21 - 30 of 236 results for: MS

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 (recommended): 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, SOC 180B, or MS&E 125.

Participants report on and review scientific articles published in peer reviewed journals. Focus is on manuscripts which report basic and mechanistic discoveries, animal modeling and translational research. The objective is to introduce MLAS students to critical scientific review of hypothesis-based research and experimental design, data analysis and interpretation. Enrollment limited to undergraduate and graduate students currently matriculated or planning to enroll in the MS in Laboratory Animal Science degree program.

(Previously numbered CS 353). Introduction to research in the Theory of Computing, with an emphasis on research methods (the practice of research), rather than on any particular body of knowledge. The students will participate in a highly structured research project: starting from reading research papers from a critical point of view and conducting bibliography searches, through suggesting new research directions, identifying relevant technical areas, and finally producing and communicating new insights. The course will accompany the projects with basic insights on the main ingredients of research. Research experience is not required, but basic theory knowledge and mathematical maturity are expected. The target participants are advanced undergrads as well as MS students with interest in CS theory. Prerequisites: CS161 and CS154. Limited class size.

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 (recommended): 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, SOC 180B, or MS&E 125.

Advanced Reflective Practice supports students in developing as adaptive learners who are able to synthesize and integrate their experiences from across the MS Design program and beyond. Through immersive interactions and technology-enhanced asynchronous work, students strengthen their capacity to navigate open-ended problems while understanding their own learning processes. The curriculum emphasizes ethical reflection, challenging students to consider the broader implications and responsibilities of their design decisions. This class is open to MS Design students only.

This class is the scaffold for the second year MS Design thesis project. Each second year student completes a year-long thesis. Topics are specific to each student and lie at the intersection of chosen method and domain focus areas. Prerequisite: Open only to second year MS Design students who have successfully completed the first year of coursework.

This is a continuation of DESIGN361A. This class is the scaffold for the second year MS Design thesis project. Each second year student completes a year-long thesis. Topics are specific to each student and lie at the intersection of chosen method and domain focus areas. Prerequisite: Open only to second year MS Design students who have successfully completed the first year of coursework.

For graduate Design students, and select students by application, who have completed DESIGN361A&B. This class is the scaffold for the second year MS Design thesis project. Each second year student completes a year-long thesis. Topics are specific to each student and lie at the intersection of chosen method and domain focus areas. Prerequisite: Open only to second year MS Design students who have successfully completed the first year of coursework.

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 (recommended): 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, SOC 180B, or MS&E 125.