The Certificate in Scientific Computation & Data Sciences is available to all undergraduates interested in the use of mathematical, statistical and computer-based techniques to investigate complex systems. Students must complete 18 semester hours of courses including an independent research project.

### Overview

Students must complete 18 semester hours of courses as follows in "Course Requirements."

Computation is transforming the process of scientific discovery as an increasing number of scientific endeavors utilize significant computational resources. Predictions and hypothesis from traditional hypothesis driven science can now be assessed and substantiated with computation. These computational resources are also the major driving force behind the newer and complementary data-driven science.

This certificate program provides undergraduates with an opportunity to enrich their field of study, albeit biology, chemistry, physics, engineering, economics, or medicine with applied computation, ranging from the development of and analysis with algorithm, database, statistical or visualization code. This program is fulfilled with a series of computational classes, and culminated with an individual project that utilizes computation to address a scientific inquiry.

### How to Apply

Please return all applications to GDC 7.408, Campus Mail Code: D9800.

### Course Requirements

Track your progress with the Course Progression Worksheet

Click HERE for courses being offered in Fall 2017.

#### PRE-REQUISITE KNOWLEDGE

#### CORE REQUIREMENTS

Take one course in **computer programming** and one course in either **Linear Algebra**, **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

The culminating requirement for the Certificate in Scientific Computation is that you conduct original research and write a research paper on some aspect of scientific computation. The paper might present original work or discuss a new technique. This original project should clearly apply the tools and techniques of computation to a scientific discipline in the pursuit of new knowledge.

**RESEARCH PAPER PROCESS & REQUIREMENTS****RESEARCH CONTRACT**(must be completed and signed in order to register for SDS 379R)**RESEARCH COURSE COMPLETION CHART****COVER PAGE TEMPLATE**

### 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 SDS office in GDC 7.408, D9800. The form is available for download HERE. Students are encouraged to apply early in their course of study. The SDS advisor 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

#### PRE-REQUISITE KNOWLEDGE

M 408D: Differential and Integral Calculus

M 408M: Multivariable Calculus

#### CORE COMPUTING COURSE

ASE 201: Introduction to Computer Programming

BME 303: Introduction to Computing

CS 313E: Elements of Software Design

EE 312: Introduction to Programming

GEO 325J: Programming in FORTRAN and MATLAB (for GEO majors only)

SDS 222/322: Introduction to Scientific Programming

#### CORE MATH COURSE

SDS 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 427J: Differential Equations with Linear Algebra

NUMERICAL METHODS ELECTIVES

ASE 311: Engineering Computation

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

SDS 335: Introduction to Scientific/Technical Computing

#### STATISTICAL METHODS ELECTIVE

BME 335: Engineering Probability and Statistics

ECO 329: Economic Statistics

EE 351K: Probability and Random Processes

M 358K: Applied Statistics

M 378K: Introduction to Mathematical Statistics

ME 335: Engineering Statistics

SDS 325H: Honors Statistics

SDS 328M: Biostatistics

Another statistics course with consent of faculty advisor

OTHER COMPUTING TOPICS ELECTIVES

BIO 348: Computational Biology and Bioinformatics

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

MIS 325: Database Management

NEU 366M: Quantitative Methods

SDS 329D: Practical Linear Algebra II

SDS 374C: Parallel Computing

SDS 374D: Distributed and Grid Computing for Scientists and Engineers

SDS 374E: Visualization and Data Analysis

APPLIED COMPUTING COURSE

ASE 347: Introduction to Computational Fluid Dynamics

BIO 321G: Computational Biology

BIO 337J: Computational Biology 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*

CS 378: Introduction to Data Mining

ECO 363C: Computational Economics

EE 361/379K: Introduction to Data Mining

FIN 372/STA 372.6: Optimization Methods in Finance

GEO 325K: Computational Methods in Geological Sciences

M 375T: Topics in Mathematics*

M 374M: Mathematical Modeling in Science and Engineering

PHY 329: Introduction to Computational Physics

*Topics courses subject to approval based on course specific topics

Contact

For additional information about the Certificate in Scientific Computation & Data Sciences program and application process, email Vicki Keller at vicki.keller[@]cns[dot]utexas[dot]edu.