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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

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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

    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:

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    Scott Alfeld

    Computer Science
    Visit Prof. Alfeld's Page

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    Brittney Bailey

    Statistics
    Visit Prof. Bailey's Page

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    Katharine Correia

    Statistics
    Visit Prof. Correia's Page

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    Kevin Donges

    Statistics
    Visit Prof. Donges' Page

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    Nicholas Horton

    Statistics
    Visit Prof. Horton's Page

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    Tanya Leise

    Mathematics
    Visit Prof. Leise's Page

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    Shu-Min Liao

    Statistics
    Visit Prof. Liao's Page

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    Matteo Riondato

    Computer Science
    Visit Prof. Riondato's Page

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    Lee Spector

    Computer Science
    Visit Prof. Spector's Page

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    Amy Wagaman

    Statistics
    Visit Prof. Wagaman's Page

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    Kate Follette

    Physics & Astronomy
    Visit Prof. Follette's Page

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    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

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    Matthew Schulkind

    Psychology
    Visit Prof. Schulkind's Page