Statistics is the science of learning from data. The theoretical foundation of statistics lies in probability theory, which is applied to decision-making under uncertainty. Data science consists of exploration, organization, representation, visualization, and modeling data. Statistics and data science together encompass a mode of questioning and reasoning that allows us to quantify uncertainty to make informed decisions. The Statistics and Data Science major provides a solid foundation in statistics through rigorous theoretical courses. Students also develop practical skills through applied courses and one year of interdisciplinary research. Most courses employ student-driven data projects that allow students to pursue their interests in various fields.
The Statistics and Data Science curriculum links faculty and students from across the college interested in learning things from data. One important goal is to prepare students for careers in data analytics, actuarial science, business, medicine, technology, law, finance, and related fields. Another crucial goal is to provide the background that is necessary for graduate study in statistics, biostatistics, data science, and other related fields. Our students will be well prepared for internships, Research Experiences for Undergraduates, or other research projects, in statistics, data science, and other related fields. The major concludes with an application and communication component which requires two semesters of research, preparing students for further data-centric investigations in the natural and social sciences. A degree in statistics and data science will enable students to enter the booming field of data science with the hands-on experience and training needed by today’s most innovative companies.
Learn more about Connections, Connecticut College's innovative new curriculum.
A Glimpse at Statistics and Data Science
Courses You Could Take
MAT 226 Linear Algebra
An introduction to topics in linear algebra, including systems of linear equations, matrices, determinants, vectors, vector spaces, linear transformations, eigenvalues, and eigenvectors.
STA 207 Advanced Regression Techniq
An introduction to simple linear regression, multiple linear regression, ordinary least squares estimation, model diagnostics, transformation of variables, weighted least squares estimation, variable selection, and logistic regression.
STA 209 Intro To Time Series Analysis
An introduction to the theory and methods of modern time series analysis.
STA 234 Statistical Computing With R
An introduction to statistical and computational thinking to solve problems with data using R. The course illustrates how to perform statistical computing in state-of-the art programming environment R/RStudio.
STA 317 Mathematical Statistics
An introduction to methods of statistical inference, with emphasis on the underlying mathematical theory. Topics include estimation, hypothesis testing, and modes of convergence.
STA 336 Statistical Machine Learning
An advanced course providing a comprehensive and detailed treatment of important topics in statistics and machine learning, including supervised and unsupervised learning techniques.
Professor of Mathematics, Assistant Chair of the Mathematics and Statistics Department, Consultant to the Registrar's Office
While pure mathematics occupies a great deal of Professor Hammond's attention, he also maintains an active interest in the liberal arts, particularly in topics relating to literature and religion. He is delighted whenever he can find connections between mathematics and the arts. He has given several talks on Dante's use of mathematical imagery in the Divine Comedy, as well as a lecture on the place of science and mathematics in Gulliver's Travels.
Whit Irwin
Adjunct Instructor of Mathematics and Statistics
Warren P. Johnson
Professor of Mathematics
Warren Johnson’s favorite area of mathematics is q-analysis. He has been working on a book on it for many years, which he used when he taught MAT 305 (Selected Topics). He loves the interplay between finite and infinite product/series identities and combinatorics that is one of the characteristic features of the subject. His other great love within mathematics is techniques of integration, so he appreciates his frequent opportunities to teach Calculus C.
Priya Kohli
Professor of Statistics, Chair of the Mathematics and Statistics Department, Chair-Elect Faculty Steering and Conference Committee
Professor Kohli’s work is driven by her passion for instilling statistically-informed thinking and making meaningful contributions. Her research, which is not just about theoretical beauty but also about practical utility, is making a significant impact. Priya Kohli's expertise spans various statistical and interdisciplinary research areas. She is particularly skilled in faculty salary modeling, covariance estimation, longitudinal data analysis, spatial random fields, and biostatistics. Her research also addresses challenges arising in interdisciplinary research in Ischemic hepatitis, RNA-seq, healthcare, and environmental sciences, demonstrating the practical relevance of her work. Her research accomplishments include various publications, U.S. and European patents, and being a speaker at several prestigious statistics and data science conferences. Drawing students into research is also at the core of her mission as an educator and researcher. She makes her research accessible to undergraduates and has several peer-reviewed articles with undergraduate students as co-authors.
Augustine B. "Tina" O'Keefe
Associate Professor of Mathematics
Augustine O'Keefe's research lies at the intersection of commutative algebra, combinatorics, and topology. In particular, she is interested in monomial and toric binomial ideals defined from combinatorial objects such as discrete graphs and simplicial complexes. The overarching goal is then to get a handle on the algebraic structure of the ideal by utilizing the combinatorial and topological structure of the defining object. The advantage to building this connection is that combinatorial objects lend themselves to running small examples in order to find overall patterns. Having concrete objects to play with, makes this area of mathematics particularly suitable for undergraduate research.
Perry D. Susskind
Professor of Mathematics
Perry Susskind teaches Multivariable Calculus, Calculus C: Integrals and Series, Linear Algebra, Real Analysis I and II and Seminar in Mathematics.
Vincent Thompson
Associate Teaching Professor of Mathematics and Statistics
Vince Thompson began his career at Connecticut College in 2011 as an adjunct professor and took on as visiting lecturer in mathematics in 2013. Thompson primarily teaches introductory courses and is skilled at presenting mathematics and statistics in a way that is accessible to students who may approach learning math with apprehension.
Thompson has taught introductory math, introductory statistics, and calculus, focusing on teaching and thinking through math while helping students to better understand mathematical concepts.
Thompson spent 23 years as a merchant marine officer and holds a captain's license. Before arriving at Conn, Thompson taught math at the high school level and continues to be certified for secondary math education in Connecticut. His lengthy career working on a variety of ships, including oil tankers, containerships and bulk carriers, combined with his experience as a high school math teacher and as a musician add another dimension to his teaching that makes him especially effective in reaching his students.
Yan Zhuang
Associate Professor of Statistics
Yan Zhuang specializes in the areas of sequential analysis, sampling strategies, sample size determination, and statistical inference. She also enjoys working with people from other disciplines to conduct interdisciplinary research. Her current interdisciplinary collaboration projects include violations of drinking water quality and Medicare quality analysis.
Courses You Could Take
Linear Algebra, Advanced Regression Techniq, Intro To Time Series Analysis, Statistical Computing With R, Mathematical Statistics, Statistical Machine Learning
People You Might Work With
Christopher Hammond, Professor of Mathematics
B.A., University of the South; M.S., Ph.D., University of Virginia
Operator theory; Complex analysis; Series Convergence Tests
Whit Irwin, Adjunct Instructor of Mathematics and Statistics
Warren P. Johnson, Professor of Mathematics
B.S., University of Minnesota; Ph.D., University of Wisconsin
q-analysis; Calculus; Determinants
Priya Kohli, Professor of Statistics
M.S., Indian Agricultural Statistical Research Institute, New Delhi; M.S., Northern Illinois University, Ph.D., Texas A&M University; HERS Alum 2023
Connecticut College Mathematics and Statistics Department 270 Mohegan Ave. New London, CT 06320
Department Assistant
Noel Brown
Campus Location
Fanning Hall
Courses You Could Take
Linear Algebra, Advanced Regression Techniq, Intro To Time Series Analysis, Statistical Computing With R, Mathematical Statistics, Statistical Machine Learning
People You Might Work With
Christopher Hammond, Professor of Mathematics
B.A., University of the South; M.S., Ph.D., University of Virginia
Operator theory; Complex analysis; Series Convergence Tests
Whit Irwin, Adjunct Instructor of Mathematics and Statistics
Warren P. Johnson, Professor of Mathematics
B.S., University of Minnesota; Ph.D., University of Wisconsin
q-analysis; Calculus; Determinants
Priya Kohli, Professor of Statistics
M.S., Indian Agricultural Statistical Research Institute, New Delhi; M.S., Northern Illinois University, Ph.D., Texas A&M University; HERS Alum 2023
