Direction and advising can be sought from the Statistics Concentration Director, Assistant Professor Alice Paul, who can be reached at Alice_paul@brown.edu.

Administrative support and general assistance can be sought through the Academic Coordinator, Bethany Brown (Bethany_brown@brown.edu).

Capstone

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

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.

Statistics concentrators may choose to complete a capstone project or to pursue honors, which involves demonstrating good academic standing and completing an honors thesis with a faculty member. The requirements and deadlines for these two options are detailed below. 

Senior Capstone

To complete the capstone project, students must  

• Secure a faculty advisor for the capstone project.
• Complete the capstone declaration form with their capstone proposal and email the completed form to Bethany Brown (bethany_brown@brown.edu) by the third week of the student’s capstone semester.
• Meet regularly, as agreed upon, with their capstone advisor and provide regular updates on the project.
• Completion of one semester of an independent study course (PHP 1970) while working on the senior capstone.  If the advisor is not a Biostatistics faculty member, then the student may register for PHP 1970 under the Director of Undergraduate Studies.
• Give a poster presentation of the capstone project at the Undergraduate Poster day. 

Honors

Outline of Honors Requirements

Students are eligible for honors if they meet the following criteria. 

• Be in good academic standing by the end of the seventh (or penultimate) semester.
• Excellence in grades as demonstrated grades of A or S-with-distinction in at least 70% of the Brown University courses used for concentration credit.
• Completion of an in-depth, original research project carried out under the guidance of a Brown-affiliated faculty advisor.
• Completion of two semesters of independent study courses (PHP 1980) while working on the honors thesis.

Honors Thesis

In order to complete the honors thesis, students must 

• Secure a faculty advisor and a reader for the proposed honors thesis project. Either the advisor or reader must be a Biostatistics faculty member.
• Enroll in and complete two semesters of PHP 1980 or other independent study course.
• Complete the honors declaration form with their thesis proposal and email the completed form to Bethany Brown (bethany_brown@brown.edu) by the third week of the student’s penultimate semester.
• Meet regularly, as agreed upon, with their honors thesis advisor and provide regular updates on the thesis project.
• Give a poster presentation of the thesis project at the Undergraduate Poster day.
• Submit a full draft of the thesis for the advisor and reader.
• Email a copy of their approved and signed thesis to Bethany Brown (bethany_brown@brown.edu). The deadline for submission is Friday, December 13th 2024 for December 2024 graduates and Friday, April 25th 2025 for May 2025 graduates.

Formatting

Students should follow the format used in the following overleaf template. 

The program requires twelve 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.

Pre-Requisites

Single-variable calculus is not an enforced requirement for our concentration, but it is a required prerequisite for many of our courses. At Brown, single-variable calculus consists of MATH 0090 followed by one of MATH 0100, MATH 0170, or MATH 0190.

• PHP1501: Essentials of Data Analysis (AP Statistics with a score of 5 or another approved introductory statistics course may substitute for PHP 1501.)

Mathematical Foundations (1 Course)

• Linear Algebra and Multivariable Calculus for Applied Mathematicians (APMA0260)

  ^{Students may opt to take both Multivariable Calculus (MATH 0180, MATH 0200, or MATH 0350) and Linear Algebra (MATH 0520 or MATH 0540) to meet this requirement and count one of these courses towards their general elective requirements.}

Statistical Inference and Modeling (4 Courses)

• Introduction to Probability and Statistics with Theory (APMA 1655) 

  OR

•  Probability (MATH1210)

   

• Statistical Inference II (APMA1660)

  OR

• Mathematical Statistics (MATH1620)

• Using R for Data Analysis (PHP1560)

  OR

• Statistical Programming with R (PHP2560)

• Applied Regression Analysis (PHP1511)

Numerical Methods (Choose 1)

• Introduction to Scientific Computing (APMA 0160)
• Computational Probability and Statistics (APMA 1690)

• Advanced Numerical Methods for Data, Simulation, and Optimization (ENGN 1950)

   

Track Electives (3 Courses)

Students can choose from the following tracks and must complete three courses within a track. Other courses may be used with the approval of the Director of the Statistics Concentration.

Health Data Science Track

• Fundamentals of Epidemiology (PHP 0850)
• Introduction To Public Health Economics (PHP 1480)
• Infectious Disease Modeling (PHP 1855)
• The Epidemiology and Control of Infectious Diseases (PHP 1854)
• Applied Generalized Linear Models (PHP 2514)
• Applied Longitudinal Data Analysis ((Half-credit)) (PHP 2516)
• Applied Multilevel Data Analysis ((Half-credit)) (PHP 2517)
• Statistical Learning and Big Data (PHP 2650)
• Analysis of Lifetime Data (PHP 2602)

Economics Track

• Introduction To Public Health Economics (PHP 1480)
• Mathematical Econometrics I (ECON 1630)
• Mathematical Econometrics II (ECON 1640)
• Research Seminar in Health Economics (ECON 1360)
• Applied Research Methods for Economists (ECON 1629)
• Advanced Topics in Econometrics (ECON 1670)
• Operations Research: Probabilistic Models (APMA 1200)
• Monte Carlo Simulation with Applications to Finance (APMA 1720)

Statistical Theory Track

• Recent Applications of Probability and Statistics (APMA 1740)
• Statistical Analysis of Time Series (APMA 1670)
• Nonparametric Statistics (APMA 1680)
• Computational Probability and Statistics (APMA 1690)
• Real Analysis I (MATH 1630)
• Fundamentals of Probability and Statistical Inference (PHP 2515) or Statistical Inference I (PHP 2520)
• Causal Inference and Missing Data (PHP 2610)
• Bayesian Statistical Methods (PHP 2530)

Interdisciplinary Track

Students may design their own track with the approval of the Director of the Undergraduate Concentration.

General Electives (Choose 2)

Students must choose two general elective courses. Pre-approved courses are listed below, but courses may also be chosen from those courses listed in the track electives. Other courses may be used with the approval of the Director of the Statistics Concentration.

• Inference in Genomics and Molecular Biology (APMA 1080)
• An Introduction to Numerical Optimization (APMA 1160)
• Operations Research: Deterministic Models (APMA 1210)
• Information Theory (APMA 1710)
• Graphs and Networks (APMA 1860)
• Computational Methods for Studying Demographic History with Molecular Data (BIOL 1435)
• Machine Learning (CSCI 1420)
• Computer Vision (CSCI 1430)
• Deep Learning (CSCI 1470)
• Fairness in Automated Decision Making (CSCI 1491)
• Algorithmic Aspects of Machine Learning (CSCI 1520)
• Computational Molecular Biology (CSCI 1810)
• Algorithmic Foundations of Computational Biology (CSCI 1820)
• Data Science (CSCI 1951A)
• Pattern Recognition and Machine Learning (ENGN 2520)
• Market and Social Surveys (SOC 1120)
• Principles and Methods of Geographic Information Systems (SOC 1340)
• Techniques of Demographic Analysis (SOC 2230)
• Spatial Data Analysis Techniques in the Social Sciences (SOC 2960G)

Capstone Project (1 Course)

• Independent Study (PHP 1970)

OR

• Honors Thesis Preparation (PHP 1980)

Non-Credit Required Course:

PHP2000 Foundations of Public Health: As part of the Statistics Concentration, students are required to complete an online, asynchonous, non-credit course (PHP2000). This course is a requirement and is meant to give a broad overview of public health and it allows students to see different areas in public health where statistics is being used. Students who are in a double concentration in public health are exempt from this course.