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Scientific Computing Minor

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About the Minor

Computation is now an integral part of modern science and engineering. In science, computer simulation allows the study of natural phenomena impossible or intractable through experimental means. Computer simulation allows the analysis and synthesis of systems too expensive, dangerous, or complex to model and build directly. Astronomers studying the formation of massive black holes, neuroscientists studying neural networks for human memory, mechanical engineers studying the designs of turbines and compressors, and electrical engineers studying the reliability of electronics aboard spacecraft are united both in the computational challenges they face and the tools and techniques they use to solve these challenges.

Students in the program in Scientific Computing are taught techniques for understanding complex physical, biological, and social systems. Students are introduced to computational methods for simulating and analyzing models of complex systems, to scientific visualization and data mining techniques needed to detect structure in massively large multidimensional data sets, to high performance computing techniques for simulating models on computing clusters with hundreds or thousands of parallel, independent processors and for analyzing terabytes or more of data that may be distributed across a massive cloud or grid storage environment.

Minor Requirements (15 Hours)

  • A) CS 1104 or CS 1101 - 3 hours

    CS 1104 is strongly recommended

  • B) CS 2204 or CS 2201 - 3 hours

    CS 2204 - strongly recommended

  • C) One of the following courses in mathematical, quantitative, and data science methods -3 hours 
    • SC 3250 - Scientific Computing Toolbox 
    • ANTH 3261 - Introduction to Geographic Information Systems and Remote Sensing
    • BMIF 6310 - Foundations of Bioinformatics
    • BMIF 7380 - Data Privacy in Biomedicine 
    • BSCI 3272 - Genome Science 
    • BME 3200 - Analysis of Biomedical Data 
    • CE 4320 - Data Analytics for Engineers 
    • CS/DS 3262 - Applied Machine Learning 
    • ECON 3032 - Applied Econometrics 
    • ECON 3035 - Econometrics Methods 
    • ECON 3750 - Econometrics for Big Data 
    • EECE 6358 - Quantitative Medical Image Analysis 
    • HOD 3200 - Introduction to Data Science 
    • MATH 3620 - Introduction to Numerical Mathematics 
    • MATH 3670 - Mathematical Data Science 
    • MATH 4600 - Numerical Analysis 
    • MATH 4620 - Linear Optimization 
    • MATH 4630 - Nonlinear Optimization 
  • D) One of the following courses in computational, simulation, and modeling methods- 3 hours
    • SC 3250 - Scientific Computing Toolbox 
    • SC 3260 - High Performance Computing 
    • ASTR 3600 - Stellar Astrophysics 
    • ASTR 3700 - Galactic Astrophysics 
    • ASTR 3800 - Structure Formation in the Universe 
    • BME 4310 - Modeling Living Systems for Therapeutic Bioengineering 
    • BME 7310 - Advanced Computational Modeling and Analysis in Biomedical Engineering Quantitative 
    • BME 7410 - Methods in Biomedical Engineering 
    • CHBE 4830 - Molecular Simulation
    • CHEM 5410 - Molecular Modeling Methods  
    • CHEM 5420 - Computational Structural Biochemistry 
    • CS 3274 - Modeling and Simulation 
    • EES 4760 - Agent and Individual Based Computational Modeling 
    • MATH 3630 - Mathematical Modeling in Biology 
    • MATH 3660 - Mathematical Modeling in Economics 
    • ME 4263 - Computational Fluid Dynamics and Multi physics Modeling
    • ME 4275 - Finite Element Analysis 
    • NSC 3270 - Computational Neuroscience 
    • PHYS 3200 - Statistical Physics 
    • PHYS 3790 - Computational Physics 
    • PSY 4218 - Computational Cognitive Modeling 
    • PSY 4219 - Scientific Computing for Psychological and Brain Sciences 
    • PSY 4775 - Models of Human Memory 
  • E) One additional course from either list C or D - 3 hours
    • SC 3250 - Scientific Computing Toolbox 
    • ANTH 3261 - Introduction to Geographic Information Systems and Remote Sensing
    • BMIF 6310 - Foundations of Bioinformatics
    • BMIF 7380 - Data Privacy in Biomedicine 
    • BSCI 3272 - Genome Science 
    • BME 3200 - Analysis of Biomedical Data 
    • CE 4320 - Data Analytics for Engineers 
    • CS/DS 3262 - Applied Machine Learning 
    • ECON 3032 - Applied Econometrics 
    • ECON 3035 - Econometrics Methods 
    • ECON 3750 - Econometrics for Big Data 
    • EECE 6358 - Quantitative Medical Image Analysis 
    • HOD 3200 - Introduction to Data Science 
    • MATH 3620 - Introduction to Numerical Mathematics 
    • MATH 3670 - Mathematical Data Science 
    • MATH 4600 - Numerical Analysis 
    • MATH 4620 - Linear Optimization 
    • MATH 4630 - Nonlinear Optimization 
    • SC 3250 - Scientific Computing Toolbox 
    • SC 3260 - High Performance Computing 
    • ASTR 3600 - Stellar Astrophysics 
    • ASTR 3700 - Galactic Astrophysics 
    • ASTR 3800 - Structure Formation in the Universe 
    • BME 4310 - Modeling Living Systems for Therapeutic Bioengineering 
    • BME 7310 - Advanced Computational Modeling and Analysis in Biomedical Engineering Quantitative 
    • BME 7410 - Methods in Biomedical Engineering 
    • CHBE 4830 - Molecular Simulation
    • CHEM 5410 - Molecular Modeling Methods  
    • CHEM 5420 - Computational Structural Biochemistry 
    • CS 3274 - Modeling and Simulation 
    • EES 4760 - Agent and Individual Based Computational Modeling 
    • MATH 3630 - Mathematical Modeling in Biology 
    • MATH 3660 - Mathematical Modeling in Economics 
    • ME 4263 - Computational Fluid Dynamics and Multi physics Modeling
    • ME 4275 - Finite Element Analysis 
    • NSC 3270 - Computational Neuroscience 
    • PHYS 3200 - Statistical Physics 
    • PHYS 3790 - Computational Physics 
    • PSY 4218 - Computational Cognitive Modeling 
    • PSY 4219 - Scientific Computing for Psychological and Brain Sciences 
    • PSY 4775 - Models of Human Memory 
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Frequently Asked Questions

  • How do the minors in Scientific Computing and Computer Science prepare me differently?

    Scientific Computing focuses on the application of computation in science and engineering, as well as many of the social sciences and humanities. It concerns itself with how methods and technology of computer science are applied to modern and cutting-edge problems within these core disciplines. The computer science minor focuses on the fundamentals of the discipline of computer science. Thus, it concerns itself with the general properties of computation, the design and development of computation to realize practical goals, and the structure and organization of computing devices to achieve efficient computation.

  • How do the minors in Scientific Computing and Data Science prepare me differently?

    There is overlap between Scientific Computing and Data Science (as fields and as minors). Like Scientific Computing, Data Science involves computation. Data Science includes statistics, machine learning, visualization, and ethics. 

  • How do I declare a minor in Scientific Computing?

    To add the minor in scientific computing, get an add minor form (or copies of the forms) from the registrar of your College or School and have them signed by one of the Directors of the scientific computing minor.

  • Should I take CS2201 or CS2204?

    CS2204 is a course designed specifically for the Scientific Computing minor, and its content is better suited for people interested in scientific computing, in terms of content, level, and application. CS2201 is an acceptable alternative if students find it impossible to enroll in CS2204 because of scheduling or if students are planning to minor in both Scientific Computing and Computer Science; CS2201 is required for the Computer Science minor without substitute.

  • CS2204 is not being offered this year. Can I take CS2201?

    Yes. Course staffing issues and scheduling may make it impossible to offer CS2204 every semester. CS2201 is an acceptable alternative.

  • How do I get involved in a research project?

    See http://www.vanderbilt.edu/scientific_computing/researchopportunities.php for more information. Students interested in participating in research should try to talk with potential faculty members well in advance. Please note that any particular faculty member may be unable to supervise an undergraduate research project for a variety of reasons (e.g., the student does not have the required background, the faculty member is going on academic leave, or the laboratory is over capacity). Talking with several potential faculty mentors is recommended.

  • Can my honors project in another discipline count also toward my Scientific Computing minor?

    In principle, yes. A core component of the project should combine scientific computing tools and techniques with a substantive scientific or engineering problem. Students (and their faculty sponsors) should talk with one of the Directors of the Scientific Computing minor if there is any uncertainty about what might or might not qualify for a suitable project within the minor.