Majors/Minor
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.
Explore the requirements for each of the majors and the minor that we provide in our department:
Statistics Core Major
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.
CONTACT THE STATISTICS & DATA SCIENCE ACADEMIC ADVISORS
Major Requirements
Theory Requirements |
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| 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 | 10–11 | |
| Probability | 36-235, 36-225, 36-218, 36-219, 21-325 or 15-259 | 9 | various |
| Statistical Inference | 36-236, 36-226, or 36-326 | 9 | C or higher in 36-235, 36-225, 36-219, 36-218, 21-325 or 15-259 |
Data-Analysis Requirements |
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| 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 | 36-303, 36-311, 36-315, 36-318, 36-46x, 36-47x, 36-490, 36-493 or 36-497 | 9 | 36-202, 36-208, 36-290, or 36-309 |
| Special Topics | 36-46x and 36-47x | 9 | various |
| Modern Regression | 36-401 | 9 | C or higher in 36-226, 36-236, or 36-326 and pass (21-240 or 21-241) |
| Advanced Methods for Data Analysis | 36-402 | 9 | C or higher in 36-401 |
| Concentration Area
This can be fulfilled by four courses or an approved Minor or Additional Major, in another department, that compliment Statistics & Data Science. |
Examples of Common Concentrations
*This is not an exhaustive list |
36 | Advisor approval needed |
Computing Requirements |
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| Course Topic/Title | Course Number | Units | Prerequisites |
| Statistical Computing | 36-350 | 9 | (36-202, 36-208, 36-290, 36-309 or 70-208, or equivalent), 36-225 and 36-235 |
| Fundamentals of Programming *Beginning with EY2023-2024 |
15-110/112 | 12 | |
Statistics and Machine Learning Major
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.
CONTACT THE STATISTICS & DATA SCIENCE ACADEMIC ADVISORS
Major Requirements
Theory Requirements
|
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| 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 |
| Concepts of Mathematics | 21-127 | 12 | |
| Linear/Matrix Algebra | 21-240, 21-241, or 21-242 | 10–11 | |
| Probability | 36-235, 36-225, 36-218, 36-219, 21-325, or 15-259 | 9 | various |
| Statistical Inference | 36-236, 36-226, or 36-326 | 9 | C or higher in 36-235, 36-225, 36-219, 36-218, 21-325 or 15-259 |
Data-Analysis Requirements (Option 1) |
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| 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 | 36-303, 36-311, 36-315, 36-318, 36-46x, 36-47x, 36-490, 36-493 or 36-497 | 9 | various |
| Advanced Elective | 36-303, 36-311, 36-315, 36-318, 36-46x, 36-47x, 36-490, 36-493 or 36-497 | 9 | various |
| Modern Regression | 36-401 | 9 | C or higher in 36-226, 36-235, or 36-326 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) |
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| Course Topic/Title | Course Number | Units | Prerequisites |
| Advanced Elective | 36-303, 36-311, 36-315, 36-318, 36-46x, 36-47x, 36-490, 36-493 or 36-497 | 9 | various |
| Advanced Elective | 36-303, 36-311, 36-315, 36-318, 36-46x, 36-47x, 36-490, 36-493 or 36-497 | 9 | ” |
| Advanced Elective | 36-303, 36-311, 36-315, 36-318, 36-46x, 36-47x, 36-490, 36-493 or 36-497 | 9 | ” |
| Modern Regression | 36-401 | 9 | C or higher in 36-226, 36-235, or 36-326 and pass (21-240 or 21-241) |
| Advanced Methods for Data Analysis | 36-402 | 9 | C or higher in 36-401 |
Computing Requirements |
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| Course Topic/Title | Course Number | Units | Prerequisites |
| Statistical Computing | 36-350 or 36-650 | 9 | (36-202, 36-208, 36-290, 36-309, 70-208, or equivalent) and 36-225 or 36-235 |
| Fundamentals of Programming | 15-112 | 12 | |
| Principles of Iterative Computation | 15-122 | 10 | C or higher in 15-112 |
| Machine Learning | 10-301/315/701 | 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 | See StatML audit in Stellic for current list | 9 | vary by elective |
Economics and Statistics Major
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.
CONTACT THE STATISTICS & DATA SCIENCE ACADEMIC ADVISORS
CONTACT THE ECONOMICS ACADEMIC ADVISORS
Major Requirements
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 | 10–11 | |
| Probability | 36-235, 36-225, 36-218, 36-219, 21-325, or 15-259 | 9 | various |
| Statistical Inference | 36-236, 36-226, or 36-326 | 9 | C or higher in 36-235, 36-225, 36-219, 36-218, 21-325 or 15-259 |
Data-Analysis Requirements |
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| 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 | 36-303, 36-311, 36-315, 36-318, 36-46x, 36-490, 36-493 or 36-497 | 9 | 36-202, 36-208, 36-290, or 36-309 |
| Advanced Elective | 36-303, 36-311, 36-315, 36-318, 36-46x, 36-490, 36-493 or 36-497 | 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 |
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| Course Topic/Title | Course Number | Units | Prerequisites |
| Statistical Computing | 36-350 | 9 | (36-202, 36-208, 36-290, 36-309 or 70-208, or equivalent) and 36-225 |
| Fundamentals of Programming *Beginning with EY2023-2024 |
15-110 or 15-112 | 12 | |
Economics Requirements |
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| Course Topic/Title | Course Number | Units | Prerequisites |
| Principles of Microeconomics | 73-102 or 73-104 | 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) |
| Economics and Data Science | 73-265 | 9 | (21120) and (36200 or 36201) and (73100 or 73102) |
| Econometrics I | 73-274 | 9 | (21256 or 21259 or 21268 or 21269) and (36225) and (73230) |
| Advanced Quantitative Analysis | 73-374, 73-423, 70-467 | 9 | various |
| Two advanced electives | 73-300 through 73-495, excluding 73-374 | 18 | various |
Statistics Major - Mathematical Science Track
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.
CONTACT THE STATISTICS & DATA SCIENCE ACADEMIC ADVISORS
Major Requirements
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 | 12 | |
| Linear/Matrix Algebra | 21-240, 21-241, or 21-242 | 10–11 | |
| Probability | 36-235, 36-225, 36-218, 36-219, 21-325, or 15-259 | 9 | various |
| Statistical Inference | 36-236, 36-226, or 36-326 | 9 | C or higher in 36-235, 36-225, 36-219, 36-218, 21-325 or 15-259 |
| Principles of Real Analysis | 21-355 | 9 | 21-127 and 21-122 |
| Intro to Probability Modeling | 36-410 | 9 | 36-235, 36-225, 36-218, 36-219, or 36-625 |
| Two of the following: | |||
| Probability and Math Intermediate Statistics Discrete Math Optimization Combinatorics Real Analysis II |
36-700 21-228 21-257 or 21-292 21-301 21-356 |
12 9 9 9 9 |
21-127 or 15-151 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 |
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| 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 | 36-303, 36-311, 36-315, 36-318, 36-47x, 36-46x, or 36-490, 36-493 or 36-497 | 9 | 36-202, 36-208, 36-290, or 36-309 |
| Special Topics | 36-46x or 36-47x | 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 |
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| Course Topic/Title | Course Number | Units | Prerequisites |
| Statistical Computing | 36-350, 15-110 or 15-112 | 9 | 36-235/219 and 36-225 |
| Fundamentals of Programming *Beginning with EY2023-2024 |
15-110 or 15-112 | 12 | |
Statistics Major - Neuroscience Track
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.
CONTACT THE STATISTICS & DATA SCIENCE ACADEMIC ADVISORS
Major Requirements
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 | 10–11 | |
| Probability | 36-235, 36-225, 36-218, 36-219, 21-325, or 15-259 | 9 | various |
| Statistical Inference | 36-236, 36-226, or 36-326 | 9 | C or higher in 36-235, 36-225, 36-219, 36-218, 21-325 or 15-259 |
Data-Analysis Requirements |
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| 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 | 36-303, 36-311, 36-315, 36-318, 36-47x, 36-46x, or 36-490, 36-493 or 36-497 | 9 | various |
| Special Topics | 36-46x or 36-47x | 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 | 9 | 36-235 and 36-225 |
| Fundamentals of Programming *Beginning with EY2023-2024 |
15-110 or 15-112 | 12 | |
Neuroscience Requirements |
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| Course Topic/Title | Course Number | Units | Prerequisites |
| Cognitive Psychology | 85-211 | 9 | |
| Biological Foundations of Behavior | 85-219 | 9 | |
| Three Neuroscience Electives |
With at least one selected from each list |
27 | |
List of Approved Neuroscience Electives A: Methodology and Analysis |
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| Course Topic/Title | Course Number | Units | Prerequisites |
| Probability and Mathematical Statistics or Intermediate Statistics | 36-700 | 12 | |
| Machine Learning | 10-301, 10-315 or 10-701 | 12 | 15-122 and (15-151 or 21-127) |
| Signals and Systems | 18-290 | 12 | |
| Cognitive Science Research Methods | 85-314 | 12 | 36-309, 85-211 or 85-219 |
| Neural Data Analysis | 86-631 or 42-631 | 12 | |
List of Approved Neuroscience Electives B: Neuroscientific Background |
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| 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 |
Statistics Minor
In order to declare the minor, students need to complete this form.
CONTACT THE STATISTICS & DATA SCIENCE ACADEMIC ADVISORS
Minor Requirements
Theory Requirements |
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| 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 | 10–11 | |
| Probability | 36-235, 36-225, 36-218, 36-219, 21-325, or 15-259 | 9 | various |
| Statistical Inference | 36-236, 36-226, or 36-326 | 9 | C or higher in 36-235, 36-225, 36-219, 36-218, 21-325 or 15-259 |
Data-Analysis Requirements |
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| 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 |
| Modern Regression | 36-401 | 9 | C or higher in 36-236, 36-226, 36-326, or 36-625 and pass (21-240 or 21-241) |
| Advanced Methods for Data Analysis | 36-402, 36-410, 36-47x, 36-46x, 36-490, 36-493, 36-497 | 9 | C or higher in 36-401 |
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 or minor, please see our Undergraduate Catalog.