Program Overview

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Academics

The university offers various academic programs, including the Interdisciplinary Doctoral Program in Statistics, Minor in Statistics and Data Science, MicroMasters program in Statistics and Data Science, Data Science and Machine Learning: Making Data-Driven Decisions, and the Norbert Wiener Fellowship.

Interdisciplinary Doctoral Program in Statistics

The Interdisciplinary Doctoral Program in Statistics is a unique program that allows students to pursue a PhD in Statistics in conjunction with another field. The program offers several interdisciplinary PhD options, including:

• Interdisciplinary PhD in Aero/Astro and Statistics
• Interdisciplinary PhD in Brain and Cognitive Sciences and Statistics
• Interdisciplinary PhD in Economics and Statistics
• Interdisciplinary PhD in Mathematics and Statistics
• Interdisciplinary PhD in Mechanical Engineering and Statistics
• Interdisciplinary PhD in Physics and Statistics
• Interdisciplinary PhD in Political Science and Statistics
• Interdisciplinary PhD in Social & Engineering Systems and Statistics

Requirements

Students must complete their primary program’s degree requirements along with the IDPS requirements. Statistics requirements must not unreasonably impact performance or progress in a student’s primary degree program.

PhD Earned on Completion

The PhD earned on completion of the program is in Mathematics and Statistics. The IDPS/Mathematics Chair is Philippe Rigollet.

Seminar

The program includes a doctoral seminar in statistics, IDS.190, which is offered only in the fall.

Course Requirements

The program requires students to complete courses in the following areas:

• Probability: Students must choose one of the following courses:
  • 6.7700 (6.436): Fundamentals of Probability
  • 18.675: Theory of Probability
• Statistics: Students must choose one of the following courses:
  • 6.7730: Modern Mathematical Statistics
  • 18.655: Mathematical Statistics
  • IDS.160: Mathematical Statistics – A Non-Asymptotic Approach
• Computation & Statistics: Students must choose one of the following courses:
  • 6.7220 (6.252/15.084): Nonlinear Optimization
  • 6.7230 (6.256): Algebraic Techniques and Semidefinite Optimization
  • 6.7810 (6.438): Algorithms for Inference
  • 6.7900 (6.867): Machine Learning
  • 6.7910 (9.520/6.860): Statistical Learning Theory and Applications
  • 6.7320 (18.337/6.338): Numerical Computing and Interactive Software
  • 18.338: Eigenvalues of Random Matrices
  • 6.5210 (18.415/6.854): Advanced Algorithms
  • 6.5220 (18.416/6.856): Randomized Algorithms
  • 18.657: Topics in Statistics
• Data Analysis: Students must choose one of the following courses:
  • 6.8800 (6.555, 16.456/HST.582): Biomedical Signal and Image Processing
  • 6.8300 (6.869): Advances in Computer Vision
  • 9.073/HST.460: Statistics for Neuroscience Research
  • 9.272/HST.576: Topics in Neural Signal Processing
  • 18.367: Waves and Imaging
  • 6.3732 (IDS.131/6.439): Statistics, Computation and Applications

Minor in Statistics and Data Science

The Minor in Statistics and Data Science is designed to provide students with a foundation in statistical analysis and data science.

MicroMasters program in Statistics and Data Science

The MicroMasters program in Statistics and Data Science is a online program that provides students with a comprehensive education in statistics and data science.

Data Science and Machine Learning: Making Data-Driven Decisions

This program is designed to provide students with the skills and knowledge needed to make data-driven decisions in a variety of fields.

Norbert Wiener Fellowship

The Norbert Wiener Fellowship is a prestigious fellowship program that provides students with the opportunity to pursue research in statistics and data science.