The mission of the Department of Computational and Data Sciences (CDS) is comprised of two objectives:

  • The first is the systematic development and application of computational techniques for modeling and simulation of scientific and social phenomena or social processes.
  • The second objective is the systematic development and application of techniques for mining, managing, and analyzing large sets of data.

The resulting interdisciplinary approach leads to understanding, interpretation, and prediction of phenomena that traditional theory or experiment cannot provide alone. CDS’s mission aims toward excellence in faculty and graduate student state-of-the-art research activities, as well as providing modern approaches to student education at both the graduate and undergraduate levels. The educational and research directions pursued in CDS are focused to reflect the interests of neighboring federal laboratories, scientific institutions, and high-technology firms to provide the students opportunities for continued or new employment. Graduate courses are also designed to accommodate part-time students, with most courses meeting once a week in the late afternoon or early evening.

The research and teaching activities associated with CDS’s programs are a reflection of the present central role of computation in the arenas of “big data” and of modeling and simulation.

Undergraduate Programs

This department offers the Computational and Data Sciences, BS, the Computational and Data Sciences Minor, and the Government Analytics Minor in cooperation with the Schar School of Policy and Government. Accelerated master’s options are also available for undergraduate students interested in the Computational Science, MS or the Bioinformatics Management, PSM.

Many opportunities exist for undergraduate students to become involved with research. Students should consult with faculty working on research topics of interest to them based on their exploration of the departmental website.

Graduate Programs

This department offers the Data Science Graduate Certificate, the Computational Social Science Graduate Certificate, the Computational Science, MS, the Computational Sciences and Informatics, PhD, and the Computational Social Science, PhD. An accelerated master’s option is also available for undergraduate students interested in the Computational Science, MS. The department also supports the Computational Social Science Concentration in the Interdisciplinary Studies, MAIS. These graduate programs are strongly supported by the extensive research activities of the faculty, including their collaborations with scientists and engineers at regional government laboratories.

Department Faculty

Professors

Axtell, Blaisten-Barojas, Croitoru

Associate Professors

Berea, Kennedy, Kinser, Lopez, Rothman

Assistant Professors

Abdullah, Belaia, Bidkhori, Dade, Kavak, White

Affiliated Faculty

Anderson, Caplan, Crooks, der Heide, Gkountouna, Griva, Handler, Lamberti, Mahabir, Melick, Renz, Roberts, Rogers, Shehu, Sheng, Smart, Susse

Mason Korea Faculty

Babalola, Colchao, Leung, Park

Adjunct Faculty

Alvarez, Ken Comer, Kevin Comer, Castro, Cruz, Hui, Iasiello, Miller, Patrick, Rahman, Rajeev, Romanelli, Russo, Scott, Silayi, Slamani, Sponseller, Swartz, Wolf

Emeritus Professors

Cioffi-Revilla, Gentle, Papaconstantopoulos

The Department of Computational and Data Sciences also supports the Computational Social Science Concentration in the Interdisciplinary Studies, MAIS.

Course Offerings

Computational and Data Sciences (CDS)

CDS Course Schedule

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. Limited to three attempts.
Mason Core: Mason Core (All), Natural Science Overview
Recommended Prerequisite: Appropriate score on the math placement test.
Schedule Type: Lecture
Grading:
This course is graded on the Undergraduate Regular scale.
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. Limited to three attempts.
Mason Core: Mason Core (All), Natural Science with Lab
Recommended Prerequisite: CDS 101. Concurrent enrollment is permitted.
Schedule Type: Laboratory
Grading:
This course is graded on the Undergraduate Regular scale.
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. Limited to three attempts.
Mason Core: Mason Core (All), Info Tech & Computing
Recommended Prerequisite: Passing score on the math placement test for MATH 113.
Schedule Type: Lecture
Grading:
This course is graded on the Undergraduate Regular scale.
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. Limited to three attempts.
Schedule Type: Lecture
Grading:
This course is graded on the Undergraduate Regular scale.

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. Limited to three attempts.
Schedule Type: Lecture
Grading:
This course is graded on the Undergraduate Regular scale.
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. Limited to three attempts.
Schedule Type: Lecture
Grading:
This course is graded on the Undergraduate Regular scale.
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 science and engineering disciplines. Continued development of algorithmic thinking skills will be done using different computational environments. Offered by Computational & Data Sciences. Limited to three attempts.
Recommended Prerequisite: CDS 130 or permission of instructor.
Schedule Type: Lecture
Grading:
This course is graded on the Undergraduate Regular scale.
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. Limited to three attempts.
Recommended Prerequisite: CDS 130.
Schedule Type: Lecture
Grading:
This course is graded on the Undergraduate Regular scale.
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.
Specialized Designation: Topic Varies
Schedule Type: Lecture
Grading:
This course is graded on the Undergraduate Regular scale.
CDS 292: Introduction to Social Network Analysis. 3 credits.
A broad introduction to network methods and applications that examine systems based on relations, structures, connectivity, location, interactions, and other network properties. This class includes, but is not limited to, social networks. Example applications covered will include: infrastructure networks, politics, diseases, and organizations, along with a variety of other phenomena. Offered by Computational & Data Sciences. Limited to three attempts.
Mason Core: Mason Core (All), Quantitative Reasoning
Schedule Type: Lecture
Grading:
This course is graded on the Undergraduate Regular scale.

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. Limited to three attempts.
Recommended Prerequisite: CDS 101 or CDS 130 or equivalent, or permission of instructor.
Schedule Type: Lecture
Grading:
This course is graded on the Undergraduate Regular scale.
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. Limited to three attempts.
Mason Core: Mason Core (All)
Specialized Designation: Writing Intensive in Major
Recommended Prerequisite: CDS 101 or CDS 130 or equivalent, or permission of instructor.
Schedule Type: Lecture
Grading:
This course is graded on the Undergraduate Regular scale.
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. Limited to three attempts.
Recommended Prerequisite: CDS 101 or CDS 130 or equivalent, or permission of instructor.
Schedule Type: Lecture
Grading:
This course is graded on the Undergraduate Regular scale.
CDS 321: Elements of Natural Language Processing. 3 credits.
This course teaches the fundamentals of natural language processing (NLP) and natural language understanding (NLU) and helps develop necessary skills for beginner and intermediate level computational linguistics models, useful for analyzing text or speech from different human languages. This course teaches various NLP/NLU methods, including text mining, text analyses and parsing, topic modeling, semantic similarities, vector representations of words, and gives an introduction to large language models (LLMs). Offered by Computational & Data Sciences. Limited to two attempts.
Recommended Prerequisite: CDS 303
Registration Restrictions:

Required Prerequisites: CDS 101C, 130C and 230C.
C Requires minimum grade of C.

Schedule Type: Lecture
Grading:
This course is graded on the Undergraduate Regular scale.
CDS 351: Elements of High Performance Computing. 3 credits.
The course explores aspects of high-performance computing (HPC) based on a diverse set of tools, including Unix basics, file systems, command scripts, Git, C++ programming, basics of parallel programming, and HPC system architectures. Offered by Computational & Data Sciences. Limited to two attempts.
Registration Restrictions:

Required Prerequisite: CDS 251C.
C Requires minimum grade of C.

Schedule Type: Lecture
Grading:
This course is graded on the Undergraduate Regular scale.

400 Level Courses

CDS 403: Machine Learning Applications in Science. 3 credits.
Covers practical applications in STEM areas of decision trees, rule-based classification, support vector machines, Bayesian networks, ensemble methods, and Neural Networks. Emphasis resides on the process of applying machine learning effectively to a variety of problems. Offered by Computational & Data Sciences. Limited to three attempts.
Registration Restrictions:

Required Prerequisites: (CDS 230C or 230XS) and (MATH 203C or 203XS) and (CDS 303C or 303XS).
C Requires minimum grade of C.
XS Requires minimum grade of XS.

Schedule Type: Lecture
Grading:
This course is graded on the Undergraduate Regular scale.
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. Limited to three attempts. 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
Grading:
This course is graded on the Undergraduate Regular scale.
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. Limited to three attempts.
Recommended Prerequisite: MATH 203, PHYS 262 or PHYS 245 or higher-level programming course, or permission of instructor.
Registration Restrictions:

Required Prerequisite: CDS 230C.
C Requires minimum grade of C.

Schedule Type: Lecture
Grading:
This course is graded on the Undergraduate Regular scale.
CDS 421: Computational Data Science. 3 credits.
Covers the governing framework of data science for storing and processing big data in a distributed computer environment using simple programming models. Includes a comprehensive selection of tools from Hadoop, MapReduce, HDFS, Spark, Flink, Hive, HBase, MongoDB, Cassandra, Kafka. Students are expected to complete several computer projects using these cyber packages. Offered by Computational & Data Sciences. Limited to three attempts.
Recommended Prerequisite: CDS 251 or equivalent computer programming language, and knowledge of computer operating system, or permission of instructor.
Schedule Type: Lecture
Grading:
This course is graded on the Undergraduate Regular scale.
CDS 461: Molecular Dynamics and Monte Carlo Simulations. 3 credits.
Covers particle methods to solve variety of physical systems. Emphasizes study of structure and thermodynamics of condensed systems in liquid and solid phases while implementing numerically the Molecular Dynamics and Monte Carlo methods. Applications and projects include a variety of atomistic and molecular simulations based on pairwise interatomic interactions. Offered by Computational & Data Sciences. Limited to two attempts.
Recommended Prerequisite: Competency in programming at CDS 251 level or higher and MATH 214 or MATH 216, or permission of the instructor.
Registration Restrictions:

Required Prerequisites: CDS 251C and PHYS 243C.
C Requires minimum grade of C.

Schedule Type: Lecture
Grading:
This course is graded on the Undergraduate Regular scale.
CDS 465: Modeling Interactive Populations. 3 credits.
Employs several computational methods to create an agent-based model of an evolving and interactive population. Applied scenarios will include human identification through DNA profiles, community analysis through connected graphs, data generation, virus tracking, and evolution of human traits in time. Software skills developed will include Python, Pandas, and SQL. Offered by Computational & Data Sciences. Limited to two attempts.
Registration Restrictions:

Required Prerequisites: (CDS 230C and 302C).
C Requires minimum grade of C.

Schedule Type: Lecture
Grading:
This course is graded on the Undergraduate Regular scale.
CDS 468: Image Operators and Processing. 3 credits.
An introductory examination of image mathematics, computational protocols, and applications. Topics include image operator notation, channel operators, informational operators, intensity operators, geometric operators, image transformations, frequency filtering, and image basis set expansions. This course will build the students’ computational skill set as applied to visual data and create a library of image analysis scripts. Offered by Computational & Data Sciences. Limited to two attempts.
Recommended Prerequisite: CDS 230 or equivalent.
Schedule Type: Lecture
Grading:
This course is graded on the Undergraduate Regular scale.
CDS 486: Advanced 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.
Specialized Designation: Topic Varies
Schedule Type: Lecture
Grading:
This course is graded on the Undergraduate Regular scale.
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
Grading:
This course is graded on the Undergraduate Regular scale.
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
Grading:
This course is graded on the Satisfactory/No Credit scale.
CDS 492: Capstone in Data Science. 3 credits.
This course is intended to provide a capstone experience for undergraduate students by synthesizing knowledge and experience that they acquired in earlier coursework to address a complex Data Science problem. This course requires analytical, collaborative, and communication skills. Offered by Computational & Data Sciences. Limited to three attempts.
Mason Core: Mason Core (All), Mason Apex
Recommended Prerequisite: CDS 230 and (CDS 301 or CDS 302) or permission of instructor.
Schedule Type: Lecture
Grading:
This course is graded on the Undergraduate Regular scale.

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, Junior Plus, 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
Grading:
This course is graded on the Graduate Regular scale.
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, Junior Plus, 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
Grading:
This course is graded on the Graduate Regular scale.

Computational Sciences and Informatics (CSI)

CSI Course Schedule

500 Level Courses

CSI 500: Computational Science Tools. 3 credits.
Introduces computer skills and packages commonly used in quantitative scientific research. Notes: CSI 601 and CSI 602, including additional material, have merged to create CSI 500. Offered by Computational & Data Sciences. May not be repeated for credit.
Recommended Prerequisite: 1 year of college calculus, knowledge of matrix algebra, and computer programming.
Registration Restrictions:

Enrollment limited to students with a class of Advanced to Candidacy, Graduate, Junior Plus, 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
Grading:
This course is graded on the Graduate Regular scale.
CSI 501: Computational Science Programming. 3 credits.
Introduces and reviews programming in C and FORTRAN with emphasis on the aspects used in the computational and data sciences. Conducted through a combination of both lecture and interactive computer laboratory. Offered by Computational & Data Sciences. May not be repeated for credit.
Recommended Prerequisite: Permission of instructor.
Registration Restrictions:

Enrollment limited to students with a class of Advanced to Candidacy, Graduate, Junior Plus, 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
Grading:
This course is graded on the Graduate Regular scale.
CSI 590: Quantitative Foundations for Computational Sciences. 3 credits.
Accelerated review of mathematical tools for scientific applications and analysis. Topics include vectors and matrices; differential and difference equations; linear systems; Fourier, Laplace, and Z-transforms; and probability theory. Notes: Not applicable to 48-credit course total for CSI PhD. Offered by Computational & Data Sciences. Limited to two attempts. Equivalent to SYST 500.
Recommended Prerequisite: MATH 213 and 214.
Registration Restrictions:

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

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

Schedule Type: Lecture
Grading:
This course is graded on the Graduate Regular scale.
CSI 597: Topics in Science and Engineering Simulation. 3 credits.
Covers selected topics in Science and Engineering simulation, not covered in fixed content computational sciences and informatics courses. Offered by Computational & Data Sciences. May not be repeated for credit.
Specialized Designation: Topic Varies
Recommended Prerequisite: Permission of instructor.
Registration Restrictions:

Enrollment limited to students with a class of Advanced to Candidacy, Graduate, Junior Plus, 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
Grading:
This course is graded on the Graduate Regular scale.

600 Level Courses

CSI 600: Quantitative Foundations for Computational Sciences. 3 credits.
Accelerated review of mathematical tools for scientific applications and analysis. Topics include vectors and matrices; differential and difference equations; linear systems; Fourier, Laplace, and Z-transforms; and probability theory. Notes: Not applicable to 48-credit course total for CSI PhD. Offered by Computational & Data Sciences. May not be repeated for credit. Equivalent to SYST 500.
Recommended Prerequisite: MATH 213 and 214.
Registration Restrictions:

Enrollment limited to students with a class of Advanced to Candidacy, Graduate, Junior Plus, 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
Grading:
This course is graded on the Graduate Regular scale.
CSI 639: Ethics in Scientific Research. 3 credits.
Reviews purpose of scientific research and principles for evaluating ethical issues. Teaches skills for survival through training in moral reasoning and responsible conduct. Discusses ethical issues and applying critical-thinking skills to design, execution, and analysis of experiments. Issues include using animals, humans in research; ethical standards in computer community; research fraud; and currently accepted guidelines for data ownership, manuscript preparation, and conduct of those in authority. Offered by Computational & Data Sciences. May not be repeated for credit.
Registration Restrictions:

Enrollment limited to students with a class of Advanced to Candidacy, Graduate, Junior Plus, 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
Grading:
This course is graded on the Graduate Regular scale.
CSI 672: Statistical Inference. 3 credits.
Fundamental principles of estimation and hypothesis testing. Topics include limiting distributions and stochastic convergence, sufficient statistics, exponential families, statistical decision theory and optimality for point estimation, Bayesian methods, maximum likelihood, asymptotic results, interval estimation, optimal tests of statistical hypotheses, and likelihood ratio tests. Offered by Computational & Data Sciences. May not be repeated for credit. Equivalent to STAT 652.
Registration Restrictions:

Required Prerequisites: ((STAT 544B- or 544XS) and (STAT 554*B- or 554XS)).
* May be taken concurrently.
B- Requires minimum grade of B-.
XS Requires minimum grade of XS.

Enrollment limited to students with a class of Advanced to Candidacy, Graduate, Junior Plus, 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
Grading:
This course is graded on the Graduate Regular scale.
CSI 674: Bayesian Artificial Intelligence. 3 credits.
Many artificial intelligence problems involve modeling uncertainty. Bayesian probabilistic models represent uncertainty and dependencies between random variables using probability distributions. You will learn the set of rules of probability and computational algorithms to manipulate these distributions. Bayesian approach enhances the effectiveness of conventional AI techniques. This course summarizes various Bayesian-based models and the standard algorithms used with them, supplemented by instances of their practical use. We will discuss applications in science, engineering, economics, medicine, sport, and law. Students will learn the commonalities and differences between the Bayesian and frequentist approaches to statistical inference, how to approach a statistics problem from the Bayesian perspective, and how to combine data with informed expert judgment soundly to derive useful and policy-relevant conclusions. Assignments focus on applying the methods to practical problems. Offered by Computational & Data Sciences. May not be repeated for credit. Equivalent to OR 664, SYST 664.
Recommended Prerequisite: STAT 544, STAT 554, or equivalent.
Registration Restrictions:

Enrollment limited to students with a class of Advanced to Candidacy, Graduate, Junior Plus, 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.

Enrollment limited to students in the College of Science or Engineering Computing colleges.

Schedule Type: Lecture
Grading:
This course is graded on the Graduate Regular scale.
CSI 676: Regression Analysis. 3 credits.
Simple and multiple linear regression, polynomial regression, general linear models, subset selection, step-wise regression, and model selection. Also covered are multicollinearity, diagnostics, and model building as well as the theory and practice of regression analysis. Offered by Computational & Data Sciences. May not be repeated for credit. Equivalent to STAT 656.
Registration Restrictions:

Required Prerequisites: ((STAT 544*B- or 544XS) and (STAT 554B- or 554XS)).
* May be taken concurrently.
B- Requires minimum grade of B-.
XS Requires minimum grade of XS.

Enrollment limited to students with a class of Advanced to Candidacy, Graduate, Junior Plus 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
Grading:
This course is graded on the Graduate Regular scale.
CSI 678: Times Series Analysis and Forecasting. 3 credits.
Modeling stationary and nonstationary processes; autoregressive, moving average and mixed model processes; hidden periodicity models; properties of models; autocovariance and autocorrelation functions, and partial autocorrelation function; spectral density functions; identification of models; estimation of model parameters, and forecasting techniques. Offered by Computational & Data Sciences. May not be repeated for credit.
Registration Restrictions:

Required Prerequisites: (STAT 544B- or 544XS) and (STAT 554B- or 554XS).
B- Requires minimum grade of B-.
XS Requires minimum grade of XS.

Enrollment limited to students with a class of Advanced to Candidacy, Graduate, Junior Plus, 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
Grading:
This course is graded on the Graduate Regular scale.
CSI 685: Fundamentals of Materials Science. 3 credits.
Covers fundamentals of materials science with emphasis on physical topics including crystal structure and symmetry, dislocation theory, theory of interfaces, multicomponent phase diagrams, theory of phase transformations, nano-materials, metallic glasses. Includes a term project, assignments from current literature, and application of computation in materials science. Offered by Computational & Data Sciences. May not be repeated for credit. Equivalent to PHYS 615.
Recommended Prerequisite: Undergraduate degree in electrical or mechanical engineering, materials science, physics, chemistry or related disciplines; or permission of instructor.
Registration Restrictions:

Enrollment limited to students with a class of Advanced to Candidacy, Graduate, Junior Plus, 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
Grading:
This course is graded on the Graduate Regular scale.
CSI 690: Numerical Methods. 3 credits.
Covers computational techniques for solving science, engineering problems. Develops algorithms to treat typical problems in applications, emphasizing types of data encountered in practice. Covers theoretical development as well as implementation, efficiency, and accuracy issues in using algorithms and interpreting results. When applicable, uses computer graphical techniques to enhance interpretation. Offered by Computational & Data Sciences. May not be repeated for credit. Equivalent to MATH 685, OR 682.
Recommended Prerequisite: MATH 203 and 214 or equivalent, and some programming experience.
Registration Restrictions:

Enrollment limited to students with a class of Advanced to Candidacy, Graduate, Junior Plus, 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
Grading:
This course is graded on the Graduate Regular scale.
CSI 695: Scientific Databases. 3 credits.
Study of database support for scientific data management. Covers requirements and properties of scientific databases, data models for statistical and scientific databases, semantic and object-oriented modeling of application domains, statistical database query languages and query optimization, advanced logic query languages, and case studies such as the human genome project and Earth-orbiting satellites. Offered by Computational & Data Sciences. May not be repeated for credit.
Recommended Prerequisite: INFS 614 or equivalent, or permission of instructor.
Registration Restrictions:

Enrollment limited to students with a class of Advanced to Candidacy, Graduate, Junior Plus, 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
Grading:
This course is graded on the Graduate Regular scale.

700 Level Courses

CSI 701: Foundations of Computational Science. 3 credits.
Covers mapping of mathematical models to computer software, including all aspects of developing scientific software such as architecture, data structures, advanced numerical algorithms, languages, documentation, optimization, validation, verification, and software reuse. Examples in bioinformatics, computational biology, computational physics, and global change demonstrate scientific advances enabled by computation. Class projects involve working in teams to develop software that implements mathematical models, using software to address important scientific questions, and conducting computational experiments with it. Offered by Computational & Data Sciences. May not be repeated for credit.
Recommended Prerequisite: Competency in UNIX and programming at CSI 501 level, and CSI 690; or permission of instructor.
Registration Restrictions:

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

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

Schedule Type: Lecture
Grading:
This course is graded on the Graduate Regular scale.
CSI 702: High-Performance Computing. 3 credits.
Hardware and software associated with high-performance scientific computing. Computer architectures, processor design, programming paradigms, parallel and vector algorithms. Emphasizes importance of software scalability in science problems. Offered by Computational & Data Sciences. May not be repeated for credit.
Recommended Prerequisite: Competency in Linux and programming at CSI 501 level or permission of instructor.
Registration Restrictions:

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

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

Schedule Type: Lecture
Grading:
This course is graded on the Graduate Regular scale.
CSI 703: Scientific and Statistical Visualization. 3 credits.
Covers visualization methods used to provide new insights and intuition concerning measurements of natural phenomena and scientific and mathematical models. Presents case studies from myriad disciplines. Topics include human perception and cognition, introduction to graphics laboratory, elements of graphing data, representation of space-time and vector variables, representation of 3-D and higher dimensional data, dynamic graphical methods, and virtual reality. Work on a visualization project required. Emphasizes software tools on Silicon Graphics workstation, but other workstations and software may be used. Offered by Computational & Data Sciences. May not be repeated for credit.
Recommended Prerequisite: STAT 554 or CS 551, or permission of instructor.
Registration Restrictions:

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

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

Schedule Type: Lecture
Grading:
This course is graded on the Graduate Regular scale.
CSI 709: Topics in Computational Sciences and Informatics. 3 credits.
Covers selected topics in computational sciences and informatics not covered in fixed-content computational sciences and informatics courses. Offered by Computational & Data Sciences. May be repeated within the term for a maximum 9 credits.
Specialized Designation: Topic Varies
Recommended Prerequisite: Admission to the PhD program and permission of instructor.
Registration Restrictions:

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

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

Schedule Type: Lecture
Grading:
This course is graded on the Graduate Regular scale.
CSI 711: Chemical Thermodynamics and Kinetics. 3 credits.
Advanced study of thermodynamics and kinetics. Covers application of kinetics to elucidation of reaction mechanisms and application of statistical thermodynamics to theory of elementary reaction rates. Offered by Computational & Data Sciences. May not be repeated for credit. Equivalent to CHEM 633.
Recommended Prerequisite: CHEM 331 and 332.
Registration Restrictions:

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

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

Schedule Type: Lecture
Grading:
This course is graded on the Graduate Regular scale.
CSI 720: Fluid Mechanics. 3 credits.
Covers basic and advanced fluid mechanics and continuous hypothesis to define fluids. Introduces tensor analysis; Euclidean and Lagrangian representations of fluid flow; Laplace's equation; continuity equation; Navier-Stokes equations; Bernoulli's theorem and Crocco's form of the equations; steady and unsteady flows; potential, incompressible, and compressible flows; gravity and sound waves; gas dynamics; and viscous flows. Offered by Computational & Data Sciences. May not be repeated for credit.
Recommended Prerequisite: CSI 690 and CSI 780, or permission of instructor.
Registration Restrictions:

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

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

Schedule Type: Lecture
Grading:
This course is graded on the Graduate Regular scale.
CSI 721: Computational Fluid Dynamics I. 3 credits.
Covers fundamentals including spatial and temporal approximation techniques for partial differential equations, solution of large systems of equations, data structures, solvers of the Laplace/ full potential equation, and simple Euler solvers. Includes two major projects: Laplace solver and 2-D Euler solver on unstructured grids. Students expected to write their own codes. Offered by Computational & Data Sciences. May not be repeated for credit.
Recommended Prerequisite: Course in partial differential equations such as MATH 678 or equivalent; knowledge of linear algebra at level of MATH 603 or CSI 740/MATH 625; coding experience in FORTRAN or C; or permission of instructor.
Registration Restrictions:

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

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

Schedule Type: Lecture
Grading:
This course is graded on the Graduate Regular scale.
CSI 739: Topics in Bioinformatics. 3 credits.
Selected topics in bioinformatics not covered in fixed-content bioinformatics courses. Offered by Computational & Data Sciences. May not be repeated for credit.
Specialized Designation: Topic Varies
Recommended Prerequisite: Permission of instructor.
Registration Restrictions:

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

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

Schedule Type: Lecture
Grading:
This course is graded on the Graduate Regular scale.
CSI 740: Numerical Linear Algebra. 3 credits.
Covers computational methods for matrix systems; theory and development of numerical algorithms for the solution of linear systems of equations, including direct and iterative methods; analysis of sensitivity of system to computer round off; and solution of least squares problems using orthogonal matrices. Also covers computation of eigenvalues and eigenvectors, singular value decomposition, and applications. Offered by Computational & Data Sciences. May not be repeated for credit. Equivalent to MATH 625.
Recommended Prerequisite: MATH 203 and some programming experience.
Registration Restrictions:

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

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

Schedule Type: Lecture
Grading:
This course is graded on the Graduate Regular scale.
CSI 742: The Mathematics of the Finite Element Method. 3 credits.
The finite element method is commonly used for developing numerical approximations to problems involving ordinary and partial differential equations. Course develops underlying mathematical foundation, examines specific types of finite elements, analyzes convergence rates and approximation properties, and uses method to solve important equations. Students develop their own codes and are expected to complete independent projects. Offered by Computational & Data Sciences. May not be repeated for credit.
Recommended Prerequisite: MATH 446 or 685, or permission of instructor.
Registration Restrictions:

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

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

Schedule Type: Lecture
Grading:
This course is graded on the Graduate Regular scale.
CSI 744: Linear and Nonlinear Modeling in the Natural Sciences. 3 credits.
Develops tools of mathematical modeling while carrying out numerical simulations. Considers examples from across the sciences. Topics include basic issues such as models, simplification, linearity, and nonlinearity; dimensionless parameters; dimensional analysis; models involving differential equations; examples from population growth and chemical kinetics; models involving partial differential equations; diffusion, transport, nonlinearity and shocks; probabilistic modeling; perturbation methods; extrapolation; and introduction to stability. Offered by Computational & Data Sciences. May not be repeated for credit.
Recommended Prerequisite: Permission of Instructor.
Registration Restrictions:

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

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

Schedule Type: Lecture
Grading:
This course is graded on the Graduate Regular scale.
CSI 745: Robust Optimization for Decision Making. 3 credits.
This course aims to cover modern robust optimization tools for data-driven decision-making under uncertainty. The course includes theory, applications, and computations. Application domains include analysis and optimization of stochastic networks, transportation, machine learning, finance, and energy. The course utilizes Python and IBM ILOG CPLEX Optimizer for computations. Offered by Computational & Data Sciences. May not be repeated for credit.
Recommended Prerequisite: CSI 690 or equivalent or permission from the instructor
Registration Restrictions:

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

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

Schedule Type: Lecture
Grading:
This course is graded on the Graduate Regular scale.
CSI 747: Nonlinear Optimization and Applications. 3 credits.
Introduction to practical aspects of nonlinear optimization. Covers applications of optimization algorithms to solving problems in science and engineering. Applications include data analysis, materials science, nanotechnology, mechanics, optical design, shape design, and trajectory optimization. Offered by Computational & Data Sciences. May not be repeated for credit.
Recommended Prerequisite: MATH 213 and 216, or permission of instructor.
Registration Restrictions:

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

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

Schedule Type: Lecture
Grading:
This course is graded on the Graduate Regular scale.
CSI 749: Topics in Computational Mathematics. 3 credits.
Selected topics in computational mathematics not covered in fixed-content computational mathematics courses. Offered by Computational & Data Sciences. May not be repeated for credit.
Specialized Designation: Topic Varies
Recommended Prerequisite: Permission of instructor.
Registration Restrictions:

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

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

Schedule Type: Lecture
Grading:
This course is graded on the Graduate Regular scale.
CSI 758: Visualization and Modeling of Complex Systems. 3 credits.
Covers elements of modeling and analysis for scientific applications. Concentrates on sample projects and student-initiated projects to use visualization, image and graphical analysis as they apply to modeling of complex data sets and systems. Reviews methods of creating and generating analysis and visualization packages. Data sets from multiple sources will be used. Modeling and analysis accompanied by appropriate readings from current literature. Offered by Computational & Data Sciences. May not be repeated for credit.
Recommended Prerequisite: Permission of instructor.
Registration Restrictions:

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

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

Schedule Type: Lecture
Grading:
This course is graded on the Graduate Regular scale.
CSI 771: Computational Statistics. 3 credits.
Covers basic computationally intensive statistical methods and related methods, which would not be feasible without modern computational resources. Covers nonparametric density estimation including kernel methods, orthogonal series methods and multivariate methods, recursive methods, cross-validation, nonparametric regression, penalized smoothing splines, the jackknife and bootstrapping, computational aspects of exploratory methods including the grand tour, projection pursuit, alternating conditional expectations, and inverse regression methods. Offered by Computational & Data Sciences. May not be repeated for credit.
Registration Restrictions:

Required Prerequisites: CSI 672B- or 672XS.
B- Requires minimum grade of B-.
XS Requires minimum grade of XS.

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

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

Schedule Type: Lecture
Grading:
This course is graded on the Graduate Regular scale.
CSI 772: Data-Driven Modeling and Learning. 3 credits.
Focuses on advances in data science related to statistical learning theory by introducing modern topics on data analytics, classification, clustering, and regression techniques, as well as data-driven decision-making. The course includes the statistical and optimization background essential for developing new efficient statistical learning, data-driven methods and algorithms. Also discusses applications of data-driven statistical learning algorithms to the solution of important real-world problems that arise in areas of science and other domains. Offered by Computational & Data Sciences. May not be repeated for credit.
Recommended Prerequisite: CSI 690
Registration Restrictions:

Required Prerequisites: STAT 652B-, 652XS, CSI 672B- or 672XS.
B- Requires minimum grade of B-.
XS Requires minimum grade of XS.

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

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

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

Schedule Type: Lecture
Grading:
This course is graded on the Graduate Regular scale.
CSI 773: Statistical Graphics and Data Exploration. 3 credits.
Exploratory data analysis provides a reliable alternative to classical statistical techniques, which are designed to be the best possible when stringent assumptions apply. Topics include graphical techniques such as scatter plots, box plots, parallel coordinate plots, and other graphical devices; re-expression and transformation of data; influence and leverage; and dimensionality reduction methods such as projection pursuit. Offered by Computational & Data Sciences. May not be repeated for credit.
Recommended Prerequisite: A 300-level statistics course and a programming course, or permission of instructor.
Registration Restrictions:

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

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

Schedule Type: Lecture
Grading:
This course is graded on the Graduate Regular scale.
CSI 775: Graphical Models for Inference and Decision Making. 3 credits.
Theory and methods for inference and decision making in environments characterized by uncertain information. Covers graphical probability and decision models. Studies approaches to representing knowledge about uncertain phenomena, and planning and acting under uncertainty. Topics include knowledge engineering, exact and approximate inference in graphical models, learning in graphical models, temporal reasoning, planning, and decision-making. Practical model-building experience provided. Students apply what they learn to a project of their own choosing. Offered by Computational & Data Sciences. May not be repeated for credit. Equivalent to OR 719.
Recommended Prerequisite: STAT 652 or permission of instructor.
Registration Restrictions:

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

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

Schedule Type: Lecture
Grading:
This course is graded on the Graduate Regular scale.
CSI 777: Principles of Knowledge Mining. 3 credits.
Principles and methods for synthesizing task-oriented knowledge from computer data and prior knowledge and presenting it in human-oriented forms such as symbolic descriptions, natural language-like representations, and graphical forms. Topics include fundamental concepts of knowledge mining; methods for target data generation and optimization; statistical and symbolic approaches; knowledge representation and visualization; and new developments such as inductive databases, knowledge generation languages, and knowledge scouts. Offered by Computational & Data Sciences. May not be repeated for credit.
Recommended Prerequisite: INFS 614 or equivalent, or Permission of Instructor.
Registration Restrictions:

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

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

Schedule Type: Lecture
Grading:
This course is graded on the Graduate Regular scale.
CSI 779: Topics in Computational Statistics. 3 credits.
Selected topics in computational statistics not covered in fixed-content computational statistics courses. Offered by Computational & Data Sciences. May be repeated within the term.
Specialized Designation: Topic Varies
Recommended Prerequisite: Permission of instructor.
Registration Restrictions:

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

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

Schedule Type: Lecture
Grading:
This course is graded on the Graduate Regular scale.
CSI 780: Principles of Modeling and Simulation in Science. 3 credits.
Applies numerical methods to study of variety of physical systems, with emphasis on modeling and simulation. Develops numerical algorithms and simulation codes to gain understanding of mechanisms, processes in physical systems. Includes several projects drawn from such areas as atomic and molecular interactions, molecular dynamics, lattice dynamics, quantum systems, chaos, percolation, random walks, aggregation mechanisms of soft solids, nanomaterials, and nonlinear dynamics. Offered by Computational & Data Sciences. May not be repeated for credit.
Recommended Prerequisite: Competency in programming at CSI 501 level or permission of instructor.
Registration Restrictions:

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

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

Schedule Type: Lecture
Grading:
This course is graded on the Graduate Regular scale.
CSI 782: Statistical Mechanics for Modeling and Simulation. 3 credits.
Studies microcanonical, canonical, and grand canonical ensembles and fluctuations. Includes modeling of ideal, dilute, and diatomic gases, liquids, and crystals, and the Liouville equation. Introduces Brownian motion, kinetic theory, and transport processes. Includes Monte Carlo algorithms and numerical methods for simulation in classical statistical mechanics. Offered by Computational & Data Sciences. May not be repeated for credit.
Recommended Prerequisite: CSI 690, or permission of instructor.
Registration Restrictions:

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

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

Schedule Type: Lecture
Grading:
This course is graded on the Graduate Regular scale.
CSI 783: Computational Quantum Mechanics. 3 credits.
Studies fundamental concepts of quantum mechanics from computational point of view, review of systems with spherically symmetric potentials, many electron atom solutions to Schrodinger's equation, electron spin in many-electron systems, atomic structure calculations, algebra of many-electron calculations, Hartree-Fock self-consistent field method, molecular structure calculations, scattering theory computations, and solid-state computations. Offered by Computational & Data Sciences. May not be repeated for credit. Equivalent to CHEM 736, PHYS 736.
Recommended Prerequisite: PHYS 502 and CSI 690 or permission of instructor.
Registration Restrictions:

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

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

Schedule Type: Lecture
Grading:
This course is graded on the Graduate Regular scale.
CSI 786: Molecular Dynamics Modeling. 3 credits.
Introduces simulation methods in physical chemistry sciences. Covers computational approaches to modeling molecular and condensed matter systems, including interatomic and molecular potentials, Molecular Dynamics methods, time averages, ensemble distributions, numerical sampling, thermodynamic functions, response theory, transport coefficients, and dynamic structure. Offered by Computational & Data Sciences. May not be repeated for credit.
Recommended Prerequisite: CSI 690 or CSI 780 or equivalent, or CHEM 633/CSI 711, or permission of instructor.
Registration Restrictions:

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

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

Schedule Type: Lecture
Grading:
This course is graded on the Graduate Regular scale.
CSI 789: Topics in Computational Physics. 3 credits.
Selected topics in computational physics not covered in fixed-content computational physics courses. Offered by Computational & Data Sciences. May not be repeated for credit.
Specialized Designation: Topic Varies
Recommended Prerequisite: Permission of instructor.
Registration Restrictions:

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

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

Schedule Type: Lecture
Grading:
This course is graded on the Graduate Regular scale.
CSI 796: Directed Reading and Research. 1-6 credits.
Reading and research on specific topic in computational sciences and informatics under direction of faculty member. May be repeated for a total of 6 credits. Offered by Computational & Data Sciences. May be repeated within the degree for a maximum 6 credits.
Recommended Prerequisite: Permission of instructor.
Registration Restrictions:

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

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

Schedule Type: Research
Grading:
This course is graded on the Graduate Regular scale.
CSI 798: Practicum Project. 1-3 credits.
Technical project involving the supervised practical application of previously studied coursework to be performed under the guidance of the Department of Computational and Data Sciences graduate faculty, plus a supervisor external to Mason in case of internships. Offered by Computational & Data Sciences. May be repeated within the degree for a maximum 3 credits.
Recommended Prerequisite: 12 graduate credits in the Master in Computational Science and permission of the graduate coordinator.
Registration Restrictions:

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

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

Schedule Type: Thesis
Grading:
This course is graded on the Satisfactory/No Credit scale.
CSI 799: Master's Thesis. 1-6 credits.
Project chosen and completed under guidance of graduate faculty member, resulting in acceptable technical report (master's thesis) and oral defense. Offered by Computational & Data Sciences. May be repeated within the degree.
Recommended Prerequisite: Completion of twelve graduate credits and Permission of Instructor.
Registration Restrictions:

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

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

Schedule Type: Thesis
Grading:
This course is graded on the Satisfactory/No Credit scale.

800 Level Courses

CSI 873: Computational Learning and Discovery. 3 credits.
Presents modern ideas, theories, and methods for computational learning and discovery, along with relevant applications including medical diagnosis, Earth science data analysis, and neuronal modeling. Includes background elucidation of fundamental concepts in computational learning, addressing discovery of equations, theory of causality, and comparison with biological and cognitive models. Students make presentations on topics of their research interest and work on projects involving state-of-the art systems. Offered by Computational & Data Sciences. May not be repeated for credit. Equivalent to CSI 763.
Recommended Prerequisite: CS 580 or equivalent or permission of instructor.
Registration Restrictions:

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

Students in a Non-Degree Post-Baccalaureate or Non-Degree Undergraduate degrees may not enroll.

Schedule Type: Lecture
Grading:
This course is graded on the Graduate Regular scale.
CSI 898: Research Colloquium in Computational Sciences and Informatics. 1 credit.
Presentations in specific research areas in computational sciences and informatics by faculty and staff members and professional visitors. Notes: A maximum 3 credits of CSI 898, 899, and 991 may be applied to PhD. Offered by Computational & Data Sciences. May be repeated within the term.
Specialized Designation: Topic Varies
Registration Restrictions:

Enrollment is limited to Graduate level students.

Schedule Type: Seminar
Grading:
This course is graded on the Satisfactory/No Credit scale.
CSI 899: Colloquium in Computational and Data Sciences. 1 credit.
Presentations in specific research areas in computational sciences and informatics by faculty and staff members and professional visitors. Notes: A maximum 3 credits of CSI 898, 899, and 991 may be applied to PhD. Offered by Computational & Data Sciences. May be repeated within the term.
Specialized Designation: Topic Varies
Registration Restrictions:

Enrollment is limited to Graduate level students.

Schedule Type: Seminar
Grading:
This course is graded on the Satisfactory/No Credit scale.

900 Level Courses

CSI 986: Advanced Topics in Large-Scale Physical Simulation. 3 credits.
Covers simulation of physical systems not covered in fixed-content physical simulation courses. Offered by Computational & Data Sciences. May be repeated within the degree for a maximum 12 credits.
Specialized Designation: Topic Varies
Recommended Prerequisite: Permission of instructor.
Registration Restrictions:

Enrollment is limited to Graduate level students.

Schedule Type: Lecture
Grading:
This course is graded on the Graduate Regular scale.
CSI 996: Doctoral Reading and Research. 1-6 credits.
Reading and research on specific topic in computational sciences and informatics under direction of faculty member. May be repeated for a total of 6 credits. Offered by Computational & Data Sciences. May be repeated within the degree for a maximum 6 credits.
Recommended Prerequisite: Admission to doctoral program, permission of instructor.
Registration Restrictions:

Enrollment is limited to Graduate level students.

Schedule Type: Research
Grading:
This course is graded on the Graduate Regular scale.
CSI 998: Doctoral Dissertation Proposal. 1-12 credits.
Covers development of research proposal under guidance of dissertation director and doctoral committee. Proposal forms basis for doctoral dissertation. Notes: No more than 12 credits of CSI 998 may be applied to doctoral degree. Offered by Computational & Data Sciences. May be repeated within the degree.
Recommended Prerequisite: Permission of advisor.
Registration Restrictions:

Enrollment is limited to Graduate level students.

Schedule Type: Dissertation
Grading:
This course is graded on the Satisfactory/No Credit scale.
CSI 999: Doctoral Dissertation. 1-12 credits.
Involves doctoral dissertation research under direction of dissertation director. Notes: No more than 24 credits in CSI 998 and 999 may be applied to doctoral degree. Offered by Computational & Data Sciences. May be repeated within the degree.
Registration Restrictions:

Enrollment limited to students with a class of Advanced to Candidacy.

Enrollment is limited to Graduate level students.

Schedule Type: Dissertation
Grading:
This course is graded on the Satisfactory/No Credit scale.

Computational Social Science (CSS)

CSS Course Schedule

600 Level Courses

CSS 600: Introduction to Computational Social Science. 3 credits.
Graduate-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.
Registration Restrictions:

Enrollment limited to students with a class of Advanced to Candidacy, Graduate, Junior Plus, 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: Seminar
Grading:
This course is graded on the Graduate Regular scale.
CSS 605: Object-Oriented Modeling in Social Science. 3 credits.
Presents and applies concepts and principles from object-based modeling paradigm. Emphasizes Unified Modeling Language (UML) to render structure and operation of complex social systems and processes. Offered by Computational & Data Sciences. May not be repeated for credit.
Recommended Prerequisite: CSS 600 or approval from instructor or program director. Concurrent enrollment is also permitted.
Registration Restrictions:

Enrollment limited to students with a class of Advanced to Candidacy, Graduate, Junior Plus, 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: Seminar
Grading:
This course is graded on the Graduate Regular scale.
CSS 610: Agent-based Modeling and Simulation. 3 credits.
Provides hands-on examination of agent-based models in social sciences by examining and experimenting with variety of social-simulation projects conducted in modeling environments such as Swarm, Repast, Ascape, and MASON (Multi-Agent Simulator of Networks and Neighborhoods). Offered by Computational & Data Sciences. May not be repeated for credit.
Recommended Prerequisite: CSS 600 or permission of instructor. Concurrent enrollment is also permitted.
Registration Restrictions:

Enrollment limited to students with a class of Advanced to Candidacy, Graduate, Junior Plus, 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: Seminar
Grading:
This course is graded on the Graduate Regular scale.
CSS 620: Origins of Social Complexity. 3 credits.
Examines when, where, and how social complexity emerged in human societies, emphasizing long-term analysis and comparative information processing in four civilizations of the ancient world: West Asia, East Asia, Andean Peru, and Mesoamerica. Offered by Computational & Data Sciences. May not be repeated for credit.
Registration Restrictions:

Required Prerequisite: CSS 600B-.
B- Requires minimum grade of B-.

Enrollment limited to students with a class of Advanced to Candidacy, Graduate, Junior Plus, 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
Grading:
This course is graded on the Graduate Regular scale.
CSS 625: Complexity Theory in the Social Sciences. 3 credits.
Examines social phenomena including language, terrorism, the Internet, warfare, and wealth based on power laws and far-from equilibrium nonlinear dynamics. Emphasizes data analysis, and modeling and interpreting complexity-theoretic dynamics. Offered by Computational & Data Sciences. May not be repeated for credit.
Registration Restrictions:

Required Prerequisite: CSS 600B-.
B- Requires minimum grade of B-.

Enrollment limited to students with a class of Advanced to Candidacy, Graduate, Junior Plus, 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: Seminar
Grading:
This course is graded on the Graduate Regular scale.
CSS 630: Comparative Computational Social Science. 3 credits.
Applies comparative method for analyzing different types of computational models in the social sciences. Strong crossdomain and interdisciplinary emphasis akin to comparative economic systems, government, or linguistics. Offered by Computational & Data Sciences. May not be repeated for credit.
Recommended Prerequisite: CSS 600 or permission of instructor. Concurrent enrollment is also permitted.
Registration Restrictions:

Enrollment limited to students with a class of Advanced to Candidacy, Graduate, Junior Plus, 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: Seminar
Grading:
This course is graded on the Graduate Regular scale.
CSS 635: Cognitive Foundations of Computational Social Science. 3 credits.
Examines cognitive foundations and information processing in computational social agents and compares to human cognitive phenomena, including emotions, trust, and reciprocity. Emphasizes modeling project. Offered by Computational & Data Sciences. May not be repeated for credit.
Recommended Prerequisite: CSS 600 and CSS 610 or permission of instructor. Concurrent enrollment is also permitted.
Registration Restrictions:

Enrollment limited to students with a class of Advanced to Candidacy, Graduate, Junior Plus, 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: Seminar
Grading:
This course is graded on the Graduate Regular scale.
CSS 640: Human and Social Evolutionary Complexity. 3 credits.
Examines long-term evolution of human and societal complexity from global and cross-cultural perspective with emphasis on computational aspects leading to today's globalization. Global history from the computational social science perspective. Offered by Computational & Data Sciences. May not be repeated for credit.
Recommended Prerequisite: CSS 600, 620, and permission of instructor.
Registration Restrictions:

Enrollment limited to students with a class of Advanced to Candidacy, Graduate, Junior Plus, 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: Seminar
Grading:
This course is graded on the Graduate Regular scale.
CSS 643: Land-Use Modeling Techniques and Applications. 3 credits.
Survey of literature on spatially explicit empirical models of land-use change. Hands-on experience developing and running simple models. Techniques include statistical models, mathematical programming models, cellular automata, agent-based models, and integrated models. Offered by Computational & Data Sciences. May not be repeated for credit.
Recommended Prerequisite: CSS 600 (may be taken concurrently) or permission of instructor.
Registration Restrictions:

Enrollment limited to students with a class of Advanced to Candidacy, Graduate, Junior Plus, 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: Seminar
Grading:
This course is graded on the Graduate Regular scale.
CSS 645: Spatial Agent-Based Models of Human-Environment Interactions. 3 credits.
Discusses key challenges in spatial modeling of human-environment interactions. Reviews agent-based modeling applications in urban and rural interactions, agriculture, forestry, and other areas. Hand-on development of simple ABM models. Investigates linkages between GIS and ABM. Notes: CSS 600 may be taken concurrently. Offered by Computational & Data Sciences. May not be repeated for credit.
Recommended Prerequisite: GGS 631 or CSS 600 (may be taken concurrently) or permission of instructor.
Registration Restrictions:

Enrollment limited to students with a class of Advanced to Candidacy, Graduate, Junior Plus, 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: Seminar
Grading:
This course is graded on the Graduate Regular scale.
CSS 650: Physics Methods for Analyzing Social Complexity. 3 credits.
Surveys complexity theoretic tools including strange attractors, Ising models, correlation functions, ergodic theory, power spectra, meanfield theory, and renormalization group. Emphasizes application to social, economic, or political systems. Offered by Computational & Data Sciences. May not be repeated for credit.
Recommended Prerequisite: CSS 600 and permission of instructor. Concurrent enrollment is also permitted.
Registration Restrictions:

Enrollment limited to students with a class of Advanced to Candidacy, Graduate, Junior Plus, 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: Seminar
Grading:
This course is graded on the Graduate Regular scale.
CSS 655: Social Systems Dynamics. 3 credits.
Introduces systems dynamics modeling of social systems governed by levels/rates or stocks/flows processes, with applications to global modeling, terrorism, urban dynamics, organizations, and social and international conflict. Offered by Computational & Data Sciences. May not be repeated for credit.
Recommended Prerequisite: CSS 600. Concurrent enrollment is also permitted.
Registration Restrictions:

Enrollment limited to students with a class of Advanced to Candidacy, Graduate, Junior Plus, 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: Seminar
Grading:
This course is graded on the Graduate Regular scale.
CSS 665: Complex Adaptive Systems in Public Policy. 3 credits.
Students learn (i) basic concepts of complex adaptive systems (CAS) and how they can be applied to policy analysis, and (ii) how to use agent-based modeling as a tool for policy analysis. Address modeling issues on representing a system, agent decision making, validation, experiment design and analysis, as well as incorporating empirical data and methods to inform agent-based modeling. Offered by Computational & Data Sciences. May not be repeated for credit.
Registration Restrictions:

Required Prerequisite: CSS 600B-.
B- Requires minimum grade of B-.

Enrollment limited to students with a class of Advanced to Candidacy, Graduate, Junior Plus, 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
Grading:
This course is graded on the Graduate Regular scale.
CSS 671: Natural Language Processing for Complex Systems. 3 credits.
This course focuses on the fundamentals of Natural Language Processing (NLP), Natural Language Understanding (NLU) and Large Language Models (LLMs), comparatively or coupled with other computational methods used in modeling communication, such as agent-based modeling, social network analysis, audio and image processing. It teaches topic modeling, topic2vec, speech recognition, clustering methods, and other relevant methods applied to social science and complex systems. Offered by Computational & Data Sciences. May not be repeated for credit.
Recommended Prerequisite: CSS 610
Registration Restrictions:

Required Prerequisite: CSS 605B-.
B- Requires minimum grade of B-.

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

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

Schedule Type: Lecture
Grading:
This course is graded on the Graduate Regular scale.
CSS 692: Social Network Analysis. 3 credits.
Methods and applications that examine complex social systems based on relations, structures, connectivity, matrix representations, location, roles, interactions, and other network properties. Applications to terrorism, cognition, organizations, and other social phenomena. Offered by Computational & Data Sciences. May not be repeated for credit.
Recommended Prerequisite: CSS 600. Concurrent enrollment is also permitted.
Registration Restrictions:

Enrollment limited to students with a class of Advanced to Candidacy, Graduate, Junior Plus, 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: Seminar
Grading:
This course is graded on the Graduate Regular scale.
CSS 695: Agent-based Computational Economics. 3 credits.
Present lectures on neoclassical economic theory as we investigate how to use agent technology to move beyond neoclassical specifications. Survey the most well-known results in agent-based economics. Read and present papers that are at the research frontier. A semester long research project 1.Will be the focal point of weekly model development (coding), data analysis, and writing. Offered by Computational & Data Sciences. May not be repeated for credit.
Recommended Prerequisite: CSS 610. Undergraduate microeconomics.
Registration Restrictions:

Enrollment limited to students with a class of Advanced to Candidacy, Graduate, Junior Plus, 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
Grading:
This course is graded on the Graduate Regular scale.

700 Level Courses

CSS 710: Advanced Agent-based Modeling and Simulation. 3 credits.
Cover topics related to large-scale agent models including how to 1) make use of available compute resources (CPU and memory) through threading and related code parallelization ideas and technologies; 2) sample data from large-scale models and calibrate/estimate such models, and 3) design experiments for models that are expensive to evaluate. Digress into other topics at the frontier of agent modeling. Offered by Computational & Data Sciences. May not be repeated for credit.
Recommended Prerequisite: CSS 610.
Registration Restrictions:

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

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

Schedule Type: Lecture
Grading:
This course is graded on the Graduate Regular scale.
CSS 717: Verification and Validation of Models. 3 credits.
This course covers verification and validation (V&V) practices across different modeling techniques. Topics include V&V terminology, data visualization for V&V, runtime verification, validation of machine learning, agent-based, cognitive, statistical, and network models as well as ethics of model development and use. Offered by Computational & Data Sciences. May not be repeated for credit.
Recommended Prerequisite: CSS 600, CSS 605, CSS 610, or CSS 692; or the permission of the instructor.
Registration Restrictions:

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

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

Schedule Type: Lecture
Grading:
This course is graded on the Graduate Regular scale.
CSS 739: Topics in Computational Social Science. 3 credits.
Selected topics in computational social science not covered in fixed-content computational social science courses. Offered by Computational & Data Sciences. May be repeated within the degree for a maximum 9 credits.
Specialized Designation: Topic Varies
Recommended Prerequisite: Permission of instructor.
Registration Restrictions:

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

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

Schedule Type: Lecture
Grading:
This course is graded on the Graduate Regular scale.
CSS 796: Directed Reading and Research. 3 credits.
Reading and research on specific topic in computational social science under direction of a faculty member. Offered by Computational & Data Sciences. May be repeated within the degree for a maximum 6 credits.
Recommended Prerequisite: Permission of instructor.
Registration Restrictions:

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

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

Schedule Type: Research
Grading:
This course is graded on the Graduate Regular scale.
CSS 798: Research Project. 3 credits.
Project chosen and completed under guidance of graduate faculty member, resulting in acceptable technical report. Offered by Computational & Data Sciences. May be repeated within the degree for a maximum 6 credits.
Recommended Prerequisite: 12 graduate core requirement credits and permission of instructor.
Registration Restrictions:

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

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

Schedule Type: Thesis
Grading:
This course is graded on the Graduate Special scale.

800 Level Courses

CSS 898: Research Colloquium in Computational Social Science. 1 credit.
Presentations in specific research areas in computational social science by Center for Social Complexity-associated faculty and professional visitors. Notes: Maximum 3 credits of CSS 898 and 899 may be applied toward PhD. Offered by Computational & Data Sciences. May be repeated within the degree for a maximum 3 credits.
Registration Restrictions:

Enrollment is limited to Graduate level students.

Schedule Type: Seminar
Grading:
This course is graded on the Satisfactory/No Credit scale.
CSS 899: Colloquium in Computational Social Science. 1 credit.
Presentations in variety of areas of computational social science by Center for Social Complexity-associated faculty and professional visitors. Notes: Maximum 3 credits of CSS 898 and 899 may be applied toward PhD. Offered by Computational & Data Sciences. May be repeated within the degree for a maximum 2 credits.
Registration Restrictions:

Enrollment is limited to Graduate level students.

Schedule Type: Seminar
Grading:
This course is graded on the Graduate Regular scale.

900 Level Courses

CSS 909: Advanced Topics in Computational Social Science. 3 credits.
Covers selected topics in computational social science and socioinformatics not covered in fixed-content courses. Offered by Computational & Data Sciences. May be repeated within the degree for a maximum 9 credits.
Specialized Designation: Topic Varies
Recommended Prerequisite: Permission of instructor.
Registration Restrictions:

Enrollment is limited to Graduate level students.

Schedule Type: Seminar
Grading:
This course is graded on the Graduate Regular scale.
CSS 996: Doctoral Reading and Research. 1-12 credits.
Reading and research on specific topic in computational social science under direction of faculty member. Offered by Computational & Data Sciences. May be repeated within the degree for a maximum 12 credits.
Recommended Prerequisite: Admission to the doctoral program and permission of instructor.
Registration Restrictions:

Enrollment is limited to Graduate level students.

Schedule Type: Research
Grading:
This course is graded on the Graduate Regular scale.
CSS 998: Doctoral Dissertation Proposal. 1-12 credits.
Covers development of research proposal, which forms basis for doctoral dissertation, under guidance of dissertation director and doctoral committee. Notes: Candidates must complete a combined minimum of 12 credits of doctoral proposal (CSS 998) and doctoral dissertation research (CSS 999), of which at least three credits must be of CSS 999. A combined maximum of 24 credits of CSS 998 and CSS 999 may be applied to the degree. Offered by Computational & Data Sciences. May be repeated within the degree.
Recommended Prerequisite: Permission of advisor.
Registration Restrictions:

Enrollment is limited to Graduate level students.

Schedule Type: Dissertation
Grading:
This course is graded on the Satisfactory/No Credit scale.
CSS 999: Doctoral Dissertation. 1-12 credits.
Doctoral dissertation research under direction of dissertation director. Notes: Candidates must complete a combined minimum of 12 credits of doctoral proposal (CSS 998) and doctoral dissertation research (CSS 999), of which at least three credits must be of CSS 999. A combined maximum of 24 credits of CSS 998 and CSS 999 may be applied to the degree. Offered by Computational & Data Sciences. May be repeated within the degree.
Recommended Prerequisite: Approval of dissertation proposal.
Registration Restrictions:

Enrollment limited to students with a class of Advanced to Candidacy.

Enrollment is limited to Graduate level students.

Schedule Type: Dissertation
Grading:
This course is graded on the Satisfactory/No Credit scale.