Data Science Overview
Program Committee
Clark Bowman
Courtney Gibbons, director
Karyn Doke
Heather Kropp
Erin Tripp
The goal of the Data Science Program is to provide comprehensive training in this growing interdisciplinary field. Through courses in mathematics, statistics, computing, and applied domains (e.g. government, environmental science, sociology), students explore the societal impact of data science and such ethical concerns as privacy rights and data validity.
Students will:
- Gain proficiency in the data life cycle: creation, curation, documentation, analysis, and communication.
- Apply data science tools to real world problems and produce well documented and reproducible analyses.
- Understand the social and ethical impact of the tools used in data science.
A Concentration in Data Science consists of 12 courses:
Three courses from Mathematics and Statistics: MATH-116, MATH-254 and MATH-351. (Note: An introductory applied statistics course such as MATH-152, ECON-166, GOVT-230, ENVST-206 or PSYCH/NEURO-201 is a prerequisite for MATH-254.)
One foundational course: DATSC-201.
Four courses from Computer Science: CPSCI-101, CPSCI-102, CPSCI-130, and CPSCI-230.
For students starting at Hamilton prior to Fall 2026: MATH-216 may be used as a substitute for DATSC-201, and the sequence CPSCI-230, CPSCI-330 may be used as a substitute for CPSCI-130, CPSCI-230. If CPSCI-330 is used as one of the four computer sciences classes, it may not also count as a foundational depth course.
A Senior Program consisting of a Senior Seminar in Mathematics and Statistics or the Senior Seminar in Computer Science.
And one elective from each of the Foundational Depth, Ethics and Social Impact, and Domain of Application categories described below.
Foundational Depth:
This is satisfied by an elective in either the Mathematics and Statistics Department or the Computer Science Department at the 300-level or above. Suggested courses are:
MATH-352 Statistical Theory and Computation
MATH-356 Statistical Methods in Machine Learning
MATH-509 Senior Seminar in Applied Probability
CPSCI-330 Algorithms
CPSCI-350 Database Theory and Practice
CPSCI-375 Artificial Intelligence
CPSCI-380 Theory of Computation
Ethics and Social Impact:
This elective should feature an examination of ethical and social concerns that can arise in the collection of data and application of algorithms. Pre-approved courses are:
ANTHR-259 Digital Technology and Social Transformations
ENVST-290 Nature and Technology
GOVT-412 The Politics of AI: Algorithms, "Big Data," and "Humans in the Loop"
MATH-260 Decisions by Design: Applied Mathematics in Society
PHIL-222 Race, Gender and Culture
SOC-286 Sociology of Science
Domain of Application:
This elective should demonstrate how the collection, visualization and interpretation of data have been integrated to advance knowledge within a specific domain. Pre-approved courses are:
BIO-212 Introduction to Bioinformatics
BIO-214 Health Care Systems
ECON-266 Introduction to Econometrics
DARTS-203 Performance, Ritual and Technology
ENVST-222 Environmental Spatial Analysis
ENVST-325 Environmental Data Science
Students may petition the program committee to accept a course other than those on the pre-approved list to satisfy the ethics and social impact requirement and/or the domain of application requirement by providing a written rationale indicating how the course meets the goal of the elective category.
SSIH Requirement
The Social, Structural and Institutional Hierarchies requirement can be fulfilled by taking MATH-260 or by taking MATH-254 and opting to do a data analysis project focused on SSIH issues. Students may also take SSIH courses offered by other departments that are pre-approved by the Math department.
Credit/No Credit Policy
Only MATH 116 can be taken on a CR/NC basis to be applied towards the concentration. For students starting at Hamilton prior to Fall 2026 who take MATH-216 instead of DATSC-201, MATH-216 may be taken on a CR/NC basis to be applied to the concentration.
Honors Policy
Students may earn honors by completing courses that satisfy the concentration with an average of 3.6 or higher, by taking a fourth full-credit elective that is at the 300 level or higher, and by making a public presentation to the department on a Data Science topic (outside of class work) during their senior year.