100 Level Courses

CDS 101: Introduction to Computational and Data Sciences. 3 credits.
Introduction to the use of computers in scientific discovery through simulations and data analysis. Covers historical development and current trends in the field. Offered by Computational & Data Sciences. May not be repeated for credit.
Recommended Prerequisite: Appropriate score on the math placement test.
Schedule Type: Lecture
CDS 102: Introduction to Computational and Data Sciences Lab. 1 credit.
Experiments in computational and data sciences explore the connections between on-going advances in the natural sciences and the rapid advances in computing and data handling. Lab exercises demonstrate the use of computers in analyzing data, in modeling science problems, and in creating numerical simulations across the science disciplines. Offered by Computational & Data Sciences. May not be repeated for credit.
Recommended Prerequisite: CDS 101. Concurrent enrollment is permitted.
Schedule Type: Laboratory
CDS 130: Computing for Scientists. 3 credits.
Covers use of computers to solve practical scientific problems. Topics include creating effective scientific presentations, analysis of experimental data, online literature, data/information ethics, scientific modeling, and communication/collaboration tools. Designed to equip students with the knowledge and confidence they need to use future hardware and software systems both as students and throughout their scientific careers. Offered by Computational & Data Sciences. May not be repeated for credit.
Recommended Prerequisite: Passing score on the math placement test for MATH 110 or MATH 113.
Schedule Type: Lecture
CDS 151: Data Ethics in an Information Society. 1 credit.
Examination of ethical issues related to access and use of information and data in the Internet age, for the general student, with special emphasis on ethical issues that apply to the proper use and interpretation of scientific and technical information. Offered by Computational & Data Sciences. May not be repeated for credit.
Schedule Type: Lecture

200 Level Courses

CDS 201: Introduction to Computational Social Science. 3 credits.
Undergraduate-level introduction to computational concepts, principles, and modeling approaches in social sciences, emphasizing simulations and elements of complexity theory as they apply to social phenomena. Survey includes systems dynamics, cellular automata, and agent-based models. Offered by Computational & Data Sciences. May not be repeated for credit.
Schedule Type: Lecture
CDS 205: Introduction to Agent-based Modeling and Simulation. 3 credits.
Undergraduate-level introduction to Agent-based Modeling. Provides a background onto why agent-based models and hands-on examination of agent-based models in the social sciences by examining and experimenting with a variety of social simulation projects. Offered by Computational & Data Sciences. May not be repeated for credit.
Schedule Type: Lecture
CDS 230: Modeling and Simulation I. 3 credits.
This course expands upon the foundation provided by CDS 130. Fundamental computational modeling techniques are used in a variety of disciplines with special emphasis on biological systems. Continued development of algorithmic thinking skills will be done using different computational environments. Offered by Computational & Data Sciences. May not be repeated for credit.
Recommended Prerequisite: CDS 130 or permission of instructor.
Schedule Type: Lecture
CDS 251: Introduction to Scientific Programming. 3 credits.
Focuses on elements of programming using the Fortran language and selected elements of the C language with emphasis on the aspects used in the computational and data sciences. Conducted through a combination of lecture and interactive computer laboratory. Offered by Computational & Data Sciences. May not be repeated for credit. Equivalent to CDS 351.
Recommended Prerequisite: CDS 130.
Schedule Type: Lecture
CDS 290: Topics in Computational and Data Sciences. 1-4 credits.
Selected topics in Computational and Data Sciences. May be accepted for credit by CDS majors and CDS minors. Offered by Computational & Data Sciences. May be repeated within the degree for a maximum 6 credits.
Schedule Type: Independent Study
CDS 292: Introduction to Social Network Analysis. 3 credits.
An introduction to methods and applications that examine social systems based on relations, structures, connectivity, location, roles, interactions, and other network properties. Example applications of Social Network Analysis covered will include politics, diseases, organizations, along with a variety of other social phenomena. Offered by Computational & Data Sciences. May not be repeated for credit.
Schedule Type: Lecture

300 Level Courses

CDS 301: Scientific Information and Data Visualization. 3 credits.
The techniques and software used to visualize scientific simulations, complex information, and data visualization for knowledge discovery. Includes examples and exercises to help students develop their understanding of the role visualization plays in computational science and provides a foundation for applications in their careers. Offered by Computational & Data Sciences. May not be repeated for credit.
Recommended Prerequisite: CDS 101 or CDS 130 or equivalent, or permission of instructor.
Schedule Type: Lecture
CDS 302: Scientific Data and Databases. 3 credits.
Data and databases used by scientists. Includes basics about database organization, queries, and distributed data systems. Student exercises will include queries of existing systems, along with basic design of simple database systems. Offered by Computational & Data Sciences. May not be repeated for credit.
Specialized Designation: Writing Intensive in the Major
Recommended Prerequisite: CDS 101 or CDS 130 or equivalent, or permission of instructor.
Schedule Type: Lecture
CDS 303: Scientific Data Mining. 3 credits.
Data mining techniques from statistics, machine learning, and visualization to scientific knowledge discovery. Students will be given a set of case studies and projects to test their understanding of this field and provide a foundation for future applications in their careers. Offered by Computational & Data Sciences. May not be repeated for credit.
Recommended Prerequisite: CDS 101 or CDS 130 or equivalent, or permission of instructor.
Schedule Type: Lecture

400 Level Courses

CDS 410: Numerical Analysis II. 3 credits.
Numerical differentiation and integration, initial-value and boundary-value problems for ordinary differential equations, methods of solution of partial differential equations, iterative methods of solution of nonlinear systems, and approximation theory. Offered by Computational & Data Sciences. May not be repeated for credit. Equivalent to MATH 447.
Recommended Prerequisite: MATH 214 and MATH 446, proficiency in at least one computer programming language and computer operating system; or permission of instructor.
Schedule Type: Lecture
CDS 411: Modeling and Simulation II. 3 credits.
Covers the application of modeling and simulation methods to various scientific applications, including fluid dynamics, solid mechanics, materials science, molecular mechanics, and astrophysics. Provides an introduction to modeling and simulation software, as well as high-performance computing. Offered by Computational & Data Sciences. May not be repeated for credit.
Recommended Prerequisite: MATH 446, PHYS 262 or PHYS 245, and a 200- or higher-level computational methods course, or permission of instructor.
Schedule Type: Lecture
CDS 421: Introduction to Computational Fluid Dynamics. 3 credits.
Covers the governing equations of fluid dynamics; numerical discretization of the governing equations and popular techniques for solving flow problems; applications of CFD to some classic fluid dynamics problems; and setting up the CFD simulation using a CFD software package. Students will understand the process of developing a geometrical model of the flow, applying appropriate boundary conditions, specifying solution parameters, and visualizing the results. Students are expected to complete several computer projects, including writing their own CFD computer program to analyze simple fluid flow problems, as well as setting up the CFD simulation using a CFD software package. Offered by Computational & Data Sciences. May not be repeated for credit.
Recommended Prerequisite: MATH 446, proficiency in at least one computer programming language and computer operating system, or permission of instructor.
Schedule Type: Lecture
CDS 461: Molecular Dynamics and Monte Carlo Simulations. 3 credits.
Covers particle methods to solve variety of physical systems. Emphasizes study and development of numerical results and visualization of these results in complex physical systems. Applications and projects include stellar and galaxy dynamics, smoothed particle hydrodynamics, plasma simulations, and semiconductor device theory algorithms on parallel and vectorized systems. Offered by Computational & Data Sciences. May not be repeated for credit.
Recommended Prerequisite: Competency in programming at CDS 251 level, college physics, and MATH 214 or MA TH 216, or permission of instructor.
Schedule Type: Lecture
CDS 486: Topics in Computational and Data Sciences. 3 credits.
Covers selected topics in computational and data sciences not covered in fixed content courses. Offered by Computational & Data Sciences. May be repeated within the degree for a maximum 6 credits.
Schedule Type: Lecture
CDS 487: Electronic Structure Computations. 3 credits.
Covers computational aspects of materials science, such as first-principles methods of electronic structure calculations of periodic solids, clusters, and molecules, as well as the use of empirical potentials. Examples will be drawn from metals, insulators, and semiconductors. Students will construct simple codes and be guided in the use of the more sophisticated available computational packages. Offered by Computational & Data Sciences. May not be repeated for credit.
Recommended Prerequisite: PHYS 308 or PHYS 402.
Schedule Type: Lecture
CDS 490: Directed Study and Research. 1-3 credits.
Students work under the guidance of a faculty member on an independent study or directed research project in the computational and data sciences. May be repeated in combination with CDS 491 for a total of 6 credits between the two classes. Offered by Computational & Data Sciences. May be repeated within the degree for a maximum 6 credits.
Recommended Prerequisite: Students must be CDS majors or minors in their junior or senior year and have permission of the instructor.
Schedule Type: Independent Study
CDS 491: Internship. 1-3 credits.
On-the-job experience for CDS majors and minors working in industry and government laboratories, including summer programs. Supervision and approval of this course must be arranged with department before registering. May be repeated in combination with CDS 490 for a total of 6 credits between the two classes. Offered by Computational & Data Sciences. May be repeated within the degree for a maximum 6 credits.
Recommended Prerequisite: Students must be CDS majors or minors in their junior or senior year and have permission of the instructor.
Schedule Type: Internship

500 Level Courses

CDS 501: Scientific Information and Data Visualization. 3 credits.
Techniques and software used to visualize scientific simulations, complex information, and data visualization for knowledge discovery. Includes examples and exercises to help students develop their understanding of the role visualization plays in computational science and provides a foundation for applications in their careers. Offered by Computational & Data Sciences. May not be repeated for credit.
Recommended Prerequisite: CDS 130 or CDS 101; or permission of instructor.
Registration Restrictions:

Enrollment limited to students with a class of Advanced to Candidacy, Graduate, Non Degree or Senior Plus.

Enrollment is limited to Graduate, Non-Degree or Undergraduate level students.

Students in a Non-Degree Undergraduate degree may not enroll.

Schedule Type: Lecture
CDS 502: Introduction to Scientific Data and Databases. 3 credits.
Data and databases used by scientists. Includes basics about database organization, queries, and distributed data systems. Student exercises will include queries of existing systems, along with basic design of database systems. Examples from different disciplines will be given. Offered by Computational & Data Sciences. May not be repeated for credit.
Recommended Prerequisite: CDS 130 or CDS 101; or permission of instructor.
Registration Restrictions:

Enrollment limited to students with a class of Advanced to Candidacy, Graduate, Non Degree or Senior Plus.

Enrollment is limited to Graduate, Non-Degree or Undergraduate level students.

Students in a Non-Degree Undergraduate degree may not enroll.

Schedule Type: Lecture