(4 crs.) Capabilities and limitations of computers. Applications of computers in today's society. Overview of computing systems and programs. Students will complete several projects using a computer. (Lec. 3, Lab. 2/Online) Not open to students who have credit in any college-level computer science course, or to computer science majors. (B3) (B4)

(4 crs.) Cross-listed as (CSC), MTH 104. Introduces mathematical problem solving and computational thinking through puzzles and games. Students work in small groups on activities to enhance their analytic abilities. Topics include numbers, probability, logic, algorithms, and graphs. (Lec. 4) Pre: High school mathematics. No programming required. (B3)

(4 crs.) The art of problem solving through computer programming. Students explore innovative and cutting edge applications that may include mobile apps, multimedia, computer games, puzzles, robotics, graphics and animation, social networking, physical computing. (Lec. 3, Lab. 1/Online) Pre: Not open to students with credit in CSC courses at 200-level or above. (B3)

(4 crs.) Honors Section of CSC 106, The Joy of Programming. The art of problem solving through computer programming. Students explore innovative and cutting edge applications that may include mobile apps, multimedia, computer games, puzzles, robotics, graphics and animation, social networking, physical computing. (Lec. 3, Lab. 1/Online) Pre: 3.40 overall GPA or higher. Not open to students with credit in CSC courses at 200-level or above. (B3)

(4 crs.) Broad introduction to computer science, with an emphasis on problem solving. Algorithm discovery. Algorithm analysis. Algorithmic solutions to problem in various sub-fields including operating systems, digital forensics, computer graphics, artificial intelligence, and bioinformatics. (Lec. 3, Lab. 2) Pre: C- or better in CSC 106 or approval of instructor.

(4 crs.) Explore how technology can be a tool for both shrinking the equity gap and reinforcing oppression, depending on the context and who wields the greatest influence. (Lec. 3, Rec. 1) (A2) (C3) (GC)

(1-4 crs.) Introductory topics of current interest in computing. This course may be repeated under different topics. (Lec., Project) Pre: permission of instructor.

(4 crs.) An integrated symbolic, numerical, and graphical approach to computer problem solving. Structured design; fundamental programming techniques. Computer algebra systems. Scientific, engineering, and mathematical applications. (Lec. 3, Lab. 2/Online) Pre: credit or concurrent enrollment in MTH 131 or 141. Not for major credit in computer science. May not be taken for credit by students with credit in CSC 201 or 211.

(4 crs.) Transform data into insight using data science techniques including obtaining, analyzing, synthesizing, visualizing and presenting significant trends as well as computer characteristics, algorithms, data representation and program development. (Lec.3, Lab. 2/Online) (B3)

(3 crs.) Cross-listed as (OCE 213), CSC 203. Computer programming, with an emphasis on ocean engineering problems; developing codes in MATLAB /Python, covering standard topics including algorithms, procedural programming, OOP, conditional statements, Inputs/Outputs, Monte-Carlo methods, and optimization problems. (Lec 3.) Pre: MTH 243 ) or permission of instructor

(4 crs.) Android and/or Apple app programming. User interfaces, app algorithms, device interaction, app marketing. Students create fully functional apps. (Online) Pre: CSC106, or CSC201, or CSC211, or previous programming experience through permission of instructor.

(4 crs.) Problem specification, solution design, and algorithm development. Topics may include data types, functions, iteration, recursion, object-oriented programming, built-in data structures, file operations, numerical and string-based operations. (Lec. 3, Lab. 2) Pre: (C- or better in CSC 110) or (B or better in CSC 106 or in CSC 201 or in CSC 200) or ELE 208 or permission of instructor.

(4 crs.) Problem specification, solution design, and algorithm development. Topics may include data types, functions, iteration, recursion, object-oriented programming, built-in data structures, file operations, numerical and string-based operations. (Lec. 3, Lab. 2) Pre: (C- or better in CSC 110) or (B or better in (CSC 106 or CSC 201 or CSC 200) and concurrent enrollment in CSC 110) or ELE 208 or permission of instructor.

(4 crs.) Abstract data types and data structures. Pointers, linked lists, stacks, queues, binary trees, and tables. Fundamentals of software engineering. Development of object-oriented programming techniques. (Lec. 3, Lab. 2/Online) Pre: C- or better in CSC 211; and MTH 180 or Computer Engineering major.

(1 cr.) Cross-listed as (CSC), DSP 220. Public health and recovery from global health crisis like COVID-19 depends on collection and analysis of accurate data. Publicly available health crisis datasets provide an opportunity to introduce students to data science through data exploration, while gaining a better understanding of the global crisis. The course focuses on interactive ways to introduce data exploration through 1-hour weekly working sessions and talks by data scientists who are working on these health crisis data. (Online)

(3 crs.) Teaching computer science (CS) in grades K-12. CS content, pedagogy, assessment. This course leads to Rhode Island Computer Science Educators Endorsement. No prior CS background required. (Online)

(4 crs.) Cross-listed as (CSC), COM 271. Learn to communicate effectively using principles and technologies of client-side web design and programming. Explores HTML, CSS, and JavaScript; current and evolving web capabilities accessibility and usability; and workflow tools. (Lec. 2, Lab. 4)

(4 crs.) Cross-listed as (CSC), COM271. Learn to communicate effectively using principles and technologies of client-side web design and programming. Explores HTML, CSS, and JavaScript; current and evolving web capabilities accessibility and usability; and workflow tools. (Lec. 2, Lab. 4/Online) Pre: CSC 106

(1-4 crs.) Topics of current interest in computing. This course may be repeated under different topics. (Lec., Project) Pre: permission of instructor.

(4 crs.) Organization of programming languages, data and control structures, syntax and semantics, compilers and interpreters. Block structured languages, recursion, parameter passing, run-time storage management. Procedural, functional, object-oriented, and logical languages. (Lec. 3, Lab. 2/Online) Pre: CSC 212.

(4 crs.) Programming environments and methodologies for the design, development, testing, and maintenance of large software systems. Student teams will develop a substantial software product from requirements to delivery using disciplined techniques. (Lec. 3, Project 3) Pre: CSC 212. (D1)

(4 crs.) Programming environments and methodologies for the design, development, testing, and maintenance of large software systems. Student teams will develop a substantial software product from requirements to delivery using disciplined techniques. (Lec. 3, Project 2/Online) Pre: CSC 212. (D1)

(4 crs.) Cross-listed as (CSC), DSP 310. Data driven programming; data sets, file formats and meta-data; descriptive statistics, data visualization, and foundations of predictive data modeling; accessing web data and data bases; distributed data management. (Lec. 3, Lab. 2) Pre: CSC201 or CSC211 or equivalent, or permission of instructor. Computer Science majors must take as CSC 310; Data Science majors must take as DSP 310.

(4 crs.) Impacts of historical development of computing on the developer practices. Tools used in programming and computational problem solving. How computers work from high level languages to hardware and machine representation. (Lec. 3, Lab. 1) Pre: CSC 110 and 211. Not for students with credit in CSC 411 or 412.

(4 crs.) Discussion of the social and ethical issues created by the use of computers. The problems that computers solve and those that they produce. Ethics and responsibilities of computer and data professionals. (Lec. 4) Pre: CSC 110 or must be in a degree-granting college

(4 crs.) Discussion of the social and ethical issues created by the use of computers. The problems that computers solve and those that they produce. Ethics and responsibilities of computer and data professionals. (Lec. 4) Pre: CSC 110 or LTI/DSP 220 or must be in a degree-granting college

(4 crs.) Combinatorial problem-solving for computer science. Set theory and logic, proofs by induction and contradiction, elementary probability; arrangements, selections, distributions, binomials, inclusion-exclusion; recurrence relations and their solution; graph theory, trees, networks. (Lec. 4) Pre: (MTH 180 or MTH 141) and CSC 212, and student must be admitted to a degree-granting college. Student may not receive credit for this course and CSC 447.

(4 crs.) Cross-listed as (CSC), COM372. Web-based information technology for communication and delivery of dynamically generated content. Technology will include current practice and tools for server-side programming, client-side programming, third-party services, data storage, and security concerns. (Lec. 2, Lab. 4) Pre: CSC271/COM271 and junior standing in a degree-granting college or permission of instructor.

(1-4 crs.) Intermediate-level topics of current interest in computing. This course may be repeated under different topics. (Lec., Project) Pre: permission of instructor.

(4 crs.) Teams of students will integrate the knowledge acquired in previous programming courses and apply it to implement a real-world software project in consultation with local industry. Students will communicate effectively both within their teams and with project stakeholders, creating written reports, technical documentation, and giving oral presentations. (Lec. 3, Lab. 2) Pre: CSC 211 or 372. May not be taken for credit by students with credit in CSC 305. NOT intended for students majoring in computer science. (B2) (D1) S/U only.

(4 crs.) Grammars and languages; lexical analysis and parsers; interpreters, translators, and virtual machines; symbol tables and type systems; code generation for real and virtual machines. Students will implement a number of interpreters, translators, and virtual machines for various small languages. (Lec. 3, Project 3) Pre: CSC 301, and student must be admitted to a degree-granting college.

(4 crs.) Interactive raster graphics; hardware, software, and algorithms. Point plotting, line drawing, geometrical transformations, clipping and windowing. Three-dimensional graphics including curves, surfaces, perspective, hidden objects, shading. User interfaces; graphical programming environments. (Lec. 3, Project 3) Pre: CSC 212 and either MTH 215 or MTH 362, and student must be admitted to a degree-granting college.

(4 crs.) Logical structure of computer systems viewed as a hierarchy of levels. Assembly language programming, assemblers, linkers, loaders. Computer architecture including digital logic, processor organization, instruction sets, addressing techniques, virtual memory, microprogramming. (Lec. 3, Project 3) Pre: CSC 212 and student must be admitted to a degree-granting college.

(4 crs.) General concepts underlying operating systems and computer networks. Topics include process management, concurrency, scheduling, memory management, information management, protection and security, modeling and performance, networking and communication. (Lec. 3, Project 3/Online) Pre: CSC 212 and student must be admitted to a degree-granting college.

(4 crs.) Programming techniques to engage a collection of autonomous processors to solve large-scale numerical and non-numerical problems. Processor interconnections. Parallel programming languages and models. Performance measures. (Lec. 3, Project 3) Pre: CSC 411 or ELE 305, and student must be admitted to a degree-granting college. In alternate years.

(3 crs.) Cross-listed as (ELE 437), CSC 417. Computer networks, layering standards, communication fundamentals, error detection and recovery, queuing theory, delay versus throughput trade-offs in networks, multiple-access channels, design issues in wide and local area networks. (Lec. 3) Pre: ((ELE 205 or 208 or CSC 211), and (ELE 436 or MTH 451 or ISE 311 (411))), or permission of instructor.

(4 crs.) Cross-listed as (ELE 438), CSC 418. Elementary cryptography, public key, private key, symmetric key, authentication protocols, firewalls, virtual private networks, transport layer security, and wireless network security. (Lec. 3, Project 3) Pre: ELE 208 or MTH 362 or MTH 451 or ISE 311 (411) or junior or senior standing in computer engineering or computer science or permission of instructor.

(4 crs.) Construction and management of large data systems. Data modeling, relational and object-oriented systems, main memory databases, query languages, query optimization, concurrency control, transaction management, distributed systems, disk organization, indexes, and emerging technologies. (Lec. 3, Project 3/Online) Pre: CSC 212, and student must be admitted to a degree-granting college.

(4 crs.) Algorithm design and analysis, advanced data structures, computational complexity. Sorting, searching including hashing and balanced trees, string pattern matching, polynomial and matrix calculations, graph and network algorithms, NP-completeness and intractability. (Lec. 3, Project 3) Pre: CSC 212 and (CSC 340 or MTH/CSC 447) and student must be admitted to a degree-granting college.

(4 crs.) Abstract models of computational systems. Classical models for uniprocessor, sequential, and stored program computers. New models based on recent advances in hardware, software, and communications and their implications in practice. (Lec. 3, Project 1) Pre: CSC 340 or CSC/MTH 447 and student must be admitted to a degree-granting college. In alternate years.

(3 crs.) Cross-listed as (MTH), CSC 447. Concepts and techniques in discrete mathematics. Finite and infinite sets, graphs, techniques of counting, Boolean algebra and applied logic, recursion equations. (Lec. 3) Pre: junior standing or better in physical or mathematical sciences, or in engineering, or permission of instructor.

(4 crs.) Symbolic, numerical, and graphical approaches to mathematical computation. Pitfalls in numerical computation. Root finding. Numerical integration and differentiation. Approximation of functions. Interpolation and curve fitting. Linear systems. Ordinary differential equations. Multidimensional numerical optimization. (Lec. 3, Lab. 2) Not for graduate credit. Pre: CSC 212 and MTH 215 and 243.

(3 crs.) Cross-listed with (PHL), CSC 451. Selected topics in modern symbolic logic including calculus of propositions, predicate calculus, and modal logics. Philosophical and mathematical aspects of the subject. (Lec. 3) Pre: Any one of PHL 101, CSC 340, CSC/MTH 447, or MTH 180, or permission of instructor.

(4 crs.) Cross-listed as (CSC), DSP 461. Broad introduction to fundamental concepts in machine learning. Survey of traditional and newly developed learning algorithms, as well as, their application to real-world problems. (Lec. 3, Lab. 1) Pre: CSC 310 and MTH 215. Computer Science majors must take as CSC 461. Data Science majors must take as DSP 461.

(4 crs.) Cross-listed as (CSF), CSC 462. This class will present the basic topics in computer security and their relation to secure programming. Security models, threats, design principles and secure coding practices will be discussed. We will also look at programming language features and semantics to evaluate whether they help or hurt the ability to write secure programs. (Lec. 3, Lab. 1) Pre: CSC 305.

(4 crs.) Supervised internship in computer science that prepares students for careers in industry. (Practicum) Pre: Advanced standing in computer science and departmental approval. May be repeated for a maximum of 8 credits.

(4 crs.) Theories, formalisms, techniques to emulate intelligent behavior using information processing models. Symbolic programming, search, problem solving, knowledge-based techniques, logic, and theorem proving. Optional topics: natural language processing, machine learning, and computer vision. (Lec. 3, Project 1) Pre: CSC 212 and student must be admitted to a degree-granting college.

(1-4 crs.) Advanced work in computer science. Conducted as supervised individual projects. (Independent Study) Pre: permission of instructor. S/U credit.

(1-4 crs.) Advanced topics of current interest in computer science. (Lec.1-4, Project 1-3) Pre: permission of instructor.

(1-4 crs.) Advanced topics of current interest in computer science where course involves substantial programming projects. May be used to fulfill major programming elective requirement. (Lec., Lab.) Pre: permission of instructor.

(4 crs.) Supervised work on a capstone project in computer science that prepares students for careers in industry and graduate study. (Practicum) Pre: advanced standing in computer science and departmental approval. May be repeated for a maximum of 8 credits. Not for graduate credit. S/U credit.

(4 crs.) Design, analysis, implementation, and comparative study of major programming language families. Topics include procedural and block-structured languages, interpretive languages, concurrency, functional languages, object-oriented programming, logic programming, dataflow languages and machines. (Lec. 3, Project 3) Pre: CSC 301.

(4 crs.) An advanced course in compiler construction covering advanced parsing techniques, compiler-writing tools, type checking and type inference, code optimization, and compiling nonstandard language features. (Lec. 3, Project 3) Pre: CSC 402. In alternate years.

(4 crs.) An advanced course in compiler construction covering advanced parsing techniques, compiler-writing tools, type checking and type inference, code optimization, and compiling nonstandard language features. (Lec. 3, Project 3) Pre: CSC 402. In alternate years.

(4 crs.) Lifecycle models; software development environments; project management. Metrics, performance, and testing. Paradigms for software design and architecture. Legal and ethical issues. (Lec. 3, Project 3) Pre: CSC 305. In alternate years.

(4 crs.) Object-oriented design and programming, the software engineering process. Traditional and current object-oriented design methods. Software reuse. Design tools. Impact of the technology on traditional software engineering. (Lec. 3, Project 3) Pre: CSC 305 and working knowledge of an object-oriented language. In alternate years.

(4 crs.) Evaluation of high-performance computer systems with respect to architectures, operating systems, and algorithms. High-speed conventional machines; array processors; multiprocessors; data flow machines; RISC architectures; VLSI-based machines. (Lec. 3, Project 3) Pre: CSC 411. In alternate years.

(4 crs.) Advanced topics in distributed systems. Networking; standard distributed computing environments. Distributed computing algorithms. Concurrency and threading. Real-time computing, scheduling, concurrency control, load allocation. (Lec. 3, Project 3) Pre: CSC 412. In alternate years.

(4 crs.) Cross-listed as (ELE 543), CSC 519.Computer network architectures, data link control and access protocols for LANs, internet protocols and applications, software and hardware issues in computer communication, delay analysis, and current research in computer networking. (Lec. 4) Pre: ELE 437 or equivalent or CSC 412 or equivalent.

(3-4 crs.) Cross-listed as (CSC), STA, CMB 522, BPS 542. Integrates computing, statistical, and biological sciences, algorithms, and data analysis/management. Multidisciplinary student research teams. Modeling dynamic biological processes. Extra project work for 4 credits. (Lec. 3, Project 3) Pre: major in a computing, statistical, or biological science or permission of instructor.

(3 crs.) Cross-listed as (ISE), CSC 525, ELE 515. Simulation of random processes and systems. Continuous and discrete simulation models. Data structures and algorithms for simulation. Generation of random variates, design of simulation experiments for optimization and validation of models and results. Selected engineering applications. (Lec. 3) Pre: CSC 212 or ISE 325, ISE 333 (433) or ELE 509, or permission of instructor.

(4 crs.) Current research and developments in database management systems. Relational, semantic, object-oriented, real-time, distributed, heterogeneous, and logic databases. Concurrency control, security, active rules, recovery, and integrity subsystems. (Lec. 3, Project 3) Pre: CSC 436 or permission of instructor. In alternate years.

(4 crs.) Algorithm design techniques such as dynamic programming, greedy method, branch and bound. Linear programming; NP-completeness; graph algorithms; number theoretic algorithms; approximation algorithms for NP-complete problems; probabilistic and parallel algorithms. (Lec. 3, Project 3) Pre: CSC 440 or 445. In alternate years.

(4 crs.) Mathematical techniques for the analysis of algorithms. Sums and products; finite difference calculus; properties of binomial coefficients; Stirling, harmonic, and Fibonacci numbers; recurrence relations; generating functions; asymptotic approximation. Case studies. (Lec. 3, Project 3) Pre: CSC 440. In alternate years.

(4 crs.) Finite automata, pushdown automata, formal grammars and Chomsky hierarchy, Turing machines, computability, basics of complexity theory. Advanced topics including some of the following: cryptography, interactive proofs, circuit complexity, completeness for various complexity classes, relations among complexity classes, new models of computation. (Lec. 3, Project 3) Pre: CSC 440 or 445. In alternate years.

(4 crs.) Cross-listed as (CSC) AMS546. Explore algorithms for data that outpaces computing. Mathematically rigorous models for designing efficient algorithms for large data sets. Sketching and streaming, locality-sensitive hashing, entropy-scaling, manifold learning, and others. (Seminar, Project) Pre: CSC 440 or by permission of instructor.

(3 crs.) Cross-listed as (MTH), CSC 547. Enumeration: generation functions, recurrence relations, classical counting numbers, inclusion-exclusion, finite set systems and designs. Polya theory, coding theory, and Ramsey theory. Finite fields and algebraic methods. (Lec. 3) Pre: MTH 316. Offered alternate fall semesters.

(3 crs.) Cross-listed as (MTH), CSC 548. Basic concepts and techniques of graph theory as well as some of their applications. Topics include: connectivity, matchings, colorings, extremal problems, Ramsey theory, planar graphs, algebraic techniques. (Lec. 3) Pre: MTH 316.

(4 crs.) Symbolic mathematical computation; history, use, representation of information, algorithms and heuristics. Big number arithmetic, manipulation of polynomials and rational expressions; algebraic simplification; factoring; symbolic integration. Organization and implementation of computer algebra systems. (Lec. 3, Project 3) Pre: CSC 440. In alternate years.

(4 crs.) Survey of traditional and newly developed deep learning methods, including convolutional networks, recurrent networks, auto-encoders, and generative adversarial networks, as well as, their application to real-world problems. (Lec. 3, Lab. 1) Pre: CSC 461 or permission by instructor.

(3 crs.) Cross-listed as (CSC), ELE 581. Topics of specialized or current interest, which may change. Topics may include expert systems, natural language processing, neural network models, machine learning. AI applications in remote sensing. (Lec. 3) Pre: CSC 481 or permission of instructor. May be repeated with permission. In alternate years.

(3 crs.) Cross-listed as (ELE), CSC 583.Algorithms used to extract information from two-dimensional images. Picture functions. Template matching. Region analysis. Contour following. Line and shape descriptsions. Perspective transformations. Three-dimensional reconstruction. Image sensors. Interfactin. applications. (Lec. 3) Pre: MTH 362 or equivalent.

(4 crs.) Cross-listed as (STA), CSC 585. Foundation of the statistical analysis of network data: visualization, node and edge characterization, inference, and sampling, mathematical and statistical network modeling and inference, modeling of static and dynamic network processes. (Lec. 3, Rec. 1) Pre: MTH 215; STA 411, or STA 412, or STA 441; or permission of instructor.

(1-4 crs.) Advanced work in computer science conducted as supervised individual projects. (Independent Study) Pre: permission of instructor. S/U credit.

(1-4 crs.) Advanced topics of current interest in computer science. (Lec. 1-4, Project 1-3) Pre: permission of instructor. May be taken more than once.

(3 crs.) Scientific programming. Algorithmic thinking. Scripting, language comparisons, code design, programming resources and communities. Not for graduate or undergraduate credit in Computer Science. Not for graduate or undergraduate computer science majors. (Lec. 3) Pre: Permission of instructor.

(1-8 crs.) Number of credits is determined each semester in consultation with the major professor or program committee. (Independent Study) S/U credit.

(1-12 crs.) Number of credits is determined each semester in consultation with the major professor or program committee. (Independent Study) S/U credit.

(1-12 crs.) Cross-listed as (CSC) STA 699. Number of credits is determined each semester in consultation with the major professor or program committee. (Independent Study) S/U credit.

(3 crs.) Introductory statistics exploring and understanding data, relationships between variables, randomness and probability. (Lec. 2, Rec. 1) (B3)

(4 crs.) Introduction to statistical computing using R. This course will have two components. In the first part of the course you will learn how to write efficient and transparent programs in R. In the second part of the course, you will learn about packages and functions that are used for statistical analyses, techniques for managing data, and using graphs to visualize data. (Lec. 3, Lab. 1) Pre: (MTH 103 or MTH 111 or MTH 131 or MTH 141) and (STA 220 or STA 307 or STA 308 or STA 409) or permission of instructor.

(4 crs.) Statistical methods applicable to health sciences. Data presentation. Vital statistics and life tables. Fitting models to health data. Testing, estimation, analysis of cross-classifications, regression, correlation. (Lec. 3, Rec. 1) Pre: MTH 107 or 108 or 131 or 141 or permission. Not open to students with credit in 308 or 409.

(4 crs.) Descriptive statistics, presentation of data, averages, measures of variation. Elementary probability, binomial and normal distributions. Sampling distributions. Statistical inference, estimation, confidence intervals, testing hypotheses, linear regression, and correlation. (Lec. 3, Rec. 1) Pre: MTH 107 or 110 or 111 or 131 or 141 or BAI (BUS) 111 or permission of instructor. Not open to students with credit in STA 307 or 409.

(4 crs.) Descriptive statistics, presentation of data, averages, measures of variation. Elementary probability, binomial and normal distributions. Sampling distributions. Statistical inference, estimation, confidence intervals, testing hypotheses, linear regression, and correlation. (Lec. 3, Rec. 1/Online) Pre: Pre: MTH 103, 107, 110, 111, 131, 141, 180, or BAI (BUS) 111, or by permission of instructor. Not open to students with credit in STA 307 or 409.

(4 crs.) Upper-level undergraduate course in missing data analysis. Covered topics will include missing data methods in experiments, deletion methods, single imputation methods, and multiple imputations. (Lec. 3, Rec. 1) Pre: STA 307, or STA 308, or STA 409, or permission of the instructor.

(4 crs.) Upper-level undergraduate course in missing data analysis. Covered topics will include missing data methods in experiments, deletion methods, single imputation methods, and multiple imputations. (Lec. 3, Lab. 1) Pre: STA 307 or STA308, and STA 411 or STA 412, or by permission of the instructor. Not for graduate credit.

(3 crs.) Same as STA 308, but is for students who have better mathematical preparation. (Lec. 3) Pre: MTH 131 or 141. Not open to students with credit in STA 307 or 308.

(4 crs.) Cross-listed as (STA), PHP, BPS 411. An overview of statistical methods with applications to health-related studies. Chi-square tests, effect measures, analysis of variances, multiple comparison procedures, linear and logistic regression, some nonparametric and survival tests. (Lec. 3, Rec. 1) Pre: STA 307, or 308, or 409, or permission of instructor.

(4 crs.) Analysis of variance (one and two ways) and multiple comparison methods. Simple and multiple linear regression, correlation analysis, and model selection methods. (Lec. 3, Rec. 1) Pre: STA 307 or 308 or 409.

(3 crs.) Cross-listed as (PHP), STA 414. This course introduces classical psychometric theories and helps students understand methods to measure important health outcomes of medication use, including clinical, humanistic, and economic outcomes. (Lec. 3) Pre: PHP 405, STA 411 or equivalent; graduate student standing or permission of the instructor.

(4 crs.) Cross-list as (STA), DSP 441. Multivariate data organization and visualization, multinomial and multivariate normal distribution, tests of hypotheses on mean vectors, multivariate regression and classification, principal component analysis, clustering, cross-validation and bootstrapping. (Lec. 3., Lab. 1) Pre: MTH 215; and STA 409, or STA 411, or STA 412; or permission of instructor.

(4 crs.) Modeling, estimation, inference, and forecasting methods are illustrated with applications from different fields. (Lec. 3, Lab. 1) Pre: STA 307 or STA 308, or equivalent, or permission of instructor. Not for graduate credit.

(4 crs.) Cross-listed as (STA), DSP 490. Practical experience in statistical consulting through various projects. Apply statistical methods to the challenges imposed by real data, and communicate findings effectively. (Lec. 2, Practicum 2) Pre: (STA 411 or 412) and STA 441, or permission of the instructor. Not for graduate credit.

(1-3 crs.) Advanced work in statistics. Conducted as supervised individual projects. (Independent Study) Pre: permission of chairperson. S/U credit.

(3 crs.) Advanced topics of current interest in statistics. (Lec. 3) Pre: permission of chairperson.

(4 crs.) Designed as a graduate course in missing data theory. Covered topics include Full Information Maximum Likelihood, Expectation-Maximization algorithm, Multiple Imputation, and nonignorable missing data models. (Lec 3, Rec. 1) Pre: STA 501, or STA 502, or STA 576, or permission of instructor.

(3 crs.) Analysis of variance and covariance, experimental design models, factorial experiments, random and mixed models, estimation of variance components, unbalanced data. (Lec. 3) Pre: STA 412.

(3 crs.) Topics in regression analysis including subset selection, biased estimation, ridge regression, and nonlinear estimation. (Lec. 3) Pre: STA 412.

(3 crs.) Cross-listed as (ISE), STA 513. Topics in statistical quality control systems. Single, multiple, and sequential sampling. Design and analysis of a wide variety of statistical control systems used in conjunction with discrete and continuous data, for several kinds of data emission. (Lec. 3) Pre: ISE 311 (411) or equivalent.

(3 crs.) Analysis of point patterns: visualizing, exploring, and modeling, space time clustering, correcting for spatial variation, clustering around a specific point source. Analysis of spatially continuous data: variogram analysis and Kriging methods. (Lec. 3) Pre: STA 412 or permission of instructor.

(3 crs.) Simple random sampling; properties of estimates, confidence limits. Sample size. Stratified random sampling; optimum allocation, effects of errors, and quota sampling. Regression and ratio estimates; systematic and multistage sampling. (Lec. 3) Pre: STA 308 or 409.

(3-4 crs.) Cross-listed as (CSC), STA, CMB 522, BPS 542. Integrates computing, statistical, and biological sciences, algorithms, and data analysis/management. Multidisciplinary student research teams. Modeling dynamic biological processes. Extra project work for 4 credits. (Lec. 3, Project 3) Pre: major in a computing, statistical, or biological science or permission of instructor.

(4 crs.) Data managing and programming in SAS: data input, formatting and labeling, conditional processing, iterative processing, numeric and character functions, customized reports, data visualization, and basic statistical analysis. (Lec. 3, Rec. 1) Pre: STA 307 or STA 308 or STA 409 or permission from instructor.

(3 crs.) Cross-listed as (STA), PSY, AFS 532. Application of statistical methods to biological and psychological research and experimentation. Experimental situations for which various ANOVA and ANCOVA designs are most suitable. (Lec. 3) Pre: STA 409 or equivalent.

(3 crs.) Bioavailability, dose response models, crossover and parallel designs, group sequential designs, survival analysis, meta analysis. (Lec. 3) Pre: STA 409, 411, or 412 or permission of instructor.

(3 crs.) Longitudinal Data, Linear Mixed Effects Models, Repeated Measures ANOVA, Generalized Linear Models for Correlated Data. (Lec. 3) Pre: STA 411 or STA 412 or permission of instructor.

(3 crs.) Review of matrix analysis. Multivariate normal distribution. Tests of hypotheses on means, Hotelling's T2, discriminate functions. Multivariate regression analysis. Canonical correlations. Principal components. Factor analysis. (Lec. 3) Pre: STA 412.

(3 crs.) Analysis of multidimensional categorical data by use of log-linear and logit models. Discussion of methods to estimate and select models followed by examples from several areas. (Lec. 3) Pre: STA 412.

(3 crs.) Introduces Bayesian methods for a variety of statistical problems. Topics include Bayesian inference, model selection, Bayesian computation, hierarchical models and Gibbs sampling. Open-source software will be utilized for Bayesian data analyses. (Lec. 3) Pre: STA 411 or STA 412 or permission of instructor.

(3 crs.) Application of statistical methodology to the following topics: population growth, interactions of populations, sampling and modeling of ecological populations, spatial patterns, species abundance relations, and ecological diversity and measurement. (Lec. 3) Pre: STA 409 or permission of instructor.

(4 crs.) Designed as a graduate course in modern time series analysis. Modeling, estimation, inference, and forecasting methods are illustrated with applications from different fields. (Lec., Lab.) Pre: STA 409 or equivalent, or permission from instructor.

(3 crs.) Cross-listed as (PHP), STA 575. Using a potential outcomes framework, this course will present methodologies for drawing causal inference in a variety of settings. Examples will be drawn from epidemiologic and medical studies. (Lec. 3/Online) Pre: STA 411 or 412 or permission of instructor.

(3 crs.) Cross-listed as (ELE), STA 584. Random variables, vectors, transformations, hypothesis testing, and errors. Classifier design: linear, nonparametric, approximation procedures. Feature selection and extraction: dimensionality reduction, linear and nonlinear mappings, clustering, and unsupervised classification. (Lec. 3) Pre: ELE 509 or introductory probability and statistics, and knowledge of computer programming.

(4 crs.) Cross-listed as (STA), CSC 585. Foundation of the statistical analysis of network data: visualization, node and edge characterization, inference, and sampling, mathematical and statistical network modeling and inference, modeling of static and dynamic network processes. (Lec. 3, Rec. 1) Pre: MTH 215; STA 411, or STA 412, or STA 441; or permission of instructor.

(1-3 crs.) Advanced work in experimental statistics conducted as supervised individual projects. (Independent Study) Pre: permission of chairperson. S/U credit.

(3 crs.) Advanced topics of current interest in statistics. (Lec. 3) Pre: permission of chairperson. May be taken more than once.

(1 cr.) Cross-listed as (STA) CSC595. Talks by experts in Computer Science and Statistics, providing insights into cutting-edge research and practical applications. Attendance and reflective writing are mandatory. (Seminar) Pre: Enrolled in the Computer and Statistical Sciences PhD program. S/U Only.

(1-6 crs.) Number of credits is determined each semester in consultation with the major professor or program committee. (Independent Study) S/U credit.

(3 crs.) Cross-listed as (PSY), STA 610. Multivariate procedures designed to reduce the dimensionality and help in the interpretation of complex data sets. Methods include principal components analysis, common factor analysis, and image analysis. Related methods: cluster analysis and multidimensional scaling. Applications involve the use of existing computer programs. (Lec. 3) Pre: PSY 533 or STA 541 or equivalent. In alternate years.

(3 crs.) Cross-listed as (PSY), STA 612. Theory and methodology of path analysis with latent variables. Discussion of 'causation' and correlation, Confirmatory Factor Analysis, Measurement and Structural Equation models. Practical applications using current computer programs (e.g. EQS). (Lec. 3) Pre: PSY 533 or 610.

(1-12 crs.) Cross-listed as (CSC) STA 699. Number of credits is determined each semester in consultation with the major professor or program committee. (Independent Study) S/U credit.

(4 crs.) Provides an overview of the technology, threats, and social impact of cybersecurity. (Lec. 3, Lab 1/Online). (C2) (B3) (GC)

(4 crs.) Overview of technical background required for cyber security. Including: binary/hex number systems, operating systems concepts and installation, Python, file systems, OSI model, network topologies and protocols. (Online) Pre: credit or concurrent enrollment in CSC 201 or CSC 110 or permission of the Instructor.

(4 crs.) The science, technology, procedures, and law of acquiring and analyzing digital evidence from computers and devices. (Online 4) Pre: C- or better in CSF 202 or permission of instructor.

(4 crs.) Selected focused topics on acquiring and analyzing evidence from digital devices. Details on analysis of specific operating system artifacts. (Online) Pre: CSF 410. Not for graduate credit.

(4 crs.) Fundamental concepts to understand threats to security; various defenses against those threats. Planning for security; technology used to defend computer systems; implementing security measures and technology. (Online 4) Pre: C- or better in CSF 202 or permission of instructor.

(4 crs.) This course provides an overview of network and systems security. It provides the underlying theory of computer security. It further introduces hands-on skills and techniques that are essential to effectively secure the networks and systems of large and small organizations. (Online 4) Pre: C- or better in CSF 202 or permission of instructor.

(4 crs.) This course provides an overview of network and systems security. It provides the underlying theory of computer security. It further introduces hands-on skills and techniques that are essential to effectively secure the networks and systems of large and small organizations. (Online) Pre: Minimum cumulative GPA of 2.7 and B or better in CSF 202, or by permission of instructor.

(4 crs.) Advanced security topics including intrusion detection, penetration testing, incident response, malware analysis, and risk management. (Online) Pre: CSF 432.

(4 crs.) Cross-listed as (CSF), CSC 438. Provides an overview of techniques used in assessing the security of networks and identifying vulnerabilities. Topics include network traffic analysis, session hijacking, social engineering, application exploitation, rootkits, network sniffers as well as developing threats. (Online) Pre: CSF 432. Not for graduate credit.

(4 crs.) Cross-listed as (CSF), CSC 462. This class will present the basic topics in computer security and their relation to secure programming. Security models, threats, design principles and secure coding practices will be discussed. We will also look at programming language features and semantics to evaluate whether they help or hurt the ability to write secure programs. (Lec. 3, Lab. 1) Pre: CSC 305.

(2 crs.) Cybersecurity career preparation. (Seminar) Pre: permission of the instructor. May be repeated for a maximum of 8 credits. S/U credit.

(4 crs.) New and emerging techniques for identifying, acquiring, and analyzing new and emerging sources of digital evidence. Current research in Digital Forensics. (Online 4) Pre: CSF 410.

(4 crs.) The structure and implementation of computing device file systems. Forensic analysis and reconstruction of digital evidence found in modern file systems. (Online 4) Pre: CSF 410.

(4 crs.) Presents advanced techniques and research for incident response and live forensics. Topics may include live forensics in cloud environments, visualization of security incidents, and live forensics in the smart grid. (Online) Pre: CSF 432 or CSF 410.

(4 crs.) Advanced topics in network security including intrusion detection, penetration testing, incident response, malware analysis, and risk management. Students will learn relevant skills and research emerging solutions to these problems. (Online 4) Pre: CSF 432.

(4 crs.) Presents advanced techniques and research on intrusion detection and network defense. Topics may include network traffic analysis, intrusion analysis, machine learning techniques for intrusion detection, data mining for intrusion detection, advanced persistent threats. (Online 4) Pre: CSF 432.

(4 crs.) Advanced techniques used in assessing the security of networks and identifying vulnerabilities. Network traffic analysis; session hijacking; social engineering; application exploitation; rootkits; network sniffers; developing threats. (Online 4) Pre: CSF 432

(4 crs.) Introduces core concepts and terminology associated with the practice of malware analysis. Provides a fundamental understanding of basic and advanced static and dynamic malware analysis. Additional topics will include: data encoding, packers, and other forms of anti-malware analysis techniques. (Online) Pre: CSF 432.

(4 crs.) Introduction to cyber threat intelligence and how it is applied across public and private sector organizations. Topics include stages of intelligence life cycle, cyber security frameworks, tradecraft skills. (Online) Pre: CSF 534.

(4 crs.) This course provides each student with a framework for understanding organizational behavior in the context of organizational decision making and leadership in a cyber security work environment. It examines the theory, research, and practice of organizational behavior in work settings, focusing on individual differences, communications, group dynamics, motivation, and leadership. Through course discussion, analytical writing, and exercises, students will learn to apply professional skills in a technical working environment to promote both individual and organizational success. (Online) Pre: CSF 430.

(4 crs.) This course provides each student with a professional experience working on an internship, applying technical and professional cyber security skills. (Online) Pre: CSF 580 or permission of instructor. S/U only.

(1-4 crs.) Advanced work in cyber security conducted as supervised individual projects. (Independent Study) Pre: permission of instructor. S/U only.

(1 cr.) Practical introduction to working with data for all majors. Working with datasets in Excel is a skill that is relevant to all majors. Data will be chosen from a variety of domains of interest. (Lec. 1) S/U only.

(1 cr.) Practical introduction to working with data using Power BI for data visualization. Learn practical skills for all majors. Data will be chosen from a variety of domains of interest. (Lec. 1) S/U only.

(1 cr.) Practical introduction to working with data for all majors. Programming in R is a skill that is relevant for many majors. Data will be chosen from a variety of subject areas of interest. (Lec. 1) S/U only.

(3 crs.) Cross-listed as (LTI), DSP 110. Learn to formulate a data-oriented research question, conduct exploratory data analysis using the R programming language, and communicate the results using a well-organized and reproducible workflow. (Lec. 3) (B3)

(3 crs.) Cross-listed as (BIO), DSP 181G. How big data affects our society, from advertising to politics to medicine. (Lec 3) Not for major credit for B.S. Biological Sciences or B.A. Biology. (A1) (GC)

(1 cr.) Cross-listed as (CSC), DSP 220. Public health and recovery from global health crisis like COVID-19 depends on collection and analysis of accurate data. Publicly available health crisis datasets provide an opportunity to introduce students to data science through data exploration, while gaining a better understanding of the global crisis. The course focuses on interactive ways to introduce data exploration through 1-hour weekly working sessions and talks by data scientists who are working on these health crisis data. (Online)

(4 crs.) Cross-listed as (CSC), DSP 310. Data driven programming; data sets, file formats and meta-data; descriptive statistics, data visualization, and foundations of predictive data modeling; accessing web data and data bases; distributed data management. (Lec. 3, Lab. 2) Pre: CSC201 or CSC211 or equivalent, or permission of instructor. Computer Science majors must take as CSC 310; Data Science majors must take as DSP 310.

(3 crs.) Cross-listed as (AMS), DSP 393G. The course implements an active learning pedagogy for students to meticulously and systematically work with 'Big Data' to develop data-driven predictive models for decision-making. (Lec. 3) Pre: Pre: STA 308 or STA 409 or BAI 210; STA 305 or LTI/DSP 110; and MTH 215. (B3) (D1) (GC)

(3 crs.) Cross-listed as (ART), DSP 404. Familiarizes students with the concepts and techniques required in creating and visualizing large and complex data, enabling students to design and present bodies of information. (Studio) Pre: junior, senior, or graduate standing. (A4) (D1)

(3 crs.) Cross-listed as (BIO), DSP 439. Learn about big data and how to write scripts to analyze data. (Lec. 3) Pre: junior standing, MTH 131 or 141. Not for graduate credit.

(4 crs.) Cross-list as (STA), DSP 441. Multivariate data organization and visualization, multinomial and multivariate normal distribution, tests of hypotheses on mean vectors, multivariate regression and classification, principal component analysis, clustering, cross-validation and bootstrapping. (Lec. 3., Lab. 1) Pre: MTH 215; and STA 409, or STA 411, or STA 412; or permission of instructor.

(4 crs.) Cross-listed as (CSC), DSP 461. Broad introduction to fundamental concepts in machine learning. Survey of traditional and newly developed learning algorithms, as well as, their application to real-world problems. (Lec. 3, Lab. 1) Pre: CSC 310 and MTH 215. Computer Science majors must take as CSC 461. Data Science majors must take as DSP 461.

(1-4 crs.) Supervised internship in data science that prepares students for careers in industry. (Practicum) Pre: Junior standing, data science majors, and permission of instructor. May be repeated for a maximum of 8 credits. Not for graduate credit. S/U only.

(4 crs.) Cross-listed as (STA), DSP 490. Practical experience in statistical consulting through various projects. Apply statistical methods to the challenges imposed by real data, and communicate findings effectively. (Lec. 2, Practicum 2) Pre: (STA 411 or 412) and STA 441, or permission of the instructor. Not for graduate credit.

(1-4 crs.) Independent research. Student works in close conjunction with a faculty member on a mutually agreeable topic. Presentation required. (Practicum) Pre: Junior standing or above and data science majors only. Exceptions may be made by the instructor. Permission of instructor. May be repeated twice for a maximum of 8 credits. Not for graduate credit. S/U only.

(3 crs.) Cross-listed as (BIO), DSP 539. Learn about big data and gain sufficient programming skills to analyze data efficiently and accurately for research. (Lec. 3) Pre: graduate standing

(3 crs.) Basic methods and tools needed for data acquisition, cleaning, and aggregation. Measures of data integrity and consistency are determined. Computer-based systems and methods for data storage, retrieval, manipulation, and display are explored. (Accelerated Online Program) Pre: Enrollment in the Online Graduate Certificate in Data Science.

(3 crs.) Cross-listed as (AMS) DSP 553. This course covers a wide range of mathematical tools from Discrete Mathematics, Calculus, Linear Algebra, and Probability Theory that arise in Data Science. Each mathematical construct is accompanied by examples of its use in solving practical problems in Data Science. (Accelerated Online Program) Pre: Enrollment in the Online Graduate Certificate in Data Science.

(3 crs.) Multivariate data organization and visualization, multivariate distributions, tests of hypotheses on mean vectors, multivariate regression and classification, penalized regression, tree-based methods, principal component analysis, clustering, cross-validation, and bootstrapping. (Accelerated Online Program) Pre: DSP 552, 553, and 554, and enolled in the Online Graduate Certificate in Data Science.

(3 crs.) Multivariate data organization and visualization, multivariate distributions, tests of hypotheses on mean vectors, multivariate regression and classification, penalized regression, tree-based methods, principal component analysis, clustering, cross-validation, and bootstrapping. (Accelerated Online Program) Pre: DSP 552 and DSP 553 and admitted to Data Science Certificate or admitted to Data Science Master's Program.

(3 crs.) Survey of traditional and newly developed machine learning techniques from an applied perspective, with emphasis on applications to a variety of domains. (Accelerated Online Program) Pre: DSP 555 and enrollment in the Online Graduate Certificate in Data Science.

(3 crs.) Survey of traditional and newly developed machine learning techniques from an applied perspective, with emphasis on applications to a variety of domains. (Accelerated Online Program) Pre: Credit or concurrent enrollment in DSP 555 and admitted to the Online Graduate Certificate in Data Science or admitted to Data Science Master's program.

(3 crs.) Students apply theoretical knowledge acquired during the Data Science Certificate program to a project involving actual data in a realistic setting. A Team-based capstone data project will provide real-world experiences of data-driven research for students. (Accelerated Online Program) Pre: DSP 556 and Enrollment in the Online Graduate Certificate in Data Science.

(3 crs.) Basic principles and approaches to data visualization, as well as essential methods for data acquisition, cleaning, and aggregation, using Python programming language and Tableau, a powerful, interactive data visualization application. (Accelerated Online Program) Pre: DSP 555 and DSP 556 and Enrollment in the Online Master's in Data Science Program.

(3 crs.) Cross-listed (AMS), DSP 563. Introduction to mathematical foundations necessary to effectively study problems in data science and machine learning. Use linear algebra and optimization pose and solve modern problems leveraging data from diverse applications. (Accelerated Online Program) Pre: DSP 556.

(3 crs.) Statistical methods using R: Empirical Bayes, James-Stein, regression trees, EM algorithm, Jackknife and bootstrap, cross validation, large-scale hypothesis testing, false discovery rate, sparse modeling, LASSO, random forest, boosting, deep learning. (Accelerated Online Program) Pre: DSP 555 and DSP 562 and Enrollment in the Online Master's in Data Science Program.

(3 crs.) Students build a thorough understanding of state -of-the art ML algorithms, including techniques that enable understanding of when and why machine learning works in order to design such algorithms. (Accelerated Online Program) Pre: DSP 563 and Enrollment in the Online Master's in Data Science Program.

(3 crs.) Foundational and recent inventions in data management research. Topics include, indexing, query processing, query languages, NoSQL databases, spatial databases, data warehousing, business intelligence, and big data frameworks. (Accelerated Online Program) Pre: DSP 562 and Enrollment in the Online Master's in Data Science Program.

(3 crs.) Apply Data Science techniques to business functions, including decision-making, finance, accounting, marketing, operations management, information technology, human resources, etc. Includes data visualization and machine learning in a business context. (Accelerated Online Program) Pre: DSP 562 and DSP 563 and Enrollment in the Online Master's in Data Science Program.

(3 crs.) Use data science techniques in data-enabled research in a topic within the biological and life sciences. (Accelerated Online Program) Pre: DSP 566 and DSP 567 and Enrollment in the Online Master's in Data Science Program.

(3 crs.) Students apply theoretical knowledge acquired during the Data Science Certificate program to a project involving actual data in a realistic setting. A Team-based capstone data project will provide real-world experiences of data-driven research for students. (Accelerated Online Program) Pre: DSP 562 and 563 and enrollment in the Online Graduate Certificate in Data Science.