This page is the effort of an individual Williams College faculty member, and is not otherwise endorsed by Williams College.
While Williams College does not have any official course of study in data science, there are nonetheless enough curricular opportunities spread across campus that a determined student could put together an unofficial course of study for themself. My recommendations are below. I am very happy to provide further guidance. Just contact me at c m t 6 @ w i l l i a m s . e d u.
Unofficial “Concentration” in Applied Data Science
Take one or, preferably, both.
- CSCI 104: Understanding Data and Computing
- CSCI 134: Intro to Computer Science
Take two. Make sure to pay attention to course prerequisites.
- STAT 101: Elementary Statistics and Data Analysis
- STAT 161: Introductory Statistics for Social Science
- STAT 201: Statistics and Data Analysis
- STAT 202: Introduction to Statistical Modeling
- ECON 255: Econometrics
- PSYC 201: Experimentation and Statistics
Take this short, free online course.
Area of Application
Take two courses that are focused around a common theme. These courses should either explicitly involve working with data, or they should be in an area that allows the possibility of data-related activities in your future career/education. I am very happy to brainstorm appropriate applications and courses with you. A few sample thematic areas might include:
- Observational Astronomy
- Data and Society
- Quantitative Ecology
- Quantitative Economics
- Environmental Science and Policy
- Geographic Analytics
- Political Analytics
- Social Justice
Unofficial “Major” in Data Science Methodologies
Complete the requirements of the Unofficial “Concentration” in Data Science (above) except for the Area of Application.
- MATH 250: Linear Algebra
Upper Division Electives
Take at least three. Make sure to pay attention to course prerequisities.
- CSCI 373: Artificial Intelligence
- CSCI 374: Machine Learning
- CSCI 375: Natural Language Processing
- ECON 371: Time Series Econometrics
- MATH 307: Computational Linear Algebra
- MATH 308: Mathematical and Computational Approaches to Social Justice
- MATH/STAT 341: Probability
- STAT 319: Statistical Computing
- STAT 346: Regression Theory
- STAT 356: Time Series Analysis
- STAT 365: Bayesian Inference
- STAT 442: Statistical Learning and Data Mining