Podcast: Data Nation

IDSS faculty and industry experts unpack how data can be used to lead, mislead, manipulate, and inform the public’s viewpoints and decisions. Season 2 has begun!

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MicroMasters in Statistics and Data Science

Learn data science methods and tools, get hands-on training in data analysis and machine learning, and find opportunities in a growing field. Watch our latest informational webinar.

globe made out of data

Data Science and Machine Learning: Making Data-Driven Decisions

This 10-week online program covers statistics and Python foundations, machine learning, prediction, recommendation systems, and more.

Nonparametric Bayesian Statistics

Bayesian nonparametrics provides modeling solutions by replacing the finite-dimensional prior distributions of classical Bayesian analysis with infinite-dimensional stochastic processes.

Causal inference algebraic statistics gene regulatory networks

Causal inference and applications to learning gene regulatory networks

Causal inference: Geometry of conditional independence structures for 3-node directed Gaussian graphical models.

Combinatorial learning submodular functions

Combinatorial learning with set functions

Learning problems that involve combinatorial objects are ubiquitous - they include the prediction of graphs, assignments, rankings, trees, groups of discrete labels or preferred sets of a user; the expression of prior structural knowledge for regularization, the identification of sets of important variables, or inference in discrete probabilistic models.

Online Learning

In this line of research, we develop strategies to optimize utility in dynamic environments in an optimal and efficient fashion.

Statistical and Computational Tradeoffs

Statistical and Computational Tradeoffs

Computational limitations of statistical problems have largely been ignored or simply overcome by ad hoc relaxations techniques.

MIT Statistics + Data Science Center
Massachusetts Institute of Technology
77 Massachusetts Avenue
Cambridge, MA 02139-4307