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 handson 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  21111 and 112, or 21120  20 or 10  
Multivariate Calc/Analysis  21256, 21259, or 21268  9–10  21112 or 21120 
Linear/Matrix Algebra  21240, 21241, or 21242  10  
Probability  36225, 36217, 21325, or 15359  9  21112, 21122, 21123, 21256, or 21259 
Statistical Inference  36226 or 36326  9  C or higher in 36217, 36225, 21325, or 15359 
DataAnalysis Requirements  
Course Topic/Title  Course Number  Units  Prerequisites 
Beginning Data Analysis  36201  9  
Intermediate Data Analysis  36202, 36208, or 36309  9  various 
Advanced Elective  36315, 36303, 3646x, or 36490  9  36202, 36208, or 36309 
Special Topics  3646x  9  various 
General Elective  various  9  various 
Modern Regression  36401  9  C or higher in 36226, 36326, or 36625 and pass 21240 or 21241 
Advanced Methods for Data Analysis  36402  9  C or higher in 36401 
Concentration Area (Four coherent, complementary courses) 
various  36  Advisor approval 
Computing Requirements  
Course Topic/Title  Course Number  Units  Prerequisites 
Statistical Computing  36350 or 36650/750  9  (36202 or 36208 or 36309 or 70208, or equivalent) and 36225 
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  21111 and 112, or 21120  20 or 10  
Integration and Approximation  21122  10  21112 or 21120 
Multivariate Calc/Analysis  21256, 21259, or 21268  9–10  21112 or 21120 
Concepts of Mathematics  21127  10  
Linear/Matrix Algebra  21240, 21241, or 21242  10  
Probability  36225, 36217, 21325, or 15359  9  21112, 21122, 21123, 21256, or 21259 
Statistical Inference  36226 or 36326  9  C or higher in 36217, 36225, 21325, or 15359 
DataAnalysis Requirements (Option 1)  
Course Topic/Title  Course Number  Units  Prerequisites 
Beginning Data Analysis  36201  9  
Intermediate Data Analysis  36202, 36208, or 36309  9  various 
Advanced Elective  36315, 36303, 3646x, or 36490  9  various 
Advanced Elective  36315, 36303, 3646x, or 36490  9  various 
Modern Regression  36401  9  C or higher in 36226, 36326, or 36625 and pass 21240 or 21241 
Advanced Methods for Data Analysis  36402  9  C or higher in 36401 
DataAnalysis Requirements (Option 2)  
Course Topic/Title  Course Number  Units  Prerequisites 
Advanced Elective  36315, 36303, 3646x, or 36490  9  various 
Advanced Elective  36315, 36303, 3646x, or 36490  9  ” 
Advanced Elective  36315, 36303, 3646x, or 36490  9  ” 
Modern Regression  36401  9  C or higher in 36226, 36326, or 36625 and pass 21240 or 21241 
Advanced Methods for Data Analysis  36402  9  C or higher in 36401 
Computing Requirements  
Course Topic/Title  Course Number  Units  Prerequisites 
Statistical Computing  36350 or 36650/750  9  (36202 or 36208 or 36309 or 70208, or equivalent) and 36225 
Fundamentals of Programming  15112  12  
Principles of Iterative Computation  15122  10  C or higher in 15112 
Machine Learning  10601/701  12  C or higher in (15122 or 15123) and (15151 or 21127) 
Algorithms and Advanced Data Structures  15351  12  15111, 15123, 15121, or 15122 
Machine Learning Elective  10405/605 15381 15386 16720 16311 11411 11761 
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  21111 and 112, or 21120  20 or 10  
Advanced Analysis, one of: Integration and Approximation Concepts of Mathematics Optimization 
21122 21127 21257 or 21292 
10 10 9 

Multivariate Calc/Analysis  21256, 21259, or 21268  9–10  21112 or 21120 
Linear/Matrix Algebra  21240, 21241, or 21242  10  
Probability  36225, 36217, 21325, or 15359  9  21112, 21122, 21123, 21256, or 21259 
Statistical Inference  36226 or 36326  9  C or higher in 36217, 36225, 21325, or 15359 
DataAnalysis Requirements  
Course Topic/Title  Course Number  Units  Prerequisites 
Beginning Data Analysis  36201  9  
Intermediate Data Analysis  36202, 36208, or 36309  9  
Advanced Elective  36315, 36303, 3646x, or 36490  9  36202, 36208, or 36309 
Advanced Elective  36315, 36303, 3646x, or 36490  9  ” 
General Elective  various  9  various 
Modern Regression  36401  9  C or higher in 36226, 36326, or 36625 and pass 21240 or 21241 
Advanced Methods for Data Analysis  36402  9  C or higher in 36401 
Computing Requirements  
Course Topic/Title  Course Number  Units  Prerequisites 
Statistical Computing  36350 or 36650/750  9  (36202 or 36208 or 36309 or 70208, or equivalent) and 36225 
Economics Requirements  
Course Topic/Title  Course Number  Units  Prerequisites 
Principles of Microeconomics  73102  9  
Principles of Macroeconomics  73103  9  73102 
Intermediate Microeconomics  73230  9  (21256 or 21259 or 21269 or 21268) and (73102 or 73100) 
Intermediate Macroeconomics  73240  9  (21259 or 21269 or 21268 or 21256) and (73103 or 73100) and (73230) 
Writing for Economists  73270  9  (76101) and (73230) and (73240) 
Econometrics I  73274  9  (21256 or 21259 or 21268 or 21269) and (36217 or 36225) and (73230) 
Econometrics II  73374  9  (21256 or 21259 or 21268 or 21269) and (36225 or 36217) and (73230) and (73274) 
Two advanced electives  73300 through 73495, excluding 73374  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  21111 and 112, or 21120  20 or 10  
Integration and Approximation  21122  10  21112 or 21120 
Multivariate Calc/Analysis  21256, 21259, or 21268  9–10  21112 or 21120 
Concepts of Mathematics  21127  10  
Linear/Matrix Algebra  21240, 21241, or 21242  10  
Probability  36225, 36217, 21325, or 15359  9  21112, 21122, 21123, 21256, or 21259 
Statistical Inference  36226 or 36326  9  C or higher in 36217, 36225, 21325, or 15359 
Principles of Real Analysis  21355  9  21127 and 21122 
Intro to Probability Modeling  36410  9  36225, 36217, 36325, or 36625 
Two of the following:  
Probability and Math Stat I Intermediate Statistics Discrete Math Optimization Combinatorics Real Analysis II 
36700 36705 21228 21257 or 21292 21301 21356 
12 12 9 9 9 9 
21127 or 15151 21240/1/2, 21256, 06262, or 18202 21127 or 15151 21240, 21241, 21242, 21256, 06262, or 18202 21122 and (15251 or 21228) (21259,21268,or 21269) and 21241/2 and 21355 
DataAnalysis Requirements  
Course Topic/Title  Course Number  Units  Prerequisites 
Beginning Data Analysis  36201  9  
Intermediate Data Analysis  36202, 36208, or 36309  9  various 
Advanced Elective  36315, 36303, 3646x, or 36490  9  36202, 36208, or 36309 
Special Topics  3646x  9  various 
General Elective  various  9  various 
Modern Regression  36401  9  C or higher in 36226, 36326, or 36625 and pass 21240 or 21241 
Advanced Methods for Data Analysis  36402  9  C or higher in 36401 
Computing Requirements  
Course Topic/Title  Course Number  Units  Prerequisites 
Statistical Computing  36350 or 36650/750  9  (36202 or 36208 or 36309 or 70208, or equivalent) and 36225 
New technologies for measuring the brain are revolutionizing our understanding of the brain, and the revolution is datadriven. 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  21111 and 112, or 21120  20 or 10  
Multivariate  21256, 21259, or 21268  9–10  21112 or 21120 
Linear/Matrix Algebra  21240, 21241, or 21242  10  
Probability  36225, 36217, 21325, or 15359  9  21112, 21122, 21123, 21256, or 21259 
Statistical Inference  36226 or 36326  9  C or higher in 36217, 36225, 21325, or 15359 
DataAnalysis Requirements  
Course Topic/Title  Course Number  Units  Prerequisites 
Beginning Data Analysis  36201  9  
Intermediate Data Analysis  36202, 36208, or 36309  9  
Advanced Elective  36315, 36303, 3646x, or 36490  9  36202, 36208, or 36309 
Special Topics  3646x  9  various 
Modern Regression  36401  9  C or higher in 36226, 36326, or 36625 and pass 21240 or 21241 
Advanced Methods for Data Analysis  36402  9  C or higher in 36401 
Computing Requirements  
Course Topic/Title  Course Number  Units  Prerequisites 
Statistical Computing  36350 or 36650/750  9  (36202 or 36208 or 36309 or 70208, or equivalent) and 36225 
Neuroscience Requirements  
Course Topic/Title  Course Number  Units  Prerequisites 
Cognitive Psychology  85211  9  
Biological Foundations of Behavior  85219  9  85100 or instructor approval 
Three Neuroscience Electives 
With at least one selected from each list 
27  
List of Approved Neuroscience Electives A: Methodology and Analysis  
Course Topic/Title  Course Number  Units  Prerequisites 
Probability and Mathematical Statistics or Intermediate Statistics  36700 or 36705  12  
Machine Learning  10601  12  15122 and (15151 or 21127) 
Systems Neuroscience  18290  12  18100 
Cognitive Science Research Methods  85314  12  36309 
Neural Data Analysis  86631 or 42631  12  
List of Approved Neuroscience Electives B: Neuroscientific Background  
Course Topic/Title  Course Number  Units  Prerequisites 
Cellular Neuroscience  03362  9  85219, 42202, 03161, or 03240 
Systems Neuroscience  03363  9  85219, 42202, 03161, or 03240 
Neural Computation  15386  9  21122 and 15122 
Cognitive Neuropsychology  85414  9  85219 or 85211 
Intro to Parallel Distributed Processing  85419  9  85213 or 85211 