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Data Science 1 is the first half of a one-year introduction to data science. The course will focus on the analysis of messy, real life data to perform predictions using statistical and machine learning methods. Material covered will integrate the five key facets of an investigation using data: (1) d...

Data Science 1 is the first half of a one-year introduction to data science. The course will focus on the analysis of messy, real life data to perform predictions using statistical and machine learning methods. Material covered will integrate the five key facets of an investigation using data: (1) d...

Data Science 2 is the second half of a one-year introduction to data science. Building upon the material in Data Science 1, the course introduces advanced methods for statistical modeling, representation, and prediction. Topics include multiple deep learning architectures such as CNNs, RNNs, transfo...

Data Science 2 is the second half of a one-year introduction to data science. Building upon the material in Data Science 1, the course introduces advanced methods for statistical modeling, representation, and prediction. Topics include multiple deep learning architectures such as CNNs, RNNs, transfo...

This is CS50, Harvard University's introduction to the intellectual enterprises of computer science and the art of programming, for concentrators and non-concentrators alike, with or without prior programming experience. (More than half of CS50 students have never taken CS before!) This course teach...

The course introduces various aspects of quantum science, including quantum computing, quantum simulation, quantum communication and quantum metrology. It will particularly focus on the presentation of different experimental platforms currently used in the field and include superconducting qubits, t...

Equivalent to 5.111; see 5.111 for description. Instruction provided through small, interactive classes taught by ESG staff.

This course examines the wide-ranging impact data science has on the world and how to think critically about issues of fairness, privacy, ethics, and bias while building algorithms and predictive models that get deployed in the form of products, policy and scientific research. Topics will include al...

The primary objective of this course is to provide a comprehensive understanding of the Deep Learning process in a practical, real-world context. With a strong emphasis on Machine Learning Operations (MLOps), this course not only reviews existing Deep Learning flows, but also enables students to bui...

This course will provide students with an introduction to environmental science and engineering by providing an overview of current environmental issues, including climate change, air pollution, and water pollution. Students critically evaluate underlying science and knowledge limitations, and explo...

An introduction to the science of global warming/climate change meant to assist students in understanding issues that often appear in the news and public debates. The course is meant for any STEM student with basic math preparation, not assuming prior science courses. Topics include the greenhouse e...

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty...

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty...

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty...

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty...

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty...

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty...

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty...

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty...

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty...