Requirements: Program in Computing


The Program in Computing concentration is an interdisciplinary program in the application of computers to scientific inquiry. A longer title for the program might be "Computing within a Scientific Context."

The concentration focuses on four major areas:  

  1. Computer program development, including the construction and implementation of data structures and algorithms
  2. Mathematical modeling of natural phenomena (including cognitive processes) using quantitative or symbolic computer techniques
  3. Analysis and visualization of complex data sets, functions and other relationships using the computer
  4. Computer hardware issues, including the integration of computers with other laboratory apparatus for data acquisition

The overall aim is to prepare the student to use computers in a variety of ways for scientific exploration and discovery.

The Kenyon College faculty voted to change from Kenyon units to semester hours. This change will go into effect for all students who start at the College in the fall of 2024. Both systems will be used throughout the course catalog with the Kenyon units being listed first.

The Curriculum

The program in computing requires a total of six courses of Kenyon coursework. COMP 118 (Introduction to Programming) serves as a foundation course for the program, introducing students to programming and other essential ideas of computer science.

Contributory courses have been identified in the arts and humanities, biology, chemistry, economics, environmental studies, mathematics, political science, physics and statistics. In these courses, computational methods form an essential means for attacking problems of various kinds.

Students in the concentration also take at least one intermediate program in computing course. The main focus of these courses is computational methods, which are developed or investigated extensively.

In addition to regular courses that are identified as contributory or intermediate, particular special-topics courses or individual studies in various departments may qualify in one of these two categories. Students who wish to credit such a course toward the concentration in program in computing should contact the program director at the earliest possible date.

The capstone course of the program is COMP 401 (Advanced Scientific Computing Seminar), a project-oriented, seminar-style course for advanced students.

Requirements for the Concentration

Required Courses 

COMP 118: Introduction to Programming or PHYS 270: Introduction to Computational Physics
COMP 401: Scientific Computing Seminar

Contributory Courses 

ARTS 191: Creative Coding
BIOL 109Y–110Y: Introduction to Experimental Biology
BIOL 328: Global Ecology and Biogeography
CHEM 126: Introductory Chemistry Laboratory II
CHEM 336: Quantum Chemistry
CHEM 341: Instrumental Analysis
CHEM 370: Advanced Lab: Computational Chemistry
CHEM 374: Advanced Lab: Spectroscopy
ECON 205: Introduction to Econometrics
ECON 337: Portfolio Allocation and Asset Pricing
ECON 375: Advanced Econometrics
ENVS 261: Geographic Information Science
IPHS 200: Programming Humanity
IPHS 300: AI for the Humanities
PHYS 140: Classical Physics
PHYS 141: First-Year Seminar in Physics
PHYS 146: Introduction to Experimental Physics
PHYS 240, 241: Fields and Spacetime and Laboratory
PHYS 345: Astrophysics and Particles
PHYS 380: Introduction to Electronics
PHYS 381, 382: Projects in Electronics 1, 2
PHYS 385, 386, 387: Advanced Experimental Physics 1, 2, 3
PSCI 280: Political Analysis
PSYC 410: Research Methods in Human Neuroscience
STAT 106: Elements of Statistics
STAT 116: Statistics in Sports
STAT 206: Data Analysis
STAT 216: Nonparametric Statistics

Intermediate Courses

BIOL 230: Computational Genomics
COMP 218: Data Structures and Program Design
COMP 318: Software Development
COMP 348: Software System Design
COMP 493: Individual Study
MATH 258: Mathematical Biology
MATH 291: Special Topic: Computational Neuroscience (spring 2021)
MATH 328: Coding Theory and Cryptography
MATH 347: Mathematical Models
MATH 368: Design and Analysis of Algorithms
STAT 226: Statistical Computing with R
STAT 416: Linear Regression Models