Department of Statistics Unitmark
Dietrich College of Humanities and Social Sciences


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: Samantha Nielsen

Faculty Advisors: Peter Freeman and Mark Schervish

   

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
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 36-201  9
Intermediate Data Analysis 36-202, 36-208, or 36-309  9 various
Advanced Elective 36-315, 36-303, 36-46x, or 36-490  9 36-202, 36-208, 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 or 36-208 or 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 Advisor: Samantha Nielsen

Faculty Advisors: Ryan Tibshirani and Ann Lee

   

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 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 36-201  9
Intermediate Data Analysis 36-202, 36-208, or 36-309  9 various
Advanced Elective 36-315, 36-303, 36-46x, or 36-490  9 various
Advanced Elective 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 36-315, 36-303, 36-46x, or 36-490  9 various
Advanced Elective 36-315, 36-303, 36-46x, or 36-490  9
Advanced Elective 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 or 36-208 or 36-309 or 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

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

Theory Requirements
Course Topic/Title Course Number Units Prerequisites
Calculus 21-111 and 112, or 21-120 20 or 10
Advanced Analysis, one of:
  
Integration and Approximation
  
Concepts of Mathematics
  
Optimization
21-122
21-127
21-257 or 21-292
10
10
 9
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 36-201  9
Intermediate Data Analysis 36-202, 36-208, or 36-309  9
Advanced Elective 36-315, 36-303, 36-46x, or 36-490  9 36-202, 36-208, or 36-309
Advanced Elective 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 or 36-208 or 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

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: Samantha Nielsen

Faculty Advisor: Jing Lei

   

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 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 36-201  9
Intermediate Data Analysis 36-202, 36-208, or 36-309  9 various
Advanced Elective 36-315, 36-303, 36-46x, or 36-490  9 36-202, 36-208, 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 or 36-208 or 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: Samantha Nielsen

Faculty Advisor: Valerie Ventura

Statistics and Neuroscience Track

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 36-201  9
Intermediate Data Analysis 36-202, 36-208, or 36-309  9
Advanced Elective 36-315, 36-303, 36-46x, or 36-490  9 36-202, 36-208, 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 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 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
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