{"id":260,"date":"2017-10-17T16:34:20","date_gmt":"2017-10-17T16:34:20","guid":{"rendered":"https:\/\/www.macalester.edu\/160-mscs\/courses\/"},"modified":"2024-07-08T15:24:52","modified_gmt":"2024-07-08T15:24:52","slug":"courses","status":"publish","type":"page","link":"https:\/\/www.macalester.edu\/mscs\/courses\/","title":{"rendered":"Course Descriptions"},"content":{"rendered":"

\n \n Computer Science <\/a> | \n \n Mathematics <\/a> | \n \n Statistics <\/a> \n \n<\/p>\n\n\n\n

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Computer Science<\/h2>\n \n
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COMP 112 - Introduction to Data Science<\/h3>\n
\n This course provides an introduction to the handling, analysis, and interpretation of the big datasets now routinely being collected in science, commerce, and government. Students achieve facility with a sophisticated, technical computing environment. The course aligns with techniques being used in several courses in the natural and social sciences, statistics, and mathematics. The course is intended to be accessible to all students, regardless of background.<\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n \n \n

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Cross-Listed as: <\/strong>STAT 112<\/a> <\/span><\/p>\n <\/div>\n \n \n <\/div>\n
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COMP 120 - Computing and Society<\/h3>\n
\n Topics course that introduces students to the field of computing by way of a central theme. Topics vary; offerings include Digital Humanities, Green Computing, and Social Media. Full description given in advance of registration. This course is suitable for students with little or no experience with computing, but it can serve as a starting point for the Computer Science major.<\/span>\n \n

Frequency: Typically offered in the fall as a First Year Course.<\/p>\n \n <\/div>\n \n \n \n \n <\/div>\n
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COMP 123 - Core Concepts in Computer Science<\/h3>\n
\n This course introduces the field of computer science, including central concepts such as the design and implementation of algorithms and programs, testing and analyzing programs, the representation of information within the computer, and the role of abstraction and metaphor in computer science. The exploration of these central ideas will draw examples from a range of application areas including multimedia processing, turtle graphics, and text processing. Course work will use the Python programming language.<\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n \n \n \n <\/div>\n
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COMP 127 - Object-Oriented Programming and Abstraction<\/h3>\n
\n What happens as software grows in complexity? How do we break a program into manageable pieces? How do we write readable, maintainable code? This course is an introduction to the building blocks of software design: abstraction, decomposition, and encapsulation. Using object-oriented programming in Java, we will create graphics, games, and simulations, and explore natural language processing. Topics may include: classes, objects, polymorphism, inheritance, testing, refactoring, events, closures, streams, immutability, parallel programming, and version control. The course culminates in a student-designed project.<\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n

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Prerequisite(s): <\/strong>COMP 123<\/a> or permission of instructor.<\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
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COMP 128 - Data Structures<\/h3>\n
\n This course familiarizes students with the fundamental data structures in computer science. Using the Java programming language, students will study existing data structure implementations, implement their own data structures, and develop data-intensive applications. The course covers stacks, queues, lists, trees, heaps, hash tables, graphs, and the common algorithms that use these data structures. Students will also receive an introduction to basic complexity analysis (Big-O), learn the time complexity of different data structure operations, and gain experience in calculating the time complexity of programs that use data structures.<\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n

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Prerequisite(s): <\/strong>COMP 127<\/a> or permission of instructor.<\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
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COMP 154 - Digital Ethics<\/h3>\n
\n This course looks at ethical questions connected with the internet as we know it today: an online environment where content is generated and shared through user activities such as blogging, media sharing, social networking, tagging, tweeting, virtual world gaming, wiki developing, and the like. We will start by considering debates over freedom of speech, privacy, surveillance, and intellectual property: issues that pre-exist the development of the Internet, but which because of it have taken on new dimensions. From here we will go on to take up some ethical questions arising from four different domains of activity on the social web: gaming, social networking, blog\/wiki developing, and \"hacktivism.\" In the third part of the course, we will consider broad questions connected to the integration of the Internet with devices other than the personal computer and mobile phone and which open the prospect of a world of integrated networked systems. What are some of the impacts of such integration on our everyday ethical relations with others and on the overall quality of our lives? How does being networked affect the meaning of being human?<\/span>\n \n

Frequency: Offered alternate years.<\/p>\n \n <\/div>\n \n \n \n

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Cross-Listed as: <\/strong>PHIL 225<\/a> <\/span><\/p>\n <\/div>\n \n \n <\/div>\n
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COMP 194 - Topics Course<\/h3>\n
\n Varies by semester. Consult the department or class schedule for current listing. <\/span>\n \n <\/div>\n \n \n \n \n <\/div>\n
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COMP 212 - Intermediate Data Science<\/h3>\n
\n This second course in the data science curriculum emphasizes advanced data wrangling and manipulation, interactive visualization, writing functions, working with data in databases, version control, and data ethics. Through open-ended and interdisciplinary projects, students practice the constant feedback loop of asking questions of the data, manipulating the data to help answer the question, and then returning to more questions.<\/span> <\/span>\n \n

Frequency: Every semester<\/p>\n \n <\/div>\n \n

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Prerequisite(s): <\/strong>STAT 112<\/a> and COMP 123<\/a> and STAT 155<\/a>; STAT 253<\/a> recommended but not required.<\/span><\/p>\n <\/div>\n \n \n \n

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Cross-Listed as: <\/strong>STAT 212<\/a> <\/span><\/p>\n <\/div>\n \n \n <\/div>\n
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COMP 221 - Algorithm Design and Analysis<\/h3>\n
\n This course offers an in-depth introduction to the design and analysis of algorithms. Students will work with algorithms in pseudocode, and will learn formal and informal methods for analyzing algorithm efficiency and correctness. Topics may include recursion, divide and conquer, dynamic programming, greedy methods, branch and bound, randomized, probabilistic, and parallel algorithms. Application areas include string processing, graphs, geometric problems, and optimization. This course will introduce computability topics including regular expressions, grammars and parsing, automata, nondeterminism, and NP completeness.<\/span>\n \n

Frequency: Every semester<\/p>\n \n <\/div>\n \n

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Prerequisite(s): <\/strong>COMP 128<\/a> (or COMP 124, if previously taken) and MATH 279<\/a>, or permission of instructor.<\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
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COMP 225 - Software Design and Development<\/h3>\n
\n This course is an introduction to the problem of building software with humans and for humans. Students work in teams to design and implement a semester-long user-facing software project of their own invention. There are no limitations on topic or technology; on the contrary, students are responsible for imagining possibilities, articulating goals, and researching and selecting suitable technologies. The format resembles a studio art class, with in-class discussion guided by sharing and critiquing classmates' ongoing work. Topics include communication, division of labor, user-centered design, human-computer interaction, product management, project management, iterative development, engineering tradeoffs, separation of concerns, code readability and maintainability, refactoring, testing, and version control. Teams give a public demonstration of their working projects at the end of the semester.<\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n

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Prerequisite(s): <\/strong>COMP 127<\/a> (COMP 128<\/a> recommended), or COMP 124 if previously taken, or permission of instructor.<\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
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COMP 240 - Computer Systems<\/h3>\n
\n This course is an introduction to how computer systems work, including how a computer represents data, how code is compiled into instructions for the CPU, and how memory is organized. Students will learn to use the C programming language and assembly language.<\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n

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Prerequisite(s): <\/strong>COMP 127<\/a> or permission of instructor.<\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
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COMP 272 - Advanced Remote Sensing<\/h3>\n
\n This course introduces students to advanced topics in remote sensing analysis and is directed to students who want to work on a research project of their own choice. Introduction to some advanced remote sensing techniques such as the use of machine learning algorithms in image classification analysis (e.g. Random Forests) and time series analysis will be provided, but ultimately topics will be defined by students' interest. Advanced remote sensing techniques will be learned using Google Earth Engine (GEE). GEE is a cloud-based geospatial analysis platform that uses JavaScript and that enables large scale processing of satellite and other types of imagery. No previous coding experience is required and given the project-based nature of the class, students can opt to use GEE or another geospatial software for their projects. Students are expected to build a body of literature related to a topic of their choice, lead discussions, analyze data, peer-review other projects, and other steps related to the production of a scientific paper. The ultimate goal is to produce a \"research manuscript\" by the end of the semester and the majority of the grade will come from completing the steps leading to manuscript production.<\/span>\n \n

Frequency: Spring semester only.<\/p>\n \n <\/div>\n \n

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Prerequisite(s): <\/strong>GEOG 352<\/a> <\/span><\/p>\n <\/div>\n \n \n \n

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Cross-Listed as: <\/strong>GEOG 372<\/a> <\/span><\/p>\n <\/div>\n \n \n <\/div>\n
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COMP 294 - Topics Course<\/h3>\n
\n Varies by semester. Consult the department or class schedule for current listing. <\/span>\n \n <\/div>\n \n \n \n \n <\/div>\n
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COMP 302 - Introduction to Database Management Systems<\/h3>\n
\n This course will introduce students to the design, implementation, and analysis of databases stored in database management systems (DBMS). Topics include implementation-neutral data modeling, database design, database implementation, and data analysis using relational algebra and SQL. Students will generate data models based on real-world problems, and implement a database in a state-of-the-art DBMS. Students will master complex data analysis by learning to first design database queries and then implement them in a database query language such as SQL. Advanced topics include objects in databases, indexing for improved performance, distributed databases, and data warehouses.<\/span>\n \n

Frequency: Every year.<\/p>\n \n <\/div>\n \n

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Prerequisite(s): <\/strong>COMP 127<\/a> or permission of instructor<\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
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COMP 320 - Computational Biology<\/h3>\n
\n This interdisciplnary course will examine selected topics in computational biology, including basic bioinformatics, algorithms used in genomics and genome analysis, computational techniques for systems biology, and synthetic biology.<\/span>\n \n

Frequency: Occasional, usually fall semester.<\/p>\n \n <\/div>\n \n

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Prerequisite(s): <\/strong>COMP 123<\/a>; BIOL 190<\/a> recommended<\/span><\/p>\n <\/div>\n \n \n \n

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Cross-Listed as: <\/strong>BIOL 320<\/a> <\/span><\/p>\n <\/div>\n \n \n <\/div>\n
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COMP 321 - Software Testing<\/h3>\n
\n This course will take a hands-on approach to software testing by applying general software testing strategies to a previously written piece of software. Students will work in groups to perform static analysis on shared code, modify it, and write manual tests. Later in the semester, we will look into automated testing approaches. We will also discuss real world software failures from a testing perspective. Examples of previously written pieces of software include homework assignments from COMP 127<\/a> and COMP 128<\/a>.<\/span>\n \n

Frequency: Alternate years.<\/p>\n \n <\/div>\n \n

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Prerequisite(s): <\/strong>COMP 127<\/a> and COMP 128<\/a> <\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
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COMP 325 - Video Games: Coding and Narrative<\/h3>\n
\n Videogames dominate entertainment culture. But like all popular forms of entertainment, they are often looked down upon as aesthetically superficial, intellectually uncomplicated, and somehow bad for you. They are \"just pop culture.\" Like Shakespeare was in his time. Like the novel was when it was invented. And like film and television shows were when they were invented. This course takes seriously the deep intellectual and aesthetic value of videogames and of videogame making. Videogames are expanding the possibilities and the borders of storytelling and narrative design. They are pushing the limits of coding wizardry. They have also become one of the most creative popular-cultural sites for experimenting with and understanding other minds and identities. In this class, students will work in interdisciplinary teams to bring world-building narrative techniques to an immersive visual setting while exploring technical challenges involved in programming and game development through hands-on projects.<\/span>\n \n

Frequency: Alternate years.<\/p>\n \n <\/div>\n \n

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Prerequisite(s): <\/strong>COMP 127<\/a> only if course is taken as the Computer Science cross-list.<\/span><\/p>\n <\/div>\n \n \n \n

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Cross-Listed as: <\/strong>ENGL 224<\/a> <\/span><\/p>\n <\/div>\n \n \n <\/div>\n
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COMP 340 - Digital Electronics<\/h3>\n
\n A survey of fundamental ideas and methods used in the design and construction of digital electronic circuits such as computers. Emphasis will be on applying the theoretical aspects of digital design to the actual construction of circuits in the laboratory. Topics to be covered include basic circuit theory, transistor physics, logic families (TTL, CMOS), Boolean logic principles, combinatorial design techniques, sequential logic techniques, memory circuits and timing, and applications to microprocessor and computer design. Three lectures and one three-hour laboratory per week.<\/span>\n \n

Frequency: Offered alternate spring semesters.<\/p>\n \n <\/div>\n \n

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Prerequisite(s): <\/strong>MATH 137<\/a> or permission of instructor.<\/span><\/p>\n <\/div>\n \n \n \n

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Cross-Listed as: <\/strong>PHYS 340<\/a> <\/span><\/p>\n <\/div>\n \n \n <\/div>\n
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COMP 342 - Operating Systems and Computer Architecture<\/h3>\n
\n This course introduces the basic design and architecture of operating systems. Concepts to be discussed include sequential and concurrent processes, synchronization and mutual exclusion, processor scheduling, time-sharing, multitasking, parallel processing, memory management, file system design, and security. Students will learn concepts through lectures, readings, and low-level programming using the C programming language.<\/span>\n \n

Frequency: Offered occsionally.<\/p>\n \n <\/div>\n \n

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Prerequisite(s): <\/strong>COMP 240<\/a> or permission of instructor.<\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
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COMP 361 - Theory of Computation<\/h3>\n
\n This course examines the theoretical foundations of computation. It explores different mathematical models that try to formalize our informal notion of an algorithm. Models include finite automata, regular expressions, grammars, and Turing machines. The course also discusses ideas about what can and cannot be computed. In addition, the course explores the basics of complexity theory, examining broad categories of problems and their algorithms, and their efficiency. The focus is on the question of P versus NP, and the NP-complete set.<\/span>\n \n

Frequency: Alternate years.<\/p>\n \n <\/div>\n \n

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Prerequisite(s): <\/strong>(COMP 128<\/a> or COMP 221<\/a> or COMP 124 if previously taken) and MATH 279<\/a>, or permission of instructor.<\/span><\/p>\n <\/div>\n \n \n \n

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Cross-Listed as: <\/strong>MATH 361<\/a> <\/span><\/p>\n <\/div>\n \n \n <\/div>\n
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COMP 364 - Human-Computer Interaction<\/h3>\n
\n From doors we can't figure out how to open to websites we can't figure out how to navigate, we've all encountered counter-intuitive designs. So how do we create design systems that inspire joy instead of frustration? And how do we ensure that we design for everyone, including people who may be very different from ourselves? This course will teach techniques to improve our design of user interfaces, by centering the human in the design process. It will provide an introduction to the field of human-computer interaction and the design and evaluation of user interfaces. Students will learn methods for designing and prototyping interactive systems and some of the principles of creating good design based on human cognition. The class will be a mix of lectures, in-class activities and design critiques. The central focus of the course is a group project, in which students will formulate a design problem, explore potential design opportunities and tradeoffs, and iteratively evaluate and improve upon a digital prototype of their design.<\/span>\n \n

Frequency: Alternate years.<\/p>\n \n <\/div>\n \n

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Prerequisite(s): <\/strong>COMP 123<\/a> or permission of instructor.<\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
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COMP 365 - Computational Linear Algebra<\/h3>\n
\n A mix of applied linear algebra and numerical analysis, this course covers a central point of contact between mathematics and computer science. Many of the computational techniques important in science, commerce, and statistics are based on concepts from linear algebra, such as subspaces, projections, and matrix decompositions. The course reviews these concepts, adopts them to large scales, and applies them in the core techniques of scientific computing. These include solving systems of linear and nonlinear equations, approximation and statistical function estimation, optimization, interpolation, eigenvalue and singular value decompositions, and compression. Applications throughout the natural sciences, social sciences, statistics, and computer science.<\/span>\n \n

Frequency: Every spring.<\/p>\n \n <\/div>\n \n

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Prerequisite(s): <\/strong>MATH 236<\/a>; and one of:MATH 135<\/a> or MATH 137<\/a> or MATH 237<\/a>; and one of: COMP 120<\/a> or COMP 123<\/a> or COMP 127<\/a> or COMP 128<\/a>; or permission of instructor.<\/span><\/p>\n <\/div>\n \n \n \n

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Cross-Listed as: <\/strong>MATH 365<\/a> <\/span><\/p>\n <\/div>\n \n \n <\/div>\n
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COMP 381 - Programming Languages<\/h3>\n
\n Why do people create different programming languages? What characteristics do languages have in common? What design decisions differentiate them, and what tradeoffs motivate those decisions? How do languages affect the style of code we write, our development processes, and the ways we think about software? In this course, we will examine a wide variety of programming languages, many briefly and a few in depth. We will compare how they approach topics such as type systems, abstraction, composition, state and mutability, access control, flow control, function dispatch, closures, metaprogramming, concurrency, memory management, compilation, and runtime environment.<\/span>\n \n

Frequency: Alternate years.<\/p>\n \n <\/div>\n \n

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Prerequisite(s): <\/strong>COMP 128<\/a> (COMP 240<\/a> and COMP 361<\/a> useful but not required)<\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
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COMP 394 - Topics Course<\/h3>\n
\n Varies by semester. Consult the department or class schedule for current listing. <\/span>\n \n <\/div>\n \n \n \n \n <\/div>\n
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COMP 435 - Data Visualization<\/h3>\n
\n In this capstone course, we will study techniques and algorithms for visualization design and analysis. We will cover topics such as visualization of multivariate, temporal, text-based, spatial, hierarchical, and network\/graph-based data to clearly and effectively convey information graphically. Our discussions will revolve around principles of graphic design, perceptual psychology, and cognitive science. Students will have the opportunity to complete several programming assignments and data analysis tasks.<\/span>\n \n

Frequency: Alternate years.<\/p>\n \n <\/div>\n \n

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Prerequisite(s): <\/strong>COMP 127<\/a> or permission of instructor.<\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
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COMP 440 - Collective Intelligence<\/h3>\n
\n This course introduces the theory and practice of data science applied to online communities such as Wikipedia, Facebook, and Twitter. Students will read and discuss recent academic research papers that analyze behavior on these websites and use computational simulation, machine learning, and data-mining techniques to analyze massive behavioral datasets in areas such as recommender systems, natural language processing, and tagging systems. This course counts as the capstone.<\/span>\n \n

Frequency: Alternate years.<\/p>\n \n <\/div>\n \n

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Prerequisite(s): <\/strong>COMP 221; or both COMP 127 and STAT 253<\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
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COMP 445 - Parallel and Distributed Processing<\/h3>\n
\n Many current computational challenges, such as Internet search, protein folding, and data mining require the use of multiple processes running in parallel, whether on a single multiprocessor machine (parallel processing) or on multiple machines connected together on a network (distributed processing). The type of processing required to solve such problems in adequate amounts of time involves dividing the program and\/or problem space into parts that can run simultaneously on many processors. In this course we will explore the various computer architectures used for this purpose and the issues involved with programming parallel solutions in such environments. Students will examine several types of problems that can benefit from parallel or distributed solutions and develop their own solutions for them. This course counts as the capstone.<\/span>\n \n

Frequency: Alternate years, sometimes more often.<\/p>\n \n <\/div>\n \n

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Prerequisite(s): <\/strong>COMP 240<\/a> and COMP 221<\/a>, or permission of instructor.<\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
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COMP 446 - Internet Computing<\/h3>\n
\n This course introduces technologies for building dynamic web applications. It will look at all stages in the web application design process, including: 1) the basic protocols and technologies underlying the web (e.g. HTTP, REST), 2) front-end web technologies, such as HTML, CSS, and Javascript, 3) and application servers that manage requests for information, update data, etc. The course will be programming-intensive, with students using web frameworks to design and implement Internet applications. The format of the course will be mainly laboratory-based sessions, where students learn components of a web application, supported by lectures and discussions. Students will research particular topics and present their findings during these discussion sessions. The course will also investigate the usability of designs from a human factors standpoint and discuss privacy and other social consequences of this technology. This course counts as the capstone.<\/span>\n \n

Frequency: Alternate years.<\/p>\n \n <\/div>\n \n

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Prerequisite(s): <\/strong>COMP 225<\/a> or permission of instructor.<\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
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COMP 456 - Projects in Data Science<\/h3>\n
\n This third course in the data science curriculum is a capstone course that emphasizes team-based learning through open-ended data science projects. Working with a team throughout the course of the semester you will take on an interdisciplinary in-depth data science project and gain experience in developing and refining research questions, identifying and wrangling datasets, and clearly presenting results and conclusions. Mini-lectures by the instructor, guest speakers, and students will present advanced topics that supplement and support team-based learning. Counts as a capstone course for the Computer Science major and the Data Science major.<\/span>\n \n

Frequency: Fall semester only.<\/p>\n \n <\/div>\n \n

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Prerequisite(s): <\/strong> COMP 212<\/a> and STAT 253<\/a> <\/span><\/p>\n <\/div>\n \n \n \n

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Cross-Listed as: <\/strong>STAT 456<\/a> <\/span><\/p>\n <\/div>\n \n \n <\/div>\n
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COMP 465 - Interactive Computer Graphics<\/h3>\n
\n This course will investigate the theory and practice of computer graphics programming using C++ and OpenGL. Through hands-on projects, supported by lecture and discussion, you will learn the fundamentals of creating interactive 2D and 3D images with applications in art, design, games, movies, science, and medicine. Topics covered will include event loops, polygonal models, rendering techniques, texturing, lighting, interaction techniques, and virtual reality. This course counts as the capstone.<\/span>\n \n

Frequency: Alternate years.<\/p>\n \n <\/div>\n \n

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Prerequisite(s): <\/strong>COMP 240<\/a>; Linear Algebra recommended but not required.<\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
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COMP 479 - Network Science<\/h3>\n
\n The modern Information Age has produced a wealth of data about the complex networks that tie us together. In response, the field of Network Science has arisen, bringing together mathematics, computer science, sociology, biology, economics and other fields. This course will explore the fundamental questions and the mathematical tools of Network Science. This includes: the structure of complex networks, including connectedness, centrality and \"long tails\"; community detection; random\/strategic models for network formation; diffusion\/contagion and \"tipping points\" on networks; and algorithms for analyzing complex networks.<\/span>\n \n

Frequency: Offered odd-numbered spring semesters.<\/p>\n \n <\/div>\n \n

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Prerequisite(s): <\/strong>COMP 123<\/a>, MATH 236<\/a>, MATH 279<\/a> and one of: COMP 221<\/a>, MATH 354<\/a>\/STAT 354<\/a>, MATH 375<\/a>, or MATH 379<\/a>.<\/span><\/p>\n <\/div>\n \n \n \n

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Cross-Listed as: <\/strong>MATH 479<\/a> <\/span><\/p>\n <\/div>\n \n \n <\/div>\n
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COMP 480 - Bodies\/Minds: AI Robotics<\/h3>\n
\n This course examines two distinct aspects of work in robotics: the physical construction of the robot's \"body\" and the creation of robot control programs that form the robot's \"mind.\" It will study the strengths and weaknesses of a variety of robot sensors, including sonar, infrared, touch, GPS, and computer vision. It will also examine both reactive and deliberative approaches to robot control programs. The course will include hands-on work with multiple robots, and a semester-long course project in robotics. This course involves programming in Python; students should have a basic familiarity with Python or be prepared to learn Python during the course. This course counts as the capstone.<\/span>\n \n

Frequency: Alternate years.<\/p>\n \n <\/div>\n \n

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Prerequisite(s): <\/strong>COMP 221<\/a> or permission of instructor.<\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
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COMP 484 - Introduction to Artificial Intelligence<\/h3>\n
\n An introduction to the basic principles and techniques of artificial intelligence. Topics will include specific AI techniques, a range of application areas, and connections between AI and other areas of study (i.e., philosophy, psychology). Techniques may include heuristic search, automated reasoning, machine learning, deliberative planning and behavior-based agent control. Application areas include robotics, games, knowledge representation, and natural language processing. This course involves programming in Python; students should have a basic familiarity with Python or be prepared to learn Python during the course. This course counts as the capstone.<\/span>\n \n

Frequency: Every fall.<\/p>\n \n <\/div>\n \n

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Prerequisite(s): <\/strong>COMP 221; or both COMP 127 and STAT 253<\/span> <\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
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COMP 487 - Computer Security and Privacy<\/h3>\n
\n An introduction to computer security and privacy. Topics will include privacy, threat modeling, software security, web tracking, web security, usable privacy and security, authentication, anonymity, network security, social engineering, the relationship of the law to security and privacy, and ethics. This course will include hands-on experience with security exploits in a Linux environment and student-led discussions of research papers. Students will complete a capstone project in a security- or privacy-related topic of their choice.<\/span>\n \n

Frequency: Every fall.<\/p>\n \n <\/div>\n \n

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Prerequisite(s): <\/strong>COMP 240<\/a> <\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
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COMP 494 - Topics Course<\/h3>\n
\n Varies by semester. Consult the department or class schedule for current listing. <\/span>\n \n <\/div>\n \n \n \n \n <\/div>\n
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COMP 601 - Tutorial<\/h3>\n
\n Closely supervised individual (or very small group) study with a faculty member in which a student may explore, by way of readings, short writings, etc., an area of computer science not available through the regular offerings. S\/SD\/N grading option only.<\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n

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Prerequisite(s): <\/strong>Permission of instructor and department chair.<\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

COMP 602 - Tutorial<\/h3>\n
\n Closely supervised individual (or very small group) study with a faculty member in which a student may explore, by way of readings, short writings, etc., an area of computer science not available through the regular offerings.<\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>Permission of instructor and department chair. <\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

COMP 603 - Tutorial<\/h3>\n
\n Closely supervised individual (or very small group) study with a faculty member in which a student may explore, by way of readings, short writings, etc., an area of computer science not available through the regular offerings.<\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>Permission of instructor and department chair. <\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

COMP 604 - Tutorial<\/h3>\n
\n Closely supervised individual (or very small group) study with a faculty member in which a student may explore, by way of readings, short writings, etc., an area of computer science not available through the regular offerings.<\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>Permission of instructor and department chair. <\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

COMP 611 - Independent Project<\/h3>\n
\n Individual project including library research, conferences with instructor, oral and written reports on independent work in computer science. Subject matter may complement but not duplicate material covered in regular courses.<\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>Arrangements must be made with a department member prior to registration and permission of instructor and department chair. <\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

COMP 612 - Independent Project<\/h3>\n
\n Individual project including library research, conferences with instructor, oral and written reports on independent work in computer science. Subject matter may complement but not duplicate material covered in regular courses.<\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>Arrangements must be made with a department member prior to registration and permission of instructor and department chair. <\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

COMP 613 - Independent Project<\/h3>\n
\n Individual project including library research, conferences with instructor, oral and written reports on independent work in computer science. Subject matter may complement but not duplicate material covered in regular courses.<\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>Arrangements must be made with a department member prior to registration and permission of instructor and department chair. <\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

COMP 614 - Independent Project<\/h3>\n
\n Individual project including library research, conferences with instructor, oral and written reports on independent work in computer science. Subject matter may complement but not duplicate material covered in regular courses.<\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>Arrangements must be made with a department member prior to registration and permission of instructor and department chair. <\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

COMP 621 - Internship<\/h3>\n
\n Internships are offered only as S\/SD\/N grading option.<\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>Available to junior and senior students with declared majors in computer science. Arrangements must be made prior to registration. Permission of instructor. Work with Internship Office.<\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

COMP 622 - Internship<\/h3>\n
\n Internships are offered only as S\/SD\/N grading option.<\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>Available to junior and senior students with declared majors in computer science. Arrangements must be made prior to registration. Permission of instructor. Work with Internship Office.<\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

COMP 623 - Internship<\/h3>\n
\n Internships are offered only as S\/SD\/N grading option.<\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>Available to junior and senior students with declared majors in computer science. Arrangements must be made prior to registration. Permission of instructor. Work with Internship Office.<\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

COMP 624 - Internship<\/h3>\n
\n Internships are offered only as S\/SD\/N grading option.<\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>Available to junior and senior students with declared majors in computer science. Arrangements must be made prior to registration. Permission of instructor. Work with Internship Office.<\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

COMP 631 - Preceptorship<\/h3>\n
\n Work in assisting faculty in the planning and teaching of a course.<\/span> <\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>Permission of instructor. Work with Academic Programs.<\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

COMP 632 - Preceptorship<\/h3>\n
\n Work in assisting faculty in the planning and teaching of a course.<\/span> <\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>Permission of instructor. Work with Academic Programs.<\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

COMP 633 - Preceptorship<\/h3>\n
\n Work in assisting faculty in the planning and teaching of a course.<\/span> <\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>Permission of instructor. Work with Academic Programs.<\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

COMP 634 - Preceptorship<\/h3>\n
\n Work in assisting faculty in the planning and teaching of a course.<\/span> <\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>Permission of instructor. Work with Academic Programs.<\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

COMP 641 - Honors Independent<\/h3>\n
\n Independent research, writing, or other preparation leading to the culmination of the senior honors project.<\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>Permission of instructor and department chair. <\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

COMP 642 - Honors Independent<\/h3>\n
\n Independent research, writing, or other preparation leading to the culmination of the senior honors project.<\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>Permission of instructor and department chair. <\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

COMP 643 - Honors Independent<\/h3>\n
\n Independent research, writing, or other preparation leading to the culmination of the senior honors project.<\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>Permission of instructor and department chair. <\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

COMP 644 - Honors Independent<\/h3>\n
\n Independent research, writing, or other preparation leading to the culmination of the senior honors project.<\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>Permission of instructor and department chair. <\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n<\/div>\n\n\n\n

\n

Mathematics<\/h2>\n \n
\n

MATH 135 - Applied Multivariable Calculus I<\/h3>\n
\n A first course designed for students with no previous calculus experience. This course focuses on calculus useful for applied work in the natural and social sciences. There is a strong emphasis on developing scientific computing and mathematical modeling skills. The topics include functions as models of data, differential calculus of functions of one and several variables, integration, differential equations, and estimation techniques. Applications are drawn from varied areas, including biology, chemistry, economics, and physics.<\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

MATH 137 - Applied Multivariable Calculus II<\/h3>\n
\n A second course in calculus that focuses on theoretical and applied calculus in the mathematical, natural, and social sciences. Topics include: partial derivatives, gradients, contour plots, constrained and unconstrained optimization, Taylor polynomials, interpretations of integrals via finite sums, the fundamental theorem of calculus, double integrals over a rectangle,and differential equations. Attention is given to both symbolic and numerical computing.<\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>MATH 135<\/a>, or AP Calculus AB (with a score of 4 or 5), or IB HL Mathematics: Analysis & Approaches (with a score of 5), or IB HL Mathematics: Applications & Interpretations (with a score of 6 or 7), or a comparable year of high school calculus.<\/span> <\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

MATH 194 - Topics Course<\/h3>\n
\n Varies by semester. Consult the department or class schedule for current listing. <\/span>\n \n <\/div>\n \n \n \n \n <\/div>\n
\n \n
\n

MATH 212 - Philosophy of Mathematics<\/h3>\n
\n Why does 2 + 2 equal four? Can a diagram prove a mathematical truth? Is mathematics a social construction or do mathematical facts exist independently of our knowing them? Philosophy of mathematics considers these sorts of questions in an effort to understand the logical and philosophical foundations of mathematics. Topics include mathematical truth, mathematical reality, and mathematical justifications (knowledge). Typically we focus on the history of mathematics of the past 200 years, highlighting the way philosophical debates arise in mathematics itself and shape its future.<\/span>\n \n

Frequency: Alternate years.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>PHIL 111<\/a>, MATH 279<\/a>, or permission of the instructor.<\/span><\/p>\n <\/div>\n \n \n \n

\n

Cross-Listed as: <\/strong>PHIL 309<\/a><\/span><\/p>\n <\/div>\n \n \n <\/div>\n
\n \n

\n

MATH 236 - Linear Algebra<\/h3>\n
\n Linear algebra is one of the pillars of mathematics, both pure and applied. Linear relations can be used to model phenomena from numerous disciplines in the mathematical sciences, physical sciences, social sciences, engineering, and computer science. This introduction to linear algebra blends mathematical computation, theory, abstraction, and application. It starts with systems of linear equations and grows into the study of matrices, vector spaces, linear independence, dimension, linear transformations, orthogonality and projections, eigenvectors, and their applications. The resulting linear algebraic framework is a flexible and powerful way to approach multidimensional problems.<\/span>\n \n

Frequency: Offered every semester.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong> MATH 279<\/a> or MATH 137<\/a> or STAT 155<\/a>.<\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

MATH 237 - Applied Multivariable Calculus III<\/h3>\n
\n A third course in calculus that focuses on methods useful for the mathematical and physical sciences. Topics include: scalar and vector-valued functions and derivatives; parameterization and integration over regions, curves, and surfaces; the divergence theorem; and Taylor series. Attention is given to both symbolic and numerical computing. Applications drawn from the natural sciences, probability, and other areas of mathematics.<\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>MATH 137<\/a>, or AP Calculus BC (with a score of 4 or 5), or IB HL Mathematics: Analysis & Approaches (with a score of 6 or 7), or a comparable two years of high school calculus.<\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

MATH 279 - Discrete Mathematics<\/h3>\n
\n Discrete mathematics studies collections of distinct, separate objects and is complementary to calculus (which studies continuous phenomena). This course introduces techniques for analyzing arrangements of objects and the relationships between them. The material emphasizes problem solving and logical argumentation, rather than computation. Topics include basic counting principles, induction, logic, recurrence relations, number theory, and graph theory.<\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

MATH 294 - Topics Course<\/h3>\n
\n Varies by semester. Consult the department or class schedule for current listing. <\/span>\n \n <\/div>\n \n \n \n \n <\/div>\n
\n \n
\n

MATH 312 - Differential Equations<\/h3>\n
\n A survey of differential equations as a tool for the study of smoothly varying quantities, with applications ranging from the physical sciences (e.g. radioactive decay and mechanics) to human behavior (e.g. economics and linguistics). We introduce famous scalar and vector differential equations as modeling tools. We cover standard analytical, computational, and qualitative methods for studying differential equations. Further topics can include the Laplace transform, an introduction to partial differential equations, dynamical systems, specialized applications, or rigorous existence and uniqueness theorems.<\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong> MATH 236<\/a> and MATH 237<\/a>.<\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

MATH 313 - Advanced Symbolic Logic<\/h3>\n
\n A second course in symbolic logic which extends the methods of logic. A main purpose of this course is to study logic itself-to prove things about the system of logic learned in the introductory course. This course is thus largely logic about logic. Topics include second order logic and basic set theory; soundness, consistency and completeness of first order logic; incompleteness of arithmetic; Turing computability; modal logic; and intuitionistic logic.<\/span>\n \n

Frequency: Alternate years.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong> PHIL 111<\/a> or MATH 279<\/a> or permission of instructor.<\/span><\/p>\n <\/div>\n \n \n \n

\n

Cross-Listed as: <\/strong>PHIL 313<\/a> <\/span><\/p>\n <\/div>\n \n \n <\/div>\n
\n \n

\n

MATH 354 - Probability<\/h3>\n
\n An introduction to probability theory and application. Fundamental probability concepts include: sample spaces, combinatorics, conditional probability, independence, random variables, probability distributions, expectation, variance, moment-generating functions, and limit theorems. Special course topics vary and may include: computer simulation, stochastic processes, and statistical inference.<\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>MATH 137<\/a> or MATH 237<\/a> <\/span><\/p>\n <\/div>\n \n \n \n

\n

Cross-Listed as: <\/strong>STAT 354<\/a> <\/span><\/p>\n <\/div>\n \n \n <\/div>\n
\n \n

\n

MATH 355 - Statistical Theory<\/h3>\n
\n An important course for students considering graduate work in statistics or biostatistics, this course explores the mathematical theory underlying modern statistical techniques. Topics include the theory behind: parameter estimation, evaluation of estimator properties, hypothesis testing, confidence intervals, and linear regression. Special topics vary and may include: tests of independence, resampling techniques, introductory Bayesian concepts, and non\u00adparametric methods.<\/span>\n \n

Frequency: Spring semester only.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>STAT 155<\/a>, MATH 236<\/a>, MATH 354<\/a>\/STAT 354<\/a>.<\/span><\/p>\n <\/div>\n \n \n \n

\n

Cross-Listed as: <\/strong>STAT 355<\/a> <\/span><\/p>\n <\/div>\n \n \n <\/div>\n
\n \n

\n

MATH 361 - Theory of Computation<\/h3>\n
\n This course examines the theoretical foundations of computation. It explores different mathematical models that try to formalize our informal notion of an algorithm. Models include finite automata, regular expressions, grammars, and Turing machines. The course also discusses ideas about what can and cannot be computed. In addition, the course explores the basics of complexity theory, examining broad categories of problems and their algorithms, and their efficiency. The focus is on the question of P versus NP, and the NP-complete set.<\/span>\n \n

Frequency: Alternate years.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>(COMP 128<\/a> or COMP 221<\/a>) and MATH 279<\/a>, or permission of instructor.<\/span><\/p>\n <\/div>\n \n \n \n

\n

Cross-Listed as: <\/strong>COMP 361<\/a> <\/span><\/p>\n <\/div>\n \n \n <\/div>\n
\n \n

\n

MATH 365 - Computational Linear Algebra<\/h3>\n
\n A mix of applied linear algebra and numerical analysis, this course covers a central point of contact between mathematics and computer science. Many of the computational techniques important in science, commerce, and statistics are based on concepts from linear algebra, such as subspaces, projections, and matrix decompositions. The course reviews these concepts, adopts them to large scales, and applies them in the core techniques of scientific computing. These include solving systems of linear and nonlinear equations, approximation and statistical function estimation, optimization, interpolation, eigenvalue and singular value decompositions, and compression. Applications throughout the natural sciences, social sciences, statistics, and computer science<\/span>\n \n

Frequency: Every spring.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>MATH 236<\/a>; one of:MATH 135<\/a> or MATH 137<\/a> or MATH 237<\/a> ; one of: COMP 120<\/a> or COMP 123<\/a> or COMP 127<\/a> or COMP 128<\/a>; or permission of instructor.<\/span><\/p>\n <\/div>\n \n \n \n

\n

Cross-Listed as: <\/strong> COMP 365<\/a> <\/span><\/p>\n <\/div>\n \n \n <\/div>\n
\n \n

\n

MATH 375 - Graph Theory<\/h3>\n
\n Graphs are mathematical structures which represent the relationships between objects in a set. Graph Theory falls under the umbrella of discrete mathematics and borrows methods from several areas of study to explore properties like the overall strength and complexity of the graph. Topics in this course include connectivity, graph coloring, trees, graph algorithms, and network flows. This course also discusses how these topics relate to graphs found in applications, such as social networks and the internet.<\/span>\n \n

Frequency: Alternate fall semesters.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>MATH 279<\/a> <\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

MATH 376 - Algebraic Structures<\/h3>\n
\n Introduction to algebraic structures, including groups, rings, fields, and vector spaces. Other topics may include geometric constructions, symmetry groups, algebraic coding theory, Burnside's counting theorem, Galois theory.<\/span>\n \n

Frequency: Every spring.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong> MATH 279<\/a> and MATH 236<\/a> .<\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

MATH 377 - Real Analysis<\/h3>\n
\n Basic theory for the real numbers and the notions of limit, continuity, differentiation, integration, convergence, uniform convergence, and infinite series. Additional topics may include metric and normed linear spaces, point set topology, analytic number theory, Fourier series.<\/span>\n \n

Frequency: Every fall.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>MATH 137<\/a> and one of: MATH 236<\/a>, MATH 237<\/a>, or MATH 279<\/a>. <\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

MATH 378 - Complex Analysis<\/h3>\n
\n A course in the study of functions of complex numbers, a topic which touches fields as varied as number theory, applied mathematics, physics, engineering, algebraic geometry, and more. We cover: geometry and algebra of complex numbers; complex functions; differentiation and integration, including the Cauchy\u00adRiemann equations, Cauchy's theorem, and the Cauchy integral formula; Taylor series, Laurent series, and the Residue Theorem. Throughout, we emphasize complex functions as transformations of the plane, and also make a strong connection to applications. This course is appropriate both for students with an interest and background in theoretical mathematics and proof, and students whose primary interest is the application of mathematics to other fields.<\/span>\n \n

Frequency: Every spring.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>MATH 236<\/a> and MATH 237<\/a>.<\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

MATH 379 - Combinatorics<\/h3>\n
\n A second course in discrete mathematics that develops more advanced counting techniques. Combinatorics is the study of arrangements, patterns and configurations. Generally speaking, we fix a set of objects and then arrange those objects into patterns satisfying special rules. Once we identify an interesting family of objects, we ask: how many are there? what are their structural properties? how can we find the \"best\" one(s)? Topics are drawn from graph theory, enumerative combinatorics, graph algorithms, and generating functions. <\/span>\n \n

Frequency: Offered odd-numbered fall semesters.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>MATH 279<\/a> <\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

MATH 394 - Topics Course<\/h3>\n
\n Varies by semester. Consult the department or class schedule for current listing. <\/span>\n \n <\/div>\n \n \n \n \n <\/div>\n
\n \n
\n

MATH 432 - Mathematical Modeling<\/h3>\n
\n Draws on the student's general background in mathematics to construct models for problems arising from such diverse areas as the physical sciences, life sciences, political science, economics, and computing. Emphasis will be on the design, analysis, accuracy, and appropriateness of a model for a given problem. Case studies will be used extensively. Specific mathematical techniques will vary with the instructor and student interest. This course counts towards the capstone requirement.<\/span>\n \n

Frequency: Every fall semester.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>MATH 312<\/a> and one of the following: COMP 120<\/a> or COMP 123<\/a> or COMP 124<\/a>.<\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

MATH 437 - Topics in Applied Mathematics<\/h3>\n
\n Topics in applied mathematics chosen from: Fourier analysis; partial differential equations; wavelets; signal processing; time-frequency analysis; stochastic processes; optimization; computational geometry; and more. Topics are examined in theoretical and applied contexts, and from analytical and computational viewpoints. This course counts toward the capstone requirement. May be repeated for credit with departmental approval.<\/span>\n \n

Frequency: Odd numbered spring semesters.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>MATH 236<\/a> and one of the following: COMP 120<\/a> or COMP 123<\/a> or COMP 124<\/a>. MATH 312<\/a> or MATH 365<\/a> recommended.<\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

MATH 465 - Signal Processing<\/h3>\n
\n This course leverages theory and computation to explore how transforming data from one domain into a different domain often makes it easier to analyze, compress, communicate, or find structure in the data. Topics include: how signals such as audio clips and images can be broken down into combinations of basic building blocks (analysis); how those fundamental building blocks can be combined into more complicated signals (synthesis); the theory and applications of Fourier, discrete cosine, wavelet, and time-frequency transforms; the Nyquist-Shannon sampling theorem; the Heisenberg uncertainty principle; and sparse representations. Applications will be drawn from audio and speech processing, graph signal processing, medical imaging, physics, geology, biology, finance, and other disciplines.<\/span>\n \n

Frequency: Alternate fall semesters.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>MATH 236<\/a> and COMP 123<\/a>. MATH 365<\/a> recommended.<\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

MATH 471 - Topology<\/h3>\n
\n A course in both theoretical and computational mathematics. Theoretical concepts include fundamental ideas from point set topology---continuity, convergence, and connectedness---as well as selected topics from algebraic topology---the fundamental group, elementary homotopy theory, and homology. This theoretical framework provides a backbone to understand new advances in topological data analysis. Applications are chosen from diverse fields such as biological aggregations, medicine, image processing, signal processing, and sensor networks. This course counts towards the capstone requirement.<\/span>\n \n

Frequency: Alternate spring semesters.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>MATH 236<\/a> and one of:<\/strong> MATH 365<\/a>; or MATH 375<\/a>; or MATH 376<\/a>; or MATH 377<\/a>; or MATH 378<\/a>; or MATH 379<\/a>.<\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

MATH 476 - Representation Theory<\/h3>\n
\n A course in matrix representations of groups, a topic which unites the powers of group theory and linear algebra. Topics include: symmetry in linear spaces, modules, group actions, characters, tensor products, and Fourier analysis on groups. Applications are chosen from: ranked data, molecular vibrations, quantum mechanics, random walks, number theory, and combinatorics. Important ideas from linear algebra are revisited from a more sophisticated point of view. These include: linear transformations, abstract vector spaces, change of basis, subspaces, direct sums, projections, and eigenvalues and eigenvectors.<\/span>\n \n

Frequency: Odd numbered fall semesters.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>MATH 376<\/a> .<\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

MATH 477 - Topics in Analysis<\/h3>\n
\n A continuation of Real Analysis. Topics chosen from: the development of the Riemann and Lebesgue integrals; measure theory; functional analysis; Fourier analysis. A large component of the course will be an open-ended exploratory project. This course counts toward the capstone requirement. <\/span>\n \n

Frequency: Alternate spring semesters.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong> MATH 236<\/a> and MATH 377<\/a> <\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

MATH 479 - Network Science<\/h3>\n
\n The modern Information Age has produced a wealth of data about the complex networks that tie us together. In response, the field of Network Science has arisen, bringing together mathematics, computer science, sociology, biology, economics and other fields. This course will explore the fundamental questions and the mathematical tools of Network Science. This includes: the structure of complex networks, including connectedness, centrality and \"long tails\"; community detection; random\/strategic models for network formation; diffusion\/contagion and \"tipping points\" on networks; and algorithms for analyzing complex networks.<\/span>\n \n

Frequency: Offered odd-numbered spring semesters.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong> COMP 123<\/a>, MATH 236<\/a>, MATH 279<\/a> and one of: COMP 221<\/a>, MATH 354<\/a>\/STAT 354<\/a>, MATH 375<\/a>, or MATH 379<\/a>.<\/span><\/p>\n <\/div>\n \n \n \n

\n

Cross-Listed as: <\/strong>COMP 479<\/a> <\/span><\/p>\n <\/div>\n \n \n <\/div>\n
\n \n

\n

MATH 494 - Topics Course<\/h3>\n
\n Varies by semester. Consult the department or class schedule for current listing. <\/span>\n \n <\/div>\n \n \n \n \n <\/div>\n
\n \n
\n

MATH 601 - Tutorial<\/h3>\n
\n Closely supervised individual (or very small group) study with a faculty member in which a student may explore, by way of readings, short writings, etc., an area of mathematics not available through the regular offerings.<\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>Permission of instructor and department chair. <\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

MATH 602 - Tutorial<\/h3>\n
\n Closely supervised individual (or very small group) study with a faculty member in which a student may explore, by way of readings, short writings, etc., an area of mathematics not available through the regular offerings.<\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>Permission of instructor and department chair. <\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

MATH 603 - Tutorial<\/h3>\n
\n Closely supervised individual (or very small group) study with a faculty member in which a student may explore, by way of readings, short writings, etc., an area of mathematics not available through the regular offerings.<\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>Permission of instructor and department chair. <\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

MATH 604 - Tutorial<\/h3>\n
\n Closely supervised individual (or very small group) study with a faculty member in which a student may explore, by way of readings, short writings, etc., an area of mathematics not available through the regular offerings.<\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>Permission of instructor and department chair. <\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

MATH 611 - Independent Project<\/h3>\n
\n Individual project including library research, conferences with instructor, oral and written reports on independent work in mathematics. Subject matter may complement but not duplicate material covered in regular courses. <\/span>\n \n <\/div>\n \n
\n

Prerequisite(s): <\/strong>Arrangement with faculty prior to registration, departmental approval, and permission of instructor and department chair. <\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

MATH 612 - Independent Project<\/h3>\n
\n Individual project including library research, conferences with instructor, oral and written reports on independent work in mathematics. Subject matter may complement but not duplicate material covered in regular courses. <\/span>\n \n <\/div>\n \n
\n

Prerequisite(s): <\/strong>Arrangement with faculty prior to registration, departmental approval, and permission of instructor and department chair. <\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

MATH 613 - Independent Project<\/h3>\n
\n Individual project including library research, conferences with instructor, oral and written reports on independent work in mathematics. Subject matter may complement but not duplicate material covered in regular courses. <\/span>\n \n <\/div>\n \n
\n

Prerequisite(s): <\/strong>Arrangement with faculty prior to registration, departmental approval, and permission of instructor and department chair. <\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

MATH 614 - Independent Project<\/h3>\n
\n Individual project including library research, conferences with instructor, oral and written reports on independent work in mathematics. Subject matter may complement but not duplicate material covered in regular courses. <\/span>\n \n <\/div>\n \n
\n

Prerequisite(s): <\/strong>Arrangement with faculty prior to registration, departmental approval, and permission of instructor and department chair. <\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

MATH 621 - Internship<\/h3>\n
\n Internships are offered only as S\/SD\/N grading option.<\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>Junior and Senior standing. Arrangements must be made prior to registration. Departmental approval and permission of instructor required. Work with Internship Office.<\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

MATH 622 - Internship<\/h3>\n
\n Internships are offered only as S\/SD\/N grading option.<\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>Junior and Senior standing. Arrangements must be made prior to registration. Departmental approval and permission of instructor required. Work with Internship Office.<\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

MATH 623 - Internship<\/h3>\n
\n Internships are offered only as S\/SD\/N grading option.<\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>Junior and Senior standing. Arrangements must be made prior to registration. Departmental approval and permission of instructor required. Work with Internship Office.<\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

MATH 624 - Internship<\/h3>\n
\n Internships are offered only as S\/SD\/N grading option.<\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>Junior and Senior standing. Arrangements must be made prior to registration. Departmental approval and permission of instructor required. Work with Internship Office.<\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

MATH 631 - Preceptorship<\/h3>\n
\n Work in assisting faculty in the planning and teaching of a course.<\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>Permission of the instructor. Work with Academic Programs Office to complete a Preceptor Learning Contract Form .<\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

MATH 632 - Preceptorship<\/h3>\n
\n Work in assisting faculty in the planning and teaching of a course.<\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>Permission of the instructor. Work with Academic Programs Office to complete a Preceptor Learning Contract Form . <\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

MATH 633 - Preceptorship<\/h3>\n
\n Work in assisting faculty in the planning and teaching of a course.<\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>Permission of the instructor. Work with Academic Programs Office to complete a Preceptor Learning Contract Form . <\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

MATH 634 - Preceptorship<\/h3>\n
\n Work in assisting faculty in the planning and teaching of a course.<\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>Permission of the instructor. Work with Academic Programs Office to complete a Preceptor Learning Contract Form . <\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

MATH 641 - Honors Independent<\/h3>\n
\n Independent research, writing, or other preparation leading to the culmination of the senior honors project.<\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>Permission of instructor and department chair. <\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

MATH 642 - Honors Independent<\/h3>\n
\n Independent research, writing, or other preparation leading to the culmination of the senior honors project.<\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>Permission of instructor and department chair. <\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

MATH 643 - Honors Independent<\/h3>\n
\n Independent research, writing, or other preparation leading to the culmination of the senior honors project.<\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>Permission of instructor and department chair. <\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

MATH 644 - Honors Independent<\/h3>\n
\n Independent research, writing, or other preparation leading to the culmination of the senior honors project.<\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>Permission of instructor and department chair. <\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n<\/div>\n\n\n\n

\n

Statistics<\/h2>\n \n
\n

STAT 112 - Introduction to Data Science<\/h3>\n
\n This course provides an introduction to the handling, analysis, and interpretation of the big datasets now routinely being collected in science, commerce, and government. Students achieve facility with a sophisticated, technical computing environment. The course aligns with techniques being used in several courses in the natural and social sciences, statistics, and mathematics. The course is intended to be accessible to all students, regardless of background.<\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n \n \n

\n

Cross-Listed as: <\/strong>COMP 112<\/a> <\/span><\/p>\n <\/div>\n \n \n <\/div>\n
\n \n

\n

STAT 125 - Epidemiology<\/h3>\n
\n Epidemiology is the study of the distribution and determinants of disease and health in human populations and the application of this understanding to the solution of public health problems. Topics include measurement of disease and health, the outbreak and spread of disease, reasoning about cause and effect, analysis of risk, detection and classification, and the evaluation of trade-offs. The course is designed to fulfill and extend the professional community's consensus definition of undergraduate epidemiology. In addition to the techniques of modern epidemiology, the course emphasizes the historical evolution of ideas of causation, treatment, and prevention of disease. The course is a required component of the concentration in Community and Global Health.<\/span>\n \n

Frequency: Offered most semesters; check with MSCS chair for upcoming academic year.<\/p>\n \n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

STAT 155 - Introduction to Statistical Modeling<\/h3>\n
\n An introductory statistics course with an emphasis on multivariate modeling. Topics include descriptive statistics, data visualizations, multivariate linear regression, logistic regression, probability, model building and interpretation (i.e., confounding variables, causal diagrams, data context), and statistical inference (i.e., confidence intervals and hypothesis testing).<\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

STAT 194 - Topics Course<\/h3>\n
\n Varies by semester. Consult the department or class schedule for current listing.<\/span><\/span>\n \n <\/div>\n \n \n \n \n <\/div>\n
\n \n
\n

STAT 202 - Data and Society<\/h3>\n
\n This course is designed to encourage and facilitate students' collaboration with real data in society and the various stakeholders in a supportive environment. Students in this course will work in small groups to engage in data-focused work in partnership with a local office or organization. Through independent and collaborative learning, students will learn new data science and statistical methods and skills needed for the data project, will develop communication and project management skills , and will regularly reflect individually and as a cohort, learning from each other as well as their organizational partners. May be repeated for credit.<\/strong><\/span>\n \n

Frequency: Offered occasionally.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>STAT 155<\/a> and STAT 112<\/a> or COMP 112<\/a> <\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

STAT 212 - Intermediate Data Science<\/h3>\n
\n This second course in the data science curriculum emphasizes advanced data wrangling and manipulation, interactive visualization, writing functions, working with data in databases, version control, and data ethics. Through open-ended and interdisciplinary projects, students practice the constant feedback loop of asking questions of the data, manipulating the data to help answer the question, and then returning to more questions.<\/span>\n \n

Frequency: Every semester<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>COMP 112<\/a> and COMP 123<\/a> and STAT 155<\/a>; STAT 253<\/a> recommended but not required.<\/span><\/p>\n <\/div>\n \n \n \n

\n

Cross-Listed as: <\/strong>COMP 212<\/a> <\/span><\/p>\n <\/div>\n \n \n <\/div>\n
\n \n

\n

STAT 253 - Statistical Machine Learning<\/h3>\n
\n The linear and logistic modeling techniques from STAT 155<\/a> are augmented with the three foundational machine learning tasks: regression, classification, and clustering. The course explores techniques central to these tasks, including methods of data exploration, supervised and unsupervised learning, parametric and nonparametric modeling, and model training and evaluation. As required by the application of these sophisticated techniques, the course also introduces foundational statistical computer programming concepts. <\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>STAT 155<\/a>.<\/span><\/strong><\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

STAT 294 - Topics Course<\/h3>\n
\n Varies by semester. Consult the department or class schedule for current listing.<\/span><\/span>\n \n <\/div>\n \n \n \n \n <\/div>\n
\n \n
\n

STAT 354 - Probability<\/h3>\n
\n An introduction to probability theory and application. Fundamental probability concepts include: sample spaces, combinatorics, conditional probability, independence, random variables, probability distributions, expectation, variance, moment-generating functions, and limit theorems. Special course topics vary and may include: computer simulation, stochastic processes, and statistical inference.<\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>MATH 137<\/a> or MATH 237<\/a><\/span><\/p>\n <\/div>\n \n \n \n

\n

Cross-Listed as: <\/strong>MATH 354<\/a> <\/span><\/p>\n <\/div>\n \n \n <\/div>\n
\n \n

\n

STAT 355 - Statistical Theory<\/h3>\n
\n An important course for students considering graduate work in statistics or biostatistics, this course explores the mathematical theory underlying modern statistical techniques. Topics include the theory behind: parameter estimation, evaluation of estimator properties, hypothesis testing, confidence intervals, and linear regression. Special topics vary and may include: tests of independence, resampling techniques, introductory Bayesian concepts, and non\u00adparametric methods.<\/span>\n \n

Frequency: Spring semester only.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>STAT 155<\/a>, MATH 236<\/a>, MATH 354<\/a>\/STAT 354<\/a>.<\/span><\/p>\n <\/div>\n \n \n \n

\n

Cross-Listed as: <\/strong>MATH 355<\/a> <\/span><\/p>\n <\/div>\n \n \n <\/div>\n
\n \n

\n

STAT 394 - Topics Course<\/h3>\n
\n Varies by semester. Consult the department or class schedule for current listing.<\/span>\n \n <\/div>\n \n \n \n \n <\/div>\n
\n \n
\n

STAT 451 - Causal Inference<\/h3>\n
\n \"Correlation does not imply causation.\" We've all heard this mantra, warding us away from reading too much into the association between murder rates and ice cream sales, between shoe size and reading ability, and the like. But this mantra leaves us wanting: how do we study causation? Questions of causation are essential when we try to understand the effects of new medical treatments, economic policies, or government programs. In this course, we'll examine frameworks of thinking, statistical tools, and study designs that enable us to learn about the causal effects of interventions. Some specific topics include causal graphs, experimental studies, and quasi-experimental study designs. This course is a capstone course with a significant project component.<\/span>\n \n

Frequency: Offered occasionally.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>STAT 155<\/a> <\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

STAT 452 - Correlated Data<\/h3>\n
\n One of the most common assumptions made in Statistics is that observations are independent; however, there are many situations in which the data violate this assumption by design. In this class, we discuss advanced visualization and modeling approaches for when the data are correlated. Topics will include time series analysis, longitudinal data analysis, and spatial data analysis. Applications are drawn from across the disciplines.<\/span>\n \n

Frequency: On a rotating basis.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>STAT 155<\/a> and MATH 354<\/a>\/STAT 354<\/a> <\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

STAT 453 - Survival Analysis<\/h3>\n
\n Survival analysis refers to a set of methods used for modeling \"time-\u00adto-\u00adevent\" or \"duration\" data. In many studies, the outcome of interest is the time between between events (e.g. onset of Alzheimer's until death, time unlit default on a loan, unemployment duration, marriage duration, removal-\u00adto-\u00adrecurrence of a tumor, emergency room length of stay). Survival analysis evolved from a practical reality: the precise values of data are often unknown. We will introduce the concepts of censoring and truncation, and discuss the Kaplan-\u00adMeier curve, parametric regression models, Cox's proportional hazards model, and time-\u00advarying covariates. The course will have an applied focus. Examples may be drawn from a variety of fields including, but not restricted to, medicine, economics, sociology, and political science. The course will count toward completion of the concentration in Community and Global Health.<\/span>\n \n

Frequency: On a rotating basis.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>STAT 155<\/a> and MATH 354<\/a>.<\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

STAT 454 - Bayesian Statistics<\/h3>\n
\n Bayesian statistics, an alternative to the traditional frequentist approach taken in our other statistics courses, is playing an increasingly integral role in modern statistics. The Bayesian philosophy is natural, allowing us to formally balance data with our prior knowledge, and updating this knowledge as more data come in. It answers natural questions. It can shine in settings where frequentist \"likelihood\" methods break down. And it is becoming increasingly popular with the availability of computing tools necessary to its implementation. This course explores the Bayesian approach to statistical analysis, Bayesian computing, and both sides of the frequentist versus Bayesian debate. Topics include Bayes' Theorem, prior and posterior probability distributions, Bayesian regression, Bayesian hierarchical models, and an introduction to Markov chain Monte Carlo computing techniques.<\/span>\n \n

Frequency: Offered occasionally.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>STAT 155<\/a> and MATH 354<\/a>.<\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

STAT 456 - Projects in Data Science<\/h3>\n
\n This third course in the data science curriculum is a capstone course that emphasizes team-based learning through open-ended data science projects. Working with a team throughout the course of the semester you will take on an interdisciplinary in-depth data science project and gain experience in developing and refining research questions, identifying and wrangling datasets, and clearly presenting results and conclusions. Mini-lectures by the instructor, guest speakers, and students will present advanced topics that supplement and support team-based learning. Counts as a capstone course for the Computer Science major and the Data Science major.<\/span>\n \n

Frequency: Fall semester only.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>STAT 212<\/a> and STAT 253<\/a> <\/span><\/p>\n <\/div>\n \n \n \n

\n

Cross-Listed as: <\/strong>COMP 456<\/a> <\/span><\/p>\n <\/div>\n \n \n <\/div>\n
\n \n

\n

STAT 494 - Topics Course<\/h3>\n
\n Varies by semester. Consult the department or class schedule for current listing.<\/span>\n \n

Frequency: On a rotating basis.<\/p>\n \n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

STAT 601 - Tutorial<\/h3>\n
\n Closely supervised individual (or very small group) study with a faculty member in which a student may explore, by way of readings, short writings, etc., an area of mathematics not available through the regular offerings.<\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong> Permission of instructor and department chair.<\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

STAT 602 - Tutorial<\/h3>\n
\n Closely supervised individual (or very small group) study with a faculty member in which a student may explore, by way of readings, short writings, etc., an area of mathematics not available through the regular offerings.<\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>Permission of instructor and department chair.<\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

STAT 603 - Tutorial<\/h3>\n
\n Closely supervised individual (or very small group) study with a faculty member in which a student may explore, by way of readings, short writings, etc., an area of mathematics not available through the regular offerings.<\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>Permission of instructor and department chair.<\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

STAT 604 - Tutorial<\/h3>\n
\n Closely supervised individual (or very small group) study with a faculty member in which a student may explore, by way of readings, short writings, etc., an area of mathematics not available through the regular offerings.<\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>Permission of instructor and department chair.<\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

STAT 611 - Independent Project<\/h3>\n
\n Individual project including library research, conferences with instructor, oral and written reports on independent work in mathematics. Subject matter may complement but not duplicate material covered in regular courses.<\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>Arrangement with faculty prior to registration, departmental approval, and permission of instructor and department chair.<\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

STAT 612 - Independent Project<\/h3>\n
\n Individual project including library research, conferences with instructor, oral and written reports on independent work in mathematics. Subject matter may complement but not duplicate material covered in regular courses.<\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>Arrangement with faculty prior to registration, departmental approval, and permission of instructor and department chair.<\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

STAT 613 - Independent Project<\/h3>\n
\n Individual project including library research, conferences with instructor, oral and written reports on independent work in mathematics. Subject matter may complement but not duplicate material covered in regular courses.<\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>Arrangement with faculty prior to registration, departmental approval, and permission of instructor and department chair.<\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

STAT 614 - Independent Project<\/h3>\n
\n Individual project including library research, conferences with instructor, oral and written reports on independent work in mathematics. Subject matter may complement but not duplicate material covered in regular courses.<\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>Arrangement with faculty prior to registration, departmental approval, and permission of instructor and department chair.<\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

STAT 621 - Internship<\/h3>\n
\n Internships are offered only as S\/SD\/N grading option.<\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>Junior and Senior standing. Arrangements must be made prior to registration. Departmental approval and permission of instructor required. Work with Internship Office.<\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

STAT 622 - Internship<\/h3>\n
\n Internships are offered only as S\/SD\/N grading option.<\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>Junior and Senior standing. Arrangements must be made prior to registration. Departmental approval and permission of instructor required. Work with Internship Office.<\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

STAT 623 - Internship<\/h3>\n
\n Internships are offered only as S\/SD\/N grading option.<\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>Junior and Senior standing. Arrangements must be made prior to registration. Departmental approval and permission of instructor required. Work with Internship Office.<\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

STAT 624 - Internship<\/h3>\n
\n Internships are offered only as S\/SD\/N grading option.<\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>Junior and Senior standing. Arrangements must be made prior to registration. Departmental approval and permission of instructor required. Work with Internship Office.<\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

STAT 631 - Preceptorship<\/h3>\n
\n Work in assisting faculty in the planning and teaching of a course.<\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>Permission of the instructor. Work with Academic Programs Office to complete a Preceptor Learning Contract Form.<\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

STAT 632 - Preceptorship<\/h3>\n
\n Work in assisting faculty in the planning and teaching of a course.<\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>Permission of the instructor. Work with Academic Programs Office to complete a Preceptor Learning Contract Form.<\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

STAT 633 - Preceptorship<\/h3>\n
\n Work in assisting faculty in the planning and teaching of a course.<\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>Permission of the instructor. Work with Academic Programs Office to complete a Preceptor Learning Contract Form.<\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

STAT 634 - Preceptorship<\/h3>\n
\n Work in assisting faculty in the planning and teaching of a course.<\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>Permission of the instructor. Work with Academic Programs Office to complete a Preceptor Learning Contract Form.<\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

STAT 641 - Honors Independent<\/h3>\n
\n Independent research, writing, or other preparation leading to the culmination of the senior honors project.<\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>Permission of instructor and department chair.<\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

STAT 642 - Honors Independent<\/h3>\n
\n Independent research, writing, or other preparation leading to the culmination of the senior honors project.<\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>Permission of instructor and department chair.<\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

STAT 643 - Honors Independent<\/h3>\n
\n Independent research, writing, or other preparation leading to the culmination of the senior honors project.<\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>Permission of instructor and department chair.<\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
\n \n

\n

STAT 644 - Honors Independent<\/h3>\n
\n Independent research, writing, or other preparation leading to the culmination of the senior honors project.<\/span>\n \n

Frequency: Every semester.<\/p>\n \n <\/div>\n \n

\n

Prerequisite(s): <\/strong>Permission of instructor and department chair.<\/span><\/p>\n <\/div>\n \n \n \n \n <\/div>\n
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