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.
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 H105 and CMSC B109, cannot be taken for credit.)
Categories of Requirements
Year-long introduction to computer science and programming, that may consist of (CMSC H105 and CMSC H106) or (CMSC B109 and CMSC B113) or (CMSC H107). CMSC H107 can be taken upon successful completion of either: CMSC H104 or CMSC B115 or the CS placement exam."
One course involving regular programming assignments and becoming familiar with discipline-specific programming idioms, chosen from the following list:
Course List Code Title Credits ASTR H204 Introduction to Astrophysics 1.00 ASTR H341 Advanced Topics: Observational Astronomy 1.0 ASTR H344 1.0 CMSC H207 Data Science and Visualization 1.0 BIOL H311 Advanced Genetic Analysis 0.5 CMSC H287 High Performance Scientific Computing 1.0 CMSC H208 Speech Synthesis and Recognition 1.0 CMSC/LING H325 Computational Linguistics 1.0 CHEM H304 Statistical Thermodynamics and Kinetics 1.0 CHEM H305 Quantum Chemistry 1.0 MATH H222 Scientific Computing: Continuous Systems 1.0 MATH S066 Stochastic and Numerical Methods 1.0 PHYS/ASTR H304 Computational Physics 1.0 CHEM H352 Topics in Biophysical Chemistry: Macromolecular Crystallography 0.50 MATH B325 Advanced topics in Applied Mathematics
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:
Course List Code Title Credits Any of the courses on the (B) list above BIOL H357 (Topics in Protein Science) 0.5 BIOL H301 Advanced Lab in Biology Sem 2 (Bioinformatics Superlab) 1.00 CHEM B322 Advanced Physical Chemistry: Mathematical Modeling & Natural Processes 1.0 CMSC H235 Information and Coding Theory 1.0 CMSC/LING H325 Computational Linguistics 1.0 ECON H324 Advanced Econometrics 1.0 MATH H204/B210 Differential Equations 1.0 MATH H210 Linear Optimization 1.0 MATH H361 Applied Multivariate Statistical Analysis 1.00 MATH H394 Advanced Topics in Computer Science and Discrete Math 1.0 MATH H397 Advanced Topics in Applied Mathematics: Models for Dynamic Processes 1.0 MATH H382/B308 Mathematical Modeling and Differential Equations 1.00 MATH S056 Modeling 1.0 Up to 1 credit of senior research if the project has a significant focus on scientific computing 1 1.0 MATH H396 Advanced Topics: Probability and Statistics 1.00 STAT H203 Statistical Methods and their Applications 1.00
e.g., ASTR H404, BIOL H40x, CHEM H361, CMSC H480, MATH H399, PHYS H41x
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.
The physics major used Oort constants to study the Milky Way for her thesis.
The mathematics major combined her interest in statistics and political economy to examine models of Chinese currency’s exchange rates.
The astrophysics major and scientific computing minor, studied quantitative morphology measures—the means of quantitatively measuring the visual appearances of galaxies—testing how effectively they could detect galaxy mergers, instances of two galaxies coming together to form a larger one.
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