A young woman working at a computer We live in a world awash with data. The rise of connected devices, sophisticated sensor networks, social media, and interconnected databases has led to an unprecedented flood of information. Making sense of the data that surround us is inherently a liberal art.

Large and complex datasets can be used to address societal challenges (e.g., climate change, energy and transportation, health, inclusion and systematic racism and inequality). Potential downsides exist as well in terms of loss of privacy, algorithmic bias, and broader ethical concerns.

To best meet these challenges, we need an integrated humanistic and scientific approach to understanding our data-infused world. Data science-related majors at Amherst include computer science and statistics, though many other majors facilitate the application of data science, including (but not limited to) anthropology, astronomy, biology, chemistry, economics, English, mathematics, neuroscience, physics, political science, psychology, and sociology.

Making sense of the data that surround us is inherently a liberal art.
—Nicholas Horton

Our Courses

Three photos of a computer classroom, a woman looking through a telescope and a computer science classroom Courses at all levels in data science, broadly defined, are available across the curriculum, including the following disciplines and courses. Introductory level courses below may satisfy prerequisite requirements for some of these courses, and provide some glimpses into data science.

Be sure you check prerequisites for courses you are interested in, as some may have higher-level requirements!

Astronomy

Mathematics

Political Science

Computer Science

Statistics

Introductory Courses


Data Science In the News

A professor sitting down looking at a student drawing a diagram on a whiteboard

Computer Science For… Science

September 8, 2020

Read about how Assistant Professor of Computer Science Matteo Riondato uses data science to figure out how to extract the most accurate information from enormous data sets.

Read the Article

Want more information?

Reach Out to Our Faculty

Students interested in data science are advised to consult with the following faculty:

A photo of Scott Alfeld

Scott Alfeld

Computer Science
Visit Prof. Alfeld's Page

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Brittney Bailey

Statistics
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A photo of Katharine Correia

Katharine Correia

Statistics
Visit Prof. Correia's Page

A photo of Kevin Donges

Kevin Donges

Statistics
Visit Prof. Donges' Page

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Nicholas Horton

Statistics
Visit Prof. Horton's Page

A photo of Tanya Leise

Tanya Leise

Mathematics
Visit Prof. Leise's Page

A photo of Shu-Min Liao

Shu-Min Liao

Statistics
Visit Prof. Liao's Page

A photo of Matteo Riondato

Matteo Riondato

Computer Science
Visit Prof. Riondato's Page

A photo of Lee Spector

Lee Spector

Computer Science
Visit Prof. Spector's Page

A photo of Amy Wagaman

Amy Wagaman

Statistics
Visit Prof. Wagaman's Page

A photo of Karamatou Yacoubou Djima

Karamatou Yacoubou Djima

Mathematics
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A photo of Kate Follette

Kate Follette

Physics & Astronomy
Visit Prof. Follette's Page

A photo of Nicholas Holschuh

Nick Holschuh

Geology
Visit Prof. Holschuh's Page

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Josef Trapani

Biology and Neuroscience
Visit Prof. Trapani's Page

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Eleonora Mattiacci

Political Science
Visit Prof. Mattiacci's Page

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Kerry Ratigan

Political Science
Visit Prof. Ratigan's Page

A photo of Matthew Schulkind

Matthew Schulkind

Psychology
Visit Prof. Schulkind's Page