Undergraduate Concentration

The Undergraduate Concentration in Statistics, established in 1997 as an interdepartmental program leading to the Sc.B. degree, was administered originally by the Center for Statistical Sciences and is now under the auspices of the Department of Biostatistics.  

The Concentration is based on several premises: that statistics is a scientific discipline in its own right, with specialized methodologies and body of knowledge; that it is essentially concerned with the art and science of data analysis; and that it is best taught in conjunction with specific, substantive applications. To this end, the Concentration is designed to provide foundations that include basic statistical concepts and methodologies, and to expose students to the role of statistical thinking and analysis in interdisciplinary research and in the public sphere.  The Concentration prepares students for careers in industry and government, for graduate study in statistics or biostatistics and other sciences, as well as for professional study in law, medicine, business, or public administration.

Diversity Statement:
The Department of Biostatistics has a deep respect for the views of all people.  We welcome and, indeed, seek differences in opinion, experiences and individuals.  The Department is dedicated to an open and welcoming environment where diverse cultures and lifestyles can converge and work together to strengthen, inform and develop the science of statistics.  We encourage the infusion of our undergraduate concentrators to enrich and strengthen the education, research and collaborative spirit within Brown University’s Department of Biostatistics and the School of Public Health.

Direction and advising can be sought from the Statistics Concentration Director, Associate Professor Roee Gutman, who can be reached at [email protected].

Administrative support and general assistance can be sought through the Academic Department Manager, Denise Arver ([email protected]), or the Student Affairs Coordinator, Rose Benar ([email protected]).

The capstone experience enables students to participate in an extensive data analysis project or in the development and evaluation of statistical methods. The usual format is a semester-long independent study under the supervision of one of the professors in the Department of Biostatistics or affiliated departments. During the independent study, students are expected to work with a professor on research projects that include major statistical components. When the supervising professor is outside of the Department of Biostatistics, the Director of the Undergraduate Statistics Concentration oversees the capstone experience to ensure it includes a substantial statistical component that fulfills the intentions of the Concentration.

An example of recent capstone project includes an analysis of the United States Renal Data System to identify whether doctors tend to increase the doses of Erythropoiesis-stimulating agent towards the end of life. Another example is an evaluation of a method designed to identify pairs of Veteran Administration nursing homes with similar past hospitalization and mortality rates that will be randomized within pairs in a future clinical trial. 

Honors in statistics requires the completion of a senior thesis and a superior record in the program. Concentrators who choose to write honor thesis are required to write a manuscript that describes a major project of statistical data analysis that they performed or a simulation study to evaluate the performance of a statistical method. Students that decide to write an honor thesis will generally integrate their capstone project into their thesis.

Examples of recent honor thesis include a comparison of the performance of Integrated Nested Laplacian Approximation to Markov chain Monte Carlo algorithm for missing data imputation, and A Bayesian multinomial logistic regression analysis of the influence of genetic predisposition for risk-taking and perceived behavior of role models on Mexican-American adolescent alcohol use.

The program requires thirteen one-semester courses. The required courses are articulated below.  Please note that only the required Calculus courses may be accepted with P/F grades.  All other required courses must be taken for a grade.

Level I: Foundations in Mathematics - Calculus  

Introductory Calculus, Part II (MATH 0100)
Intermediate Calculus (MATH 0180)

Level I: Foundations in Mathematics – Linear Algebra 

Linear Algebra (MATH 0520)

Computing (1 of 2)

Introduction to Scientific Computing (APMA 0160)
Introduction to Scientific Computing and Problem Solving (CSCI 0040)

Introduction to Statistical Thinking and Practice 

Essentials of Data Analysis (PHP 1501)

With the approval of the Director of the Statistics Concentration – one of these courses may serve as replacement

Introductory Statistics for Social Research (SOC 1100)
Introduction to Econometrics (ECON 1620)
Essential Statistics (APMA 0650)
Statistical Analysis of Biological Data (BIOL 0495) 
Introductory Statistics for Education Research and Policy Analysis (EDUC 1110) 
Quantitative Methods in Psychology (CLPS 0900) 

Level II: Core Courses in Theory and Data Analysis

Foundations of Mathematical Statistics: (Students must choose either of the two sequences)

Statistical Inference I (APMA 1650 OR APMA 1655)
Statistical Inference II (APMA 1660)


Probability (MATH 1610)
Mathematical Statistics (MATH 1620)

Introduction to Biostatistics

Principles of Biostatistics and Data Analysis (PHP1510 OR PHP 2510)

Level III: Advanced Courses in Statistical Methods

Statistical Computing

Statistical Computing I (PHP 1560 OR PHP 2560)

Statistical Modeling

Applied Regression Analysis (PHP 1511 OR PHP 2511)

Electives in Social Science and Biostatistics (Students must choose two.)

Market and Social Surveys (SOC 1120)
Principles and Methods of Geographic Information Systems (SOC 1340)
Techniques of Demographic Analysis (SOC 2230)
Machine Learning (CSCI 1420)
Computational Molecular Biology (CSCI 1810)
Algorithmic Foundations of Computational Biology (CSCI 1820)
Data Science (CSCI 1951A)
Fundamentals of Epidemiology (PHP 0850) 
Clinical Trials Methodology (PHP 2030) 
Introduction to Methods in Epidemiologic Research (PHP 2120) 
Intermediate Methods in Epidemiologic Research (PHP 2200)
Fundamentals of Probability and Statistical Inference (PHP 2515)
Statistical Inference I (PHP 2520)
Bayesian Statistical Methods (PHP 2530)
Practical Data Analysis (PHP 2550)
Statistical Inference II (PHP 2580)
Analysis of Lifetime Data (PHP 2602)
Linear Models (PHP 2601)
Statistical Methods for Spatial Data (PHP 2604)
Causal Inference and Missing Data (PHP 2610)
Statistical Methods in Bioinformatics, I (PHP 2620)
Quantitative Models of Biological Systems (APMA 1070)
Inference in Genomics and Molecular Biology (APMA 1080)
Operations Research: Probabilistic Models (APMA 1200)
Computational Probability and Statistics (APMA 1690)
Information Theory (APMA 1710)
Recent Applications of Probability and Statistics (APMA 1740)
Graphs and Networks (APMA 1860)
Recent Applications of Probability and Statistics (APMA 2610)
Pattern Recognition and Machine Learning (ENGN 2520)
Introduction to Programming for the Mind, Brain and Behavior (CLPS 1292)
Computational Cognitive Neuroscience (CLPS 1492)
Econometrics I (ECON 1630)
Econometrics II (ECON 1640)
Health Ecoomics (ECON 1360)
Big Data (ECON 1660)
Applied Algebraic Topology (MATH 1810A)
Other Analytical / Computational /Statistical courses with the approval of the Director of the Statistics Concentration

Prospective students may obtain Advanced Placement credit for the requirements in mathematics, computing, and introductory statistics. Students who have already completed an introductory course in statistics will be granted permission to proceed to Level II core courses if they meet the prerequisites in mathematics and computing. Honors work in statistics requires the completion of a senior thesis and superior record in the concentration.