Scientific Computing Concentration

Haverford’s concentration in scientific computing offers students the opportunity to explore the computational dimensions of the natural and social sciences.

Our program is committed to providing students with a solid foundation in the tools and concepts that drive the field as a whole, while enabling them to explore the computational aspects particular to their own major disciplines. Students pursuing the concentration typically use it to enhance their majors in chemistry, physics/astronomy, math, biology, computer science, or economics.

One of just a handful of similar programs offered at undergraduate institutions nationally, we are especially well-known for our emphasis on project-based experiences. Our students graduate equipped with the knowledge as well as the real-world experience that will enable them to succeed in graduate school and the job market.

Curriculum & Courses

Concentrators pursue a course of study that emphasizes general computing skills and the application of those skills to the specific scientific discipline in which they are majoring.

Students must take two classes that introduce them to computer science and programming broadly and one that familiarizes them with the use of computation within the discipline they are pursuing. They must also complete three computation-related courses from a list of discipline-specific electives.

  • Concentration

    The concentration consists of six credits that fall into four categories of requirements, denoted (A), (B), (C), (D). These are merely categorical labels, and we have no intention of expressing a time-ordered sequence. In fact, we anticipate that many students in fields other than computer science will take at least one course in the (B) and/or (C) requirements before discovering an interest in the concentration, and then take courses to satisfy the other requirements afterward.

    The six courses should be selected from the following list and approved by the student’s concentration advisor. Of the six credits required for the concentration, no more than two of the courses in (B) or (C) may count towards both the concentration and the student’s major. (Also, per College rules, students may not count among the 32 course credits required for graduation any course that substantially repeats the content of another course already completed, even though the course numbers may suggest an advancing sequence. For example, both introductory computer science courses, CMSC 105 and CMSC 110, cannot be taken for credit.)

    Categories of Requirements

    Category A: Year-long introduction to computer science and programming, that may consist of (CMSC 105 and CMSC 106) or (CMSC B110 and CMSC B206) or (CMSC 107).

    Category B: One course involving regular programming assignments and becoming familiar with discipline-specific programming idioms, chosen from the following list:

    • ASTR 341: Advanced Topics in Astrophysics: Observational Astronomy
    • ASTR 342: Advanced Topics in Astrophysics: Modern Galactic Astronomy
    • ASTR 344: Advanced Topics in Astrophysics: Computational Astrophysics
    • CMSC 187: Scientific Computing-Discrete Problems
    • CMSC 207: Data Science and Visualization
    • CMSC 250: Computational Models in the Sciences
    • CMSC 287: High Performance Scientific Computing
    • CMSC/LING 325:  Computational Linguistics
    • CHEM 304: Statistical Thermodynamics and Kinetics
    • CHEM 305: Quantum Chemistry
    • MATH 222: Scientific Computing-Continuous Problems
    • PHYS 304: Computational Physics

    Category C: Three credits worth of electives in which real-world phenomena are investigated using computation, at a significant level as determined by the standards of that discipline. At least one of these three credits must come from a 300-level course or courses (not senior research). A normative route in the sciences would be for a student to take two taught courses on this list and apply one credit of senior research to this requirement. Alternatively, students whose senior work is not computational but who still wish to pursue the concentration can complete three taught courses from this list. These courses should be drawn from the following list:

    • Any of the courses on the (B) list above
    • BIOL 300: Superlab
    • BIOL 301: Advanced Genetic Analysis (1/2 credit)
    • BIOL 354: Computational Genomics (1/2 credit)
    • BIOL 357: Protein Design (1/2 credit)
    • CHEM 322: Advanced Physical Chemistry: Mathematical Modeling & Natural Processes
    • CMSC 120: Visualizing Information
    • CMSC 225: Fundamentals of Databases
    • CMSC 235: Information and Coding Theory
    • CMSC 250: Computational Models in the Sciences
    • CMSC/LING 325:  Computational Linguistics
    • ECON 032: Operations Research
    • MATH 204/210: Differential Equations, in years in which it includes significant computer lab exercises involving modeling and/or simulation
    • MATH 210: Linear Optimization and Game Theory
    • MATH 286: Applied Multivariate Statistical Analysis
    • MATH 394: Advanced Topics in Computer Science and Discrete Math
    • MATH 397: Advanced Topics in Applied Math
    • MATH 056: Modeling
    • PHYS 306: Mathematical Methods in the Physical Sciences
    • PHYS 316: Electronic Instrumentation and Computers
    • PHYS 026: Chaos, Fractals, Complexity, Self-Organization, and Emergence
    • Up to 1 credit of senior research (e.g., ASTR 404, BIOL 40x, CHEM 361, CMSC 480, MATH 399, PHYS 41x), if the project has a significant focus on scientific computing

    Category D: Some part of completion of the concentration must include a project-based experience in which computation is applied to investigate a real-world phenomenon, e.g.,

    • A senior thesis/experience with significant scientific computing component, or
    • A summer research experience, or
    • A multi-week project for a course that may (or may not) be one of the three electives that fulfill requirement (C)

Associated Programs and Concentrations

Research & Outreach

The culmination of the concentration is our project-based experience, which can take the form of a senior thesis, a summer research experience, or a project completed in connection with a relevant course. These projects are enhanced by the close involvement of our faculty and enable concentrators to develop and execute sophisticated and highly innovative computational investigations within their area of study.

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