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Computational Science Sequence

A recommendation for students with strong math aptitudes majoring in the natural sciences

Computational Science is a discipline concerned with the design, implementation, and use of mathematical models to analyze and solve scientific problems. Computational Science involves the development and application of computational models and simulations, often coupled with high-performance computing, to solve complex physical problems arising in natural phenomena. It has been described as the "third mode of discovery" (next to theory and experimentation) and is regularly used by professors in the natural sciences.

The Department of Computer Science encourages undergraduates in the natural sciences who have strong math aptitudes to consider taking the following computational science sequence of courses to enhance their degree:

  • Tec 201 Digital Literacy with Programming introduces students to productivity tools, markdown (a common technical documentation tool), Juptyer notebooks (a popular computation, graphing, and documentation tool), and interactive programming.

  • CpS 110 Object-Oriented Programming I provides an extensive introduction to programming in Python, a popular programming language used in to conduct research in the natural sciences. This is an entry-level programming class, requiring no programming background.

  • CpS 210 Data Structures builds on the foundation of CpS 110 by supplying students with the knowledge needed to write programs that run efficiently and produce research results quickly.

  • CpS 408 Parallel and Distributed Algorithms teaches students to write programs that leverage multi-core CPU’s and high-performance computing clusters to process the enormous data sets common in natural science research.

Computational science appears in various fields: genomic data analysis, drug discovery research, computational neuroscience, biotechnology, bioinformatics, molecular modeling, materials science, chemical process simulation, climate and weather modeling, aerospace simulation, astrophysics research, nuclear science, defense modeling and simulation, natural resource management, environmental data science, business intelligence, health data analysis, scientific programming, etc.

Students receive significant benefit from enrolling in even one or two of these courses. A fundamental knowledge of programming benefits everyone in analytical professions.

For more information, contact Dr. Sarah Gothard.