regression analysis (e.g., ECON 102 or 108, CS 129, EARTHSYS 140, HUMBIO 88, POLISCI 150C, or STATS 60 or 101). If you plan to take microeconomics (e.g., ECON 1, 50, or 51), we recommend you take those either beforehand or concurrently. If you have no economics background, you may still be comfortable in class if you are strong in math, statistics, and/or computer science. If you have not used R before, that is OK: we will guide you from the beginning. If you have used R before, you can still learn a lot in this class through the applications.