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Main Image for Computational Sciences at Bard

Computational Sciences at Bard

Photo by Pete Mauney '93 MFA '00
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The Computational Sciences program is an umbrella over computer science and data analytics, offering both a major and a second focus in computer science as well as a second focus in data analytics.

Computer Science focuses on fundamental ideas from the field, covering theoretical, applied, and systems-oriented topics. Most courses include hands-on projects so students can learn by building, and by participating in research projects in laboratories devoted to cognition, computational biology, robotics, and symbolic computation. Data Analytics prepares students to use data to address problems in both their major and in multidisciplinary settings, providing skills necessary to do data analysis, modeling and simulation, and data visualization, and understand how data are used to make decisions and predictions about the future. Courses also address Issues of algorithmic bias, data ethics, and the power exercised by those who control data and make decisions about its use.
The Computational Sciences Program at Bard

The Computational Sciences Program at Bard

The Computational Sciences Program at Bard offers three standard ways to enter the program:

  • CMSC 110-119 Introduction to Computing
  • CMSC 141 or 143 Object-Oriented Programming
  • CMSC 201 Data Structures
Students who would like more information about studying computer science at Bard are encouraged to speak with any of the computer science faculty. They can also read detailed descriptions of the degree requirements.

Program Requirements

Our Alumni/ae

Steven Wu '12
Steven Wu '12
The liberal arts college experience at Bard helped Steven discover his passion for computer science research. He received his BA in math and computer science from Bard in 2012, PhD in computer science from the University of Pennsylvania in 2017, and was a postdoctoral researcher at Microsoft Research in New York City. He is broadly interested in algorithm design, specifically in the areas of data privacy, fairness in machine learning, and algorithmic game theory. He joined the University of Minnesota–Twin Cities as an assistant professor in the Computer and Engineering Department in the fall of 2018.

Computational Sciences Resources

  • Computational Sciences Tutor Form
  • github
  • GitHub Tutorial
  • LaTeX
  • ACM
  • ACM Code of Ethics
  • CS Teaching Tips
  • FossWire Unix Cheat Sheet
  • Floss Manuals: Command Line
  • Linux Command: Learning the Shell
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