Overview
Students must complete 18 semester hours of courses as follows in "Course Requirements".
Scientific computation is the use of mathematical, statistical and computer-based techniques to investigate complex systems. In a world where computation is fueling innovation and success, a certificate in scientific computation will make you more competitive for jobs and top-tier graduate schools in your field.
Scientific computation has applications in science, engineering, economics, medicine, sociology and many other disciplines. For example, scientific computation has helped us to understand the causes and effects of climate change, financial catastrophes, and the motions of stars, to optimize the performance of mechanical systems, to model the human brain, to control the spread of disease, and to develop more effective medicines.
How to Apply
Click HERE to download the application. Please return all applications to WCH 2.104, Campus Mail Code: G2500.
Course Requirements
(Click on bolded topics to go to a list of currently approved courses).
PRE-REQUISITE KNOWLEDGE
Multivariate Calculus
CORE REQUIREMENTS
Take one course in computer programming and one course in either Linear Algebra, or Discrete Mathematics, or Differential Equations.
SCIENTIFIC COMPUTING COURSES
Choose two of the following categories and take one course in each: Numerical Methods, Statistical Methods, Other Computing Topics.
APPLIED COMPUTING COURSES
Select one computing course in an applied area of your choosing.
RESEARCH PROJECT
Conduct independent research advised by a member of the SSC Scientific Computing faculty. A final research report must be submitted upon completion of the course. Click HERE for more details on the report.
Frequently Asked Questions
Q: What are the requirements?
A: You will complete 18 semester hours, including a research project, as specified in the coursework guidelines. You must earn a letter grade of C- or better in all courses required for certification.
Q: How do I sign up?
A: Submit an application form to the SSC office in WCH 2.104, G2500. The form is available for download HERE . Students are encouraged to apply early in their course of study. The SSC will help each student choose an appropriate course sequence and develop his or her independent study project.
Q: Can Certificate courses also fulfill my degree requirements?
A: Some courses that are required by the certificate will also fulfill degree requirements established by a student's major department.
Q: Will the certificate appear on my transcript?
A: Yes. Your official UT transcript will state that you completed the Undergraduate Certificate Program in Scientific Computation.
Approved Courses
M 408D: Differential and Integral Calculus
M 408M: Multivariable Calculus
EE 312: Introduction to Programming
SSC 222: Introduction to Scientific Programming
Equivalent course with consent of faculty advisor
SSC 329C: Practical Linear Algebra I
M 340L: Matrices and Matrix Calculations
M 341: Linear Algebra and Matrix Theory
M 362M: Introduction to Stochastic Processes
M 372K: PDE and Applications
CE 379K: Computer Methods for Civil Engineering
CHE 348: Numerical Methods in Chemical Engineering
CS 323E: Elements of Scientific Computing
CS 323H: Scientific Computing - Honors
CS 367: Numerical Methods
M 348: Scientific Computation in Numerical Analysis
SSC 335: Introduction to Scientific/Technical Computing
EE 351K: Probability and Random Processes
M 358K: Applied Statistics
M 378K: Introduction to Mathematical Statistics
BME 335: Engineering, Probability, and Random Processes
Another statistics course with consent of faculty advisor
CS 324E: Elements of Graphics and Visualization
CS 327E: Elements of Databases
CS 329E: Topics in Elements of Computing*
CS 377: Principles and Applications of Parallel Programming
M 346: Applied Linear Algebra
M 362M: Introduction to Stochastic Processes
M 368K: Numerical Methods for Applications
M 372K: PDE and Applications
M 376C: Methods of Applied Mathematics
ME 367S: Simulation Modeling
SSC 329D: Practical Linear Algebra II
SSC 374C: Parallel Computing
SSC 374D: Distributed and Grid Computing for Scientists and Engineers
SSC 374E: Visualization and Data Analysis
ASE 347: Introduction to Computational Fluid Dynamics
BIO 321G: Computational Biology
BME 341: Engineering Tools for Computational Genomics Lab
BME 342: Computational Biomechanics
BME 346: Computational Structural Biology
BME 377T: Topics in Biomedical Engineering
CH 368: Advanced Topics in Chemistry*
CS 329E: Topics in Elements of Computing*
ECO 363C: Computational Economics
EE 379K: Introduction to Data Mining
GEO 325K: Computational Methods in Geological Sciences
M 375T: Topics in Mathematics*
M 474M: Mathematical Modeling in Science and Engineering
PHY 329: Introduction to Computational Physics
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