The Bachelor of Science degree in Statistics 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. To ensure deep rigorous understanding of the foundations and main methods of analysis in statistics, the program is composed of three parts: a) foundations in mathematics and computing, combined with an introduction to statistical thinking and practice; b) four core courses on the fundamentals of statistical theory and data analysis; and c) more advanced material covering important areas of statistical methodology. A capstone project involving substantial data analysis or focused on methodology/theory is required. Students also have opportunities to acquire practical experience in study design, data management, and statistical analysis by working as undergraduate research assistants in projects in one of the participating academic departments or Research Centers at Brown.
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. The undergraduate concentration guide is available here.
The Undergraduate Concentration in Statistics is administered by the Department of Biostatistics and leads to a Sc.B. degree. To ensure deep rigorous understanding of the foundations and main methods of analysis in statistics, the program is composed of three parts. The first part entails foundations in mathematics and computing, combined with an introduction to statistical thinking and practice. The second part includes four core courses that provide a comprehensive account of the fundamentals of statistical theory and data analysis. The third part delves into more advanced material covering important areas of statistical methodology. In addition to the formal coursework, students are required to complete a capstone project that involves a substantial data analysis or a methodological/theoretical project. Students also have opportunities to acquire practical experience in study design, data management, and statistical analysis by working as undergraduate research assistants in projects in one of the participating academic Departments or Research Centers at Brown.
The program requires thirteen one-semester courses. The required courses are as follows:
| LEVEL I: Foundations in Mathematics - Calculus | 2 | |
| Single Variable Calculus, Part II | ||
| Multivariable Calculus | ||
| LEVEL I - Foundations in Mathematics - Linear Algebra | 1 | |
| Linear Algebra | ||
| Computing | 1 | |
| Introduction to Scientific Computing | ||
or CSCI 0040 | Introduction to Scientific Computing and Problem Solving | |
| Introduction to Statistical Thinking and Practice | 1 | |
| Essentials of Data Analysis | ||
With the approval of the Director of the Statistics Concentration, one of the following courses may serve as replacement: | ||
| Introductory Statistics for Social Research | ||
| Introduction to Econometrics | ||
| Essential Statistics | ||
| Statistical Analysis of Biological Data | ||
| Statistical Methods | ||
| LEVEL II - Core Courses in Theory and Data Analysis | 2 | |
| Statistical Inference I | ||
or APMA 1655 | Honors Statistical Inference I | |
| Statistical Inference II | ||
OR | ||
| Probability | ||
| Mathematical Statistics | ||
| Introduction to Biostatistics | 1 | |
| Principles of Biostatistics and Data Analysis | ||
OR | ||
| Principles of Biostatistics and Data Analysis | ||
| LEVEL III: Advanced Courses in Statistical Methods | 2 | |
| Statistical Programming in R | ||
OR | ||
| Statistical Programming with R | ||
| AND | ||
| Applied Regression Analysis | ||
OR | ||
| Applied Regression Analysis | ||
| Capstone Project | 1 | |
| Independent Study | ||
| Electives in Social Science and Biostatistics (Students must choose 2) | 2 | |
| Market and Social Surveys | ||
| Principles and Methods of Geographic Information Systems | ||
| Techniques of Demographic Analysis | ||
| Machine Learning | ||
| Computational Molecular Biology | ||
| Algorithmic Foundations of Computational Biology | ||
| Data Science | ||
| Fundamentals of Epidemiology | ||
| Clinical Trials Methodology | ||
| Introduction to Methods in Epidemiologic Research | ||
| Intermediate Methods in Epidemiologic Research | ||
| Fundamentals of Probability and Statistical Inference | ||
| Statistical Inference I | ||
| Bayesian Statistical Methods | ||
| Practical Data Analysis | ||
| Statistical Inference II | ||
| Analysis of Lifetime Data | ||
| Linear Models | ||
| Causal Inference and Missing Data | ||
| Statistical Methods in Bioinformatics, I | ||
| Quantitative Models of Biological Systems | ||
| Inference in Genomics and Molecular Biology | ||
| Operations Research: Probabilistic Models | ||
| Computational Probability and Statistics | ||
| Information Theory | ||
| Recent Applications of Probability and Statistics | ||
| Graphs and Networks | ||
| Recent Applications of Probability and Statistics | ||
| Pattern Recognition and Machine Learning | ||
| Introduction to Programming for the Mind, Brain and Behavior | ||
| Computational Cognitive Neuroscience | ||
| Health Economics | ||
| Mathematical Econometrics I | ||
| Mathematical Econometrics II | ||
| Big Data | ||
| Applied Algebraic Topology | ||
Other Analytical/Computational/ Statistical courses with the approval of the Director of the Statistics Concentration | ||
| Total Credits | 13 | |
Prospective students will be able to obtain Advanced Placement credit for the requirements in mathematics. 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.
PHP 0100: As part of the capstone course or thesis, students should complete an online course, PHP 0100, at their own pace. 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. The course does not require any additional credit and is completed as part of the independent study, PHP 1970/1980. Students who are in a double concentration in public health are exempt from this course.
Senior Thesis: A senior honors thesis is not a requirement for graduation, but concentrators who choose to write one 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. Generally, writing a senior thesis includes two semesters of independent study (PHP 1980), the capstone project may serve as one of those.
Honors:
Statistics requires the completion of a senior thesis and a superior record in the program.
Study Abroad/Study Away: Up to two courses taken elsewhere (study abroad or other transfer) may be applied to required courses. Meet with a concentration adviser to discuss; provide a syllabus for each course to be considered for transfer to your concentration plan.
The program is administered by the Department of Biostatistics, located at 121 South Main Street, 7th floor.
For additional information please contact: Roee Gutman, Box G-S-121-7; Telephone: 401-863-2682; Fax: 401-863-9182; e-mail: Roee Gutman