## Undergraduate Majors

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

## Stat Core

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: Glenn Clune

Faculty Advisors: Peter Freeman

### Major Requirements

Course Topic/Title Course Number Units Prerequisites Theory Requirements 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, 36-218, 36-219, 21-325, or 15-359 9 various Statistical Inference 36-226 or 36-326 9 C or higher in 36-225, 36-219, 36-218, 21-325, or 15-359 Data-Analysis Requirements 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, 36-490, 36-493 or 36-497 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) and (21-256 or 21-259 or 21-268) 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 Business Computer Science Humanities Analytics Minor Computational Finance Computational Biology Psychology Social and Decision Sciences Integrative Design, Arts, and Technology *This is not an exhaustive list 36 Advisor approval needed Computing Requirements Statistical Computing 36-350 or 36-650 9 (36-202, 36-208, 36-290, 36-309 or 70-208, or equivalent) and 36-225

## StatML

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 Advisors: Amanda Mitchell and Samantha Nielsen

Faculty Advisors: Ryan Tibshirani and Ann Lee

### Major Requirements

Course Topic/Title Course Number Units Prerequisites Theory Requirements 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, 36-218, 36-219, 21-325, or 15-359 9 various Statistical Inference 36-226 or 36-326 9 C or higher in 36-225, 36-218, 36-219, 21-325, or 15-359 Data-Analysis Requirements (Option 1) 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, 36-490, 36-493 or 36-497 9 various Advanced Elective 36-311, 36-315, 36-303, 36-46x, 36-490, 36-493 or 36-497 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) and (21-256 or 21-259 or 21-268) Advanced Methods for Data Analysis 36-402 9 C or higher in 36-401 Data-Analysis Requirements (Option 2) Advanced Elective 36-311, 36-315, 36-303, 36-46x, 36-490, 36-493 or 36-497 9 various Advanced Elective 36-311, 36-315, 36-303, 36-46x, 36-490, 36-493 or 36-497 9 ” Advanced Elective 36-311, 36-315, 36-303, 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) and (21-256 or 21-259 or 21-268) Advanced Methods for Data Analysis 36-402 9 C or higher in 36-401 Computing Requirements Statistical Computing 36-350 or 36-650 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 10-301/701 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 10-405/60515-38115-38616-72016-31111-41111-761 9 vary by elective

## EconStat

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

### Major Requirements

Course Topic/Title Course Number Units Prerequisites Theory Requirements 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, 36-218, 36-219, 21-325, or 15-359 9 various Statistical Inference 36-226 or 36-326 9 C or higher in 36-225, 36-218, 36-219, 21-325, or 15-359 Data-Analysis Requirements 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, 36-490, 36-493 or 36-497 9 36-202, 36-208, 36-290, or 36-309 Advanced Elective 36-311, 36-315, 36-303, 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) and (21-256 or 21-259 or 21-268) Advanced Methods for Data Analysis 36-402 9 C or higher in 36-401 Computing Requirements Statistical Computing 36-350 or 36-650 9 (36-202, 36-208, 36-290, 36-309 or 70-208, or equivalent) and 36-225 Economics Requirements 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) 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) Econometrics II 73-374 9 (21256 or 21259 or 21268 or 21269) and (36225) and (73230) and (73274) Two advanced electives 73-300 through 73-495, excluding 73-374 18 various

## MathStat

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: Glenn Clune

Faculty Advisor: Jing Lei

### Major Requirements

Course Topic/Title Course Number Units Prerequisites Theory Requirements 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, 36-218, 36-219, 21-325, or 15-359 9 various Statistical Inference 36-226 or 36-326 9 C or higher in 36-225, 36-218, 36-219, 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, 36-218, 36-219, 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 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, 36-493 or 36-497 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) and (21-256 or 21-259 or 21-268) Advanced Methods for Data Analysis 36-402 9 C or higher in 36-401 Computing Requirements Statistical Computing 36-350 or 36-650 9 (36-202, 36-208, 36-290, 36-309 or 70-208, or equivalent) and 36-225

## StatNeuro

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: Glenn Clune

Faculty Advisor: Valerie Ventura

### Statistics and Neuroscience Track

Course Topic/Title Course Number Units Prerequisites Theory Requirements 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, 36-218, 36-219, 21-325, or 15-359 9 various Statistical Inference 36-226 or 36-326 9 C or higher in 36-225, 36-218, 36-219, 21-325, or 15-359 Data-Analysis Requirements 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, 36-493 or 36-497 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) and (21-256 or 21-259 or 21-268) Advanced Methods for Data Analysis 36-402 9 C or higher in 36-401 Computing Requirements Statistical Computing 36-350 or 36-650 9 (36-202 or 36-208 or 36-309 or 70-208, or 36-290 or equivalent) and 36-225 Neuroscience Requirements Cognitive Psychology 85-211 9 Biological Foundations of Behavior 85-219 9 85-100 or instructor approval Three Neuroscience Electives With at least one selected from each list(A) Methodology and Analysis and(B) Neuroscientific Background. 27 List of Approved Neuroscience Electives A: Methodology and Analysis Probability and Mathematical Statistics or Intermediate Statistics 36-700 or 36-705 12 Machine Learning 10-301 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 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