Courses

Alex Reinhart – August 5, 2024 refsmmat.com

Fall 2024

36-707 Regression Analysis, an introduction to regression and data analysis for first-year PhD students in Statistics & Data Science.
36-614 Data Engineering and Distributed Environments, a half-semester core course in the Master of Science in Applied Data Science program covering relational databases, SQL, and distributed data.

36-615 Software for Large-Scale Data, a revised (half-semester) core course in the Master of Science in Applied Data Science program covering distributed data analysis and machine learning.
46-927 Statistical Machine Learning II, a mini covering clustering, classification, neural networks, and other machine learning tools for students in the Masters in Computational Finance program.
36-627/727 Modern Experimental Design, a master’s and PhD-level introduction to experimental design, causality, and modern developments such as adaptive design and bandits.

36-401 Modern Regression, a junior-level undergraduate course in applied linear regression.
36-614 Data Engineering and Distributed Environments, a half-semester core course in the Master of Science in Applied Data Science program covering relational databases, SQL, and distributed data.

36-615 Software for Large-Scale Data, a new (half-semester) core course in the Master’s in Statistical Practice program covering deep learning and related topics.
36-616 Computational Methods for Statistics, a new (half-semester) core course in the Master’s in Statistical Practice program covering computational tools for statistics and data science.
46-927 Statistical Machine Learning II, a mini covering clustering, classification, neural networks, and other machine learning tools for students in the Masters in Computational Finance program.

36-707 Regression Analysis, an introduction to regression and data analysis for first-year PhD students in Statistics & Data Science.
36-614 Data Engineering and Distributed Environments, a new (half-semester) core course in the Master’s in Statistical Practice program covering relational databases, SQL, and distributed data.

36-402 Advanced Methods for Data Analysis, a senior undergraduate course on generalized linear models, nonparametric regression, cross-validation, bootstrapping, and some unsupervised learning topics.
46-927 Statistical Machine Learning II, a mini covering clustering, classification, neural networks, and other machine learning tools for students in the Masters in Computational Finance program.

36-750 Statistical Computing, the Statistics & Data Science PhD core course on software engineering, data structures, algorithms, and databases, with statistical applications.
I also supported 46-983, the Machine Learning Capstone Project for the Master’s in Computational Finance program.

36-402 Advanced Methods for Data Analysis, a senior undergraduate course on generalized linear models, nonparametric regression, cross-validation, bootstrapping, and some unsupervised learning topics.
46-927 Statistical Machine Learning II, a mini covering clustering, classification, neural networks, and other machine learning tools for students in the Masters in Computational Finance program.

36-707 Regression Analysis, an introduction to regression and data analysis for first-year PhD students in Statistics & Data Science.
36-750 Statistical Computing, the Statistics & Data Science PhD core course on software engineering, data structures, algorithms, and databases, with statistical applications. (650 is now split into a separate course of our master’s students.)

36-402 Advanced Methods for Data Analysis, a senior undergraduate course on generalized linear models, nonparametric regression, cross-validation, bootstrapping, and some unsupervised learning topics.

36-707 Regression Analysis, an introduction to regression and data analysis for first-year PhD students in Statistics & Data Science.
36-650/750 Statistical Computing, the Statistics & Data Science masters and PhD core course on software engineering, data structures, algorithms, and databases, with statistical applications.

36-651/751 Advanced Statistical Computing, a project-based advanced computing course for masters and PhD students in Statistics & Data Science.
36-764 Teaching Statistics, a reading- and discussion-based professional development class for PhD students in Statistics & Data Science to learn about pedagogy and teaching strategies. (Co-taught with Rebecca Nugent.)
46-927 Statistical Machine Learning II, a mini covering clustering, classification, neural networks, and other machine learning tools for students in the Masters in Computational Finance program.

36-650/750 Statistical Computing. (Co-taught with Christopher Genovese.)
36-727 Modern Experimental Design, a PhD-level introduction to experimental design, causality, and modern developments such as adaptive design and bandits.