Statistics consists of two intertwined threads of inquiry: Statistical Theory and Data Analysis. The former uses probability theory to build and analyze mathematical models of data in order to devise methods for making effective predictions and decisions in the face of uncertainty. The latter involves techniques for extracting insights from complicated data, designs for accurate measurement and comparison, and methods for checking the validity of theoretical assumptions. Statistical Theory informs Data Analysis and vice versa. The Statistics Department curriculum follows both of these threads and helps the student develop the complementary skills required.

Click on the majors above to see requirements for each of the five majors that we provide in our department.

Note: We recommend that you use the information provided as a general guideline, and then schedule a meeting with a Statistics Undergraduate Advisor to discuss the requirements in more detail, and build a program that is tailored to your strengths and interests.

For more detailed information on each major please see our Undergraduate Catalog

Our core major builds a strong foundation in methods, theory, computation, and practice. We emphasize modern methods, strong communication skills, and hands-on experience analyzing real data. This is an ideal choice for any student interested in statistical thinking and data science and is tremendous preparation for a career that requires data skills.

**Academic Advisor:** Samantha Nielsen

**Faculty Advisors:** Peter Freeman and Mark Schervish

Theory Requirements | |||

Course Topic/Title | Course Number | Units | Prerequisites |
---|---|---|---|

Calculus | 21-111 and 112, or 21-120 | 20 or 10 | |

Multivariate Calc/Analysis | 21-256, 21-259, or 21-268 | 9–10 | 21-112 or 21-120 |

Linear/Matrix Algebra | 21-240, 21-241, or 21-242 | 10 | |

Probability | 36-225, 36-217, 21-325, or 15-359 | 9 | 21-112, 21-122, 21-123, 21-256, or 21-259 |

Statistical Inference | 36-226 or 36-326 | 9 | C or higher in 36-217, 36-225, 21-325, or 15-359 |

Data-Analysis Requirements | |||

Course Topic/Title | Course Number | Units | Prerequisites |

Beginning Data Analysis | 36-200 | 9 | |

Intermediate Data Analysis | 36-202, 36-208, 36-290, or 36-309 | 9 | various |

Advanced Elective | 36-311, 36-315, 36-303, 36-46x, or 36-490 | 9 | 36-202, 36-208, 36-290, or 36-309 |

Special Topics | 36-46x | 9 | various |

General Elective | various | 9 | various |

Modern Regression | 36-401 | 9 | C or higher in 36-226, 36-326, or 36-625 and pass 21-240 or 21-241 |

Advanced Methods for Data Analysis | 36-402 | 9 | C or higher in 36-401 |

Concentration Area (Four coherent, complementary courses) |
various | 36 | Advisor approval |

Computing Requirements | |||

Course Topic/Title | Course Number | Units | Prerequisites |

Statistical Computing | 36-350 or 36-650/750 | 9 | (36-202, 36-208, 36-290, 36-309 or 70-208, or equivalent) and 36-225 |

This joint major develops the critical ideas and skills underlying statistical machine learning — the creation and study of algorithms that enable systems to automatically learn and improve with experience. It is ideal for students interested in statistical computation, data science, or “Big Data” problems, including those planning to pursue a related Ph.D. or a job in the tech industry.

**Academic Advisor:** Samantha Nielsen

**Faculty Advisors:** Ryan Tibshirani and Ann Lee

Theory Requirements | |||

Course Topic/Title | Course Number | Units | Prerequisites |
---|---|---|---|

Calculus | 21-111 and 112, or 21-120 | 20 or 10 | |

Integration and Approximation | 21-122 | 10 | 21-112 or 21-120 |

Multivariate Calc/Analysis | 21-256, 21-259, or 21-268 | 9–10 | 21-112 or 21-120 |

Concepts of Mathematics | 21-127 | 10 | |

Linear/Matrix Algebra | 21-240, 21-241, or 21-242 | 10 | |

Probability | 36-225, 36-217, 21-325, or 15-359 | 9 | 21-112, 21-122, 21-123, 21-256, or 21-259 |

Statistical Inference | 36-226 or 36-326 | 9 | C or higher in 36-217, 36-225, 21-325, or 15-359 |

Data-Analysis Requirements (Option 1) | |||

Course Topic/Title | Course Number | Units | Prerequisites |

Beginning Data Analysis | 36-200 | 9 | |

Intermediate Data Analysis | 36-202, 36-208, 36-290, or 36-309 | 9 | various |

Advanced Elective | 36-311, 36-315, 36-303, 36-46x, or 36-490 | 9 | various |

Advanced Elective | 36-311, 36-315, 36-303, 36-46x, or 36-490 | 9 | various |

Modern Regression | 36-401 | 9 | C or higher in 36-226, 36-326, or 36-625 and pass 21-240 or 21-241 |

Advanced Methods for Data Analysis | 36-402 | 9 | C or higher in 36-401 |

Data-Analysis Requirements (Option 2) | |||

Course Topic/Title | Course Number | Units | Prerequisites |

Advanced Elective | 36-311, 36-315, 36-303, 36-46x, or 36-490 | 9 | various |

Advanced Elective | 36-311, 36-315, 36-303, 36-46x, or 36-490 | 9 | ” |

Advanced Elective | 36-311, 36-315, 36-303, 36-46x, or 36-490 | 9 | ” |

Modern Regression | 36-401 | 9 | C or higher in 36-226, 36-326, or 36-625 and pass 21-240 or 21-241 |

Advanced Methods for Data Analysis | 36-402 | 9 | C or higher in 36-401 |

Computing Requirements | |||

Course Topic/Title | Course Number | Units | Prerequisites |

Statistical Computing | 36-350 or 36-650/750 | 9 | (36-202, 36-208, 36-290, 36-309, 70-208, or equivalent) and 36-225 |

Fundamentals of Programming | 15-112 | 12 | |

Principles of Iterative Computation | 15-122 | 10 | C or higher in 15-112 |

Machine Learning | 10-601/701 | 12 | C or higher in (15-122 or 15-123) and (15-151 or 21-127) |

Algorithms and Advanced Data Structures | 15-351 | 12 | 15-111, 15-123, 15-121, or 15-122 |

Machine Learning Elective | 10-405/605 15-381 15-386 16-720 16-311 11-411 11-761 |
9 | vary by elective |

This joint major focuses on the skills needed to apply statistical modeling and methodology to the empirical analysis of economic data. It is ideal for students who plan to pursue an advanced degree in statistics, economics, or management or a career in government, industry, finance, education, or public policy.

**Statistics Academic Advisor:** Samantha Nielsen

**Economics Academic Advisor:** Kathleen Conway

**Faculty Advisors:** Rebecca Nugent and Edward Kennedy

Theory Requirements | |||

Course Topic/Title | Course Number | Units | Prerequisites |
---|---|---|---|

Calculus | 21-111 and 112, or 21-120 | 20 or 10 | |

Multivariate Calc/Analysis | 21-256, 21-259, or 21-268 | 9–10 | 21-112 or 21-120 |

Linear/Matrix Algebra | 21-240, 21-241, or 21-242 | 10 | |

Probability | 36-225, 36-217, 21-325, or 15-359 | 9 | 21-112, 21-122, 21-123, 21-256, or 21-259 |

Statistical Inference | 36-226 or 36-326 | 9 | C or higher in 36-217, 36-225, 21-325, or 15-359 |

Data-Analysis Requirements | |||

Course Topic/Title | Course Number | Units | Prerequisites |

Beginning Data Analysis | 36-200 | 9 | |

Intermediate Data Analysis | 36-202, 36-208, 36-290, or 36-309 | 9 | |

Advanced Elective | 36-311, 36-315, 36-303, 36-46x, or 36-490 | 9 | 36-202, 36-208, 36-290, or 36-309 |

Advanced Elective | 36-311, 36-315, 36-303, 36-46x, or 36-490 | 9 | ” |

General Elective | various | 9 | various |

Modern Regression | 36-401 | 9 | C or higher in 36-226, 36-326, or 36-625 and pass 21-240 or 21-241 |

Advanced Methods for Data Analysis | 36-402 | 9 | C or higher in 36-401 |

Computing Requirements | |||

Course Topic/Title | Course Number | Units | Prerequisites |

Statistical Computing | 36-350 or 36-650/750 | 9 | (36-202, 36-208, 36-290, 36-309 or 70-208, or equivalent) and 36-225 |

Economics Requirements | |||

Course Topic/Title | Course Number | Units | Prerequisites |

Principles of Microeconomics | 73-102 | 9 | |

Principles of Macroeconomics | 73-103 | 9 | 73-102 |

Intermediate Microeconomics | 73-230 | 9 | (21256 or 21259 or 21269 or 21268) and (73102 or 73100) |

Intermediate Macroeconomics | 73-240 | 9 | (21259 or 21269 or 21268 or 21256) and (73103 or 73100) and (73230) |

Writing for Economists | 73-270 | 9 | (76101) and (73230) and (73240) |

Econometrics I | 73-274 | 9 | (21256 or 21259 or 21268 or 21269) and (36217 or 36225) and (73230) |

Econometrics II | 73-374 | 9 | (21256 or 21259 or 21268 or 21269) and (36225 or 36217) and (73230) and (73274) |

Two advanced electives | 73-300 through 73-495, excluding 73-374 | 18 | various |

This track focuses on the fundamental mathematical theory underlying statistical inference and prediction. It is ideal for students who are interested in pursuing a Ph.D. in Statistics, an advanced degree in a related field requiring strong mathematical preparation, or a career in which a strong background in statistical theory is valuable.

**Academic Advisor:** Samantha Nielsen

**Faculty Advisor:** Jing Lei

Theory Requirements | |||

Course Topic/Title | Course Number | Units | Prerequisites |
---|---|---|---|

Calculus | 21-111 and 112, or 21-120 | 20 or 10 | |

Integration and Approximation | 21-122 | 10 | 21-112 or 21-120 |

Multivariate Calc/Analysis | 21-256, 21-259, or 21-268 | 9–10 | 21-112 or 21-120 |

Concepts of Mathematics | 21-127 | 10 | |

Linear/Matrix Algebra | 21-240, 21-241, or 21-242 | 10 | |

Probability | 36-225, 36-217, 21-325, or 15-359 | 9 | 21-112, 21-122, 21-123, 21-256, or 21-259 |

Statistical Inference | 36-226 or 36-326 | 9 | C or higher in 36-217, 36-225, 21-325, or 15-359 |

Principles of Real Analysis | 21-355 | 9 | 21-127 and 21-122 |

Intro to Probability Modeling | 36-410 | 9 | 36-225, 36-217, 36-325, or 36-625 |

Two of the following: | |||

Probability and Math Stat I Intermediate Statistics Discrete Math Optimization Combinatorics Real Analysis II |
36-700 36-705 21-228 21-257 or 21-292 21-301 21-356 |
12 12 9 9 9 9 |
21-127 or 15-151 21-240/1/2, 21-256, 06-262, or 18-202 21-127 or 15-151 21-240, 21-241, 21-242, 21-256, 06-262, or 18-202 21-122 and (15-251 or 21-228) (21-259,21-268,or 21-269) and 21-241/2 and 21-355 |

Data-Analysis Requirements | |||

Course Topic/Title | Course Number | Units | Prerequisites |

Beginning Data Analysis | 36-200 | 9 | |

Intermediate Data Analysis | 36-202, 36-208, 36-290, or 36-309 | 9 | various |

Advanced Elective | 36-311, 36-315, 36-303, 36-46x, or 36-490 | 9 | 36-202, 36-208, 36-290, or 36-309 |

Special Topics | 36-46x | 9 | various |

General Elective | various | 9 | various |

Modern Regression | 36-401 | 9 | C or higher in 36-226, 36-326, or 36-625 and pass 21-240 or 21-241 |

Advanced Methods for Data Analysis | 36-402 | 9 | C or higher in 36-401 |

Computing Requirements | |||

Course Topic/Title | Course Number | Units | Prerequisites |

Statistical Computing | 36-350 or 36-650/750 | 9 | (36-202, 36-208, 36-290, 36-309 or 70-208, or equivalent) and 36-225 |

New technologies for measuring the brain are revolutionizing our understanding of the brain, and the revolution is data-driven. This track focuses on the statistical problems in neuroscience, including neural data analysis and neuroimaging. It is ideal for students interested in data science with an emphasis on brain and behavior or in neuroscience with an emphasis on data analysis.

**Academic Advisor:** Samantha Nielsen

**Faculty Advisor:** Valerie Ventura

Theory Requirements | |||

Course Topic/Title | Course Number | Units | Prerequisites |
---|---|---|---|

Calculus | 21-111 and 112, or 21-120 | 20 or 10 | |

Multivariate | 21-256, 21-259, or 21-268 | 9–10 | 21-112 or 21-120 |

Linear/Matrix Algebra | 21-240, 21-241, or 21-242 | 10 | |

Probability | 36-225, 36-217, 21-325, or 15-359 | 9 | 21-112, 21-122, 21-123, 21-256, or 21-259 |

Statistical Inference | 36-226 or 36-326 | 9 | C or higher in 36-217, 36-225, 21-325, or 15-359 |

Data-Analysis Requirements | |||

Course Topic/Title | Course Number | Units | Prerequisites |

Beginning Data Analysis | 36-200 | 9 | |

Intermediate Data Analysis | 36-202, 36-208, 36-290, or 36-309 | 9 | |

Advanced Elective | 36-311, 36-315, 36-303, 36-46x, or 36-490 | 9 | 36-202, 36-208, 36-290, or 36-309 |

Special Topics | 36-46x | 9 | various |

Modern Regression | 36-401 | 9 | C or higher in 36-226, 36-326, or 36-625 and pass 21-240 or 21-241 |

Advanced Methods for Data Analysis | 36-402 | 9 | C or higher in 36-401 |

Computing Requirements | |||

Course Topic/Title | Course Number | Units | Prerequisites |

Statistical Computing | 36-350 or 36-650/750 | 9 | (36-202 or 36-208 or 36-309 or 70-208, or 36-290 or equivalent) and 36-225 |

Neuroscience Requirements | |||

Course Topic/Title | Course Number | Units | Prerequisites |

Cognitive Psychology | 85-211 | 9 | |

Biological Foundations of Behavior | 85-219 | 9 | 85-100 or instructor approval |

Three Neuroscience Electives |
With |
27 | |

List of Approved Neuroscience Electives A: Methodology and Analysis | |||

Course Topic/Title | Course Number | Units | Prerequisites |

Probability and Mathematical Statistics or Intermediate Statistics | 36-700 or 36-705 | 12 | |

Machine Learning | 10-601 | 12 | 15-122 and (15-151 or 21-127) |

Systems Neuroscience | 18-290 | 12 | 18-100 |

Cognitive Science Research Methods | 85-314 | 12 | 36-309 |

Neural Data Analysis | 86-631 or 42-631 | 12 | |

List of Approved Neuroscience Electives B: Neuroscientific Background | |||

Course Topic/Title | Course Number | Units | Prerequisites |

Cellular Neuroscience | 03-362 | 9 | 85-219, 42-202, 03-161, or 03-240 |

Systems Neuroscience | 03-363 | 9 | 85-219, 42-202, 03-161, or 03-240 |

Neural Computation | 15-386 | 9 | 21-122 and 15-122 |

Cognitive Neuropsychology | 85-414 | 9 | 85-219 or 85-211 |

Intro to Parallel Distributed Processing | 85-419 | 9 | 85-213 or 85-211 |