printer friendly pageEARTHSYS 153: Data Science for Social Impact (COMM 140X, DATASCI 154, ECON 163, MS&E 134, POLISCI 154, PUBLPOL 155, SOC 127)
You have some experience coding in R or Python. You've taken a class or two in basic stats or data science. But what's next? How can you use data science skills to make the world a better place? If you're asking those questions, then "Data Science for Social Impact" is for you. In this class, you'll work in four areas where data are being used to make the world better: health care, education, detecting discrimination, and clean energy technologies. You'll work with data from hospitals, schools, police departments, and electric utilities. You'll apply causal inference, prediction, and optimization techniques to help businesses, governments, and other organizations make better decisions. You'll see the challenges that arise when analyzing real data (for example, when some data are missing, or when the randomized experiment gets implemented wrong). You'll get ideas for an impactful and meaningful senior thesis, summer internship, and future career. Concretely, you'll have weekly problem s
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You have some experience coding in R or Python. You've taken a class or two in basic stats or data science. But what's next? How can you use data science skills to make the world a better place? If you're asking those questions, then "Data Science for Social Impact" is for you. In this class, you'll work in four areas where data are being used to make the world better: health care, education, detecting discrimination, and clean energy technologies. You'll work with data from hospitals, schools, police departments, and electric utilities. You'll apply causal inference, prediction, and optimization techniques to help businesses, governments, and other organizations make better decisions. You'll see the challenges that arise when analyzing real data (for example, when some data are missing, or when the randomized experiment gets implemented wrong). You'll get ideas for an impactful and meaningful senior thesis, summer internship, and future career. Concretely, you'll have weekly problem sets involving data analysis in R or python. You'll learn and apply techniques like fixed effects regression, difference-in-differences, instrumental variables, regularized regression, random forests, causal forests, and optimization. Class sessions will 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: Experience programming in R or python, or willingness to learn very quickly on your own. A basic statistics or data science course, such as any of the following:
DATASCI 112,
ECON 102 or 108,
CS 129,
EARTHSYS 140,
HUMBIO 88,
POLISCI 150A,
STATS 60 or 101,
SOC 180B, or MS&E 125.
Terms: Aut, Spr
| Units: 5
| UG Reqs: WAY-AQR, WAY-SI
EARTHSYS 290: Master's Seminar
Required of and open only to Earth Systems co-terminal MS and MA students. There are a multitude of ways to think about and define sustainability. Definitions of sustainability are determined by intersecting factors including power dynamics, economics, scientific discovery, patterns of climate migration, advances in engineering, social and political inequality. What hopes, fears, and tradeoffs are related to 'sustainability'? This course will provide space for in-depth reading and discussion related to the central question of the course - What does sustainability mean? Students will read contemporary literature by authors grappling with questions related to sustainability in various forms. Students are expected to lead class discussions on the readings for the course. Guest speakers will engage students by discussing how they apply their own notions of sustainability to their work.
Terms: Aut, Win
| Units: 3
ECON 163: Data Science for Social Impact (COMM 140X, DATASCI 154, EARTHSYS 153, MS&E 134, POLISCI 154, PUBLPOL 155, SOC 127)
You have some experience coding in R or Python. You've taken a class or two in basic stats or data science. But what's next? How can you use data science skills to make the world a better place? If you're asking those questions, then "Data Science for Social Impact" is for you. In this class, you'll work in four areas where data are being used to make the world better: health care, education, detecting discrimination, and clean energy technologies. You'll work with data from hospitals, schools, police departments, and electric utilities. You'll apply causal inference, prediction, and optimization techniques to help businesses, governments, and other organizations make better decisions. You'll see the challenges that arise when analyzing real data (for example, when some data are missing, or when the randomized experiment gets implemented wrong). You'll get ideas for an impactful and meaningful senior thesis, summer internship, and future career. Concretely, you'll have weekly problem s
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You have some experience coding in R or Python. You've taken a class or two in basic stats or data science. But what's next? How can you use data science skills to make the world a better place? If you're asking those questions, then "Data Science for Social Impact" is for you. In this class, you'll work in four areas where data are being used to make the world better: health care, education, detecting discrimination, and clean energy technologies. You'll work with data from hospitals, schools, police departments, and electric utilities. You'll apply causal inference, prediction, and optimization techniques to help businesses, governments, and other organizations make better decisions. You'll see the challenges that arise when analyzing real data (for example, when some data are missing, or when the randomized experiment gets implemented wrong). You'll get ideas for an impactful and meaningful senior thesis, summer internship, and future career. Concretely, you'll have weekly problem sets involving data analysis in R or python. You'll learn and apply techniques like fixed effects regression, difference-in-differences, instrumental variables, regularized regression, random forests, causal forests, and optimization. Class sessions will 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: Experience programming in R or python, or willingness to learn very quickly on your own. A basic statistics or data science course, such as any of the following:
DATASCI 112,
ECON 102 or 108,
CS 129,
EARTHSYS 140,
HUMBIO 88,
POLISCI 150A,
STATS 60 or 101,
SOC 180B, or MS&E 125.
Terms: Aut, Spr
| Units: 5
| UG Reqs: WAY-AQR, WAY-SI
ECON 261: The Engineering Economics of Electricity Markets (EE 268)
This course presents the power system engineering and economic concepts necessary to understand the costs and benefits of transitioning to a low carbon electricity supply industry. The technical characteristics of generation units and transmission and distribution networks as well as the mechanisms used to operate the electricity supply industries will be studied. The fundamental economics of wholesale markets and how intermittent renewables impact the price and quantity of physical and financial products traded in these markets (e.g., energy, capacity, ancillary services, and financial contracts) will be analyzed. Long-term resource adequacy mechanisms will be introduced and their properties analyzed. The role of both short-duration and seasonal energy storage will be analyzed. Mechanisms for determining the engineering and economic need for transmission network expansions in a wholesale market will be discussed. The impact of distributed versus grid-scale generation on the performanc
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This course presents the power system engineering and economic concepts necessary to understand the costs and benefits of transitioning to a low carbon electricity supply industry. The technical characteristics of generation units and transmission and distribution networks as well as the mechanisms used to operate the electricity supply industries will be studied. The fundamental economics of wholesale markets and how intermittent renewables impact the price and quantity of physical and financial products traded in these markets (e.g., energy, capacity, ancillary services, and financial contracts) will be analyzed. Long-term resource adequacy mechanisms will be introduced and their properties analyzed. The role of both short-duration and seasonal energy storage will be analyzed. Mechanisms for determining the engineering and economic need for transmission network expansions in a wholesale market will be discussed. The impact of distributed versus grid-scale generation on the performance of electricity supply industries will be studied. A detailed treatment of electricity retailing will focus on the importance of active demand-side participation in a low carbon energy sector. This course uses knowledge of probability at the level of
Stats 116, optimization at the level of MS&E 111, statistical analysis at the level of Economics 102B, microeconomics at the level of Economics 51 and computer programming in R.
Last offered: Autumn 2023
EDUC 259: Education Data Science Seminar
This three-quarter seminar is a required course for Education Data Science MS students. Central to the seminar are discussing opportunities and challenges of Education Data Science; developing community among EDS students, faculty, and external EDS innovators; making room for peer learning around students' course work, skills and experiences; and increasing understanding of and preparation for internships, the capstone project and job opportunities. Finally, students will work towards a collective EDS Seminar Paper in order to apply their learning within the seminar and coursework in an education research relevant context.
EDUC 259A: Education Data Science Seminar
This three-quarter seminar is a required course for Education Data Science MS students. Central to the seminar are discussing opportunities and challenges of Education Data Science; developing community among EDS students, faculty, and external EDS innovators; making room for peer learning around students' course work, skills and experiences; and increasing understanding of and preparation for internships, the capstone project and job opportunities. Finally, students will work towards a collective EDS Seminar Paper in order to apply their learning within the seminar and coursework in an education research relevant context.
Terms: Aut
| Units: 1-3
Instructors:
Smith, S. (PI)
;
Hardy, M. (TA)
EDUC 259B: Education Data Science Seminar
This three-quarter seminar is a required course for Education Data Science MS students. Central to the seminar are discussing opportunities and challenges of Education Data Science; developing community among EDS students, faculty, and external EDS innovators; making room for peer learning around students' course work, skills and experiences; and increasing understanding of and preparation for internships, the capstone project and job opportunities. Finally, students will work towards a collective EDS Seminar Paper in order to apply their learning within the seminar and coursework in an education research relevant context.
Terms: Win
| Units: 1-3
Instructors:
Smith, S. (PI)
;
Hardy, M. (TA)
EDUC 259C: Education Data Science Seminar
This three-quarter seminar is a required course for Education Data Science MS students. Central to the seminar are discussing opportunities and challenges of Education Data Science; developing community among EDS students, faculty, and external EDS innovators; making room for peer learning around students' course work, skills and experiences; and increasing understanding of and preparation for internships, the capstone project and job opportunities. Finally, students will work towards a collective EDS Seminar Paper in order to apply their learning within the seminar and coursework in an education research relevant context.
Terms: Spr
| Units: 1-3
Instructors:
Smith, S. (PI)
;
Hardy, M. (TA)
EDUC 259D: Education Data Science Capstone Projects
This three-quarter seminar is open to and required for second-year Education Data Science MS students. Central to the seminar is discussing work in progress on Capstone Projects.
Terms: Aut
| Units: 1-3
Instructors:
Smith, S. (PI)
;
Kapoor, R. (TA)
EDUC 259E: Education Data Science Capstone Projects
This three-quarter seminar is open to and required for second-year Education Data Science MS students. Central to the seminar is discussing work in progress on Capstone Projects. Capstone Projects may require curricular practical training and the course meets the requirements for CPT for students on F-1V visas
Terms: Win
| Units: 1-3
Instructors:
Smith, S. (PI)
;
Joshi, M. (TA)
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