Computer Science Classes

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.

General Education Requirements:
Quantitative Thinking Q2

Distribution Requirements:
Natural science and mathematics

Course Materials

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.

General Education Requirements:
Quantitative Thinking Q2

Distribution Requirements:
Natural science and mathematics

Course Materials

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.

General Education Requirements:
Quantitative Thinking Q2

Distribution Requirements:
Natural science and mathematics

Course Materials

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.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

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.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

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.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

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.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

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. Prerequisite(s): COMP 123 or permission of instructor.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

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. Prerequisite(s): COMP 123 or permission of instructor.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

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. Prerequisite(s): COMP 123 or permission of instructor.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

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. Prerequisite(s): COMP 127 or permission of instructor.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

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. Prerequisite(s): COMP 127 or permission of instructor.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

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. Prerequisite(s): STAT 112 and COMP 123 and STAT 155; STAT 253 recommended but not required.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

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. Prerequisite(s): COMP 128 (or COMP 124, if previously taken) and MATH 279, or permission of instructor.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

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. Prerequisite(s): COMP 128 (or COMP 124, if previously taken) and MATH 279, or permission of instructor.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

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. Prerequisite(s): COMP 127 (COMP 128 recommended), or COMP 124 if previously taken, or permission of instructor.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

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. Prerequisite(s): COMP 127 (COMP 128 recommended), or COMP 124 if previously taken, or permission of instructor.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

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. Prerequisite(s): COMP 127 or permission of instructor.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

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. Prerequisite(s): COMP 127 or permission of instructor.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

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. Prerequisite(s): GEOG 352

General Education Requirements:
Quantitative Thinking Q3

Distribution Requirements:
Social science

Course Materials

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. Prerequisite(s): COMP 127  or permission of instructor

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

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. Prerequisite(s): COMP 127  or permission of instructor

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

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. Prerequisite(s): COMP 127 only if course is taken as the Computer Science cross-list.

General Education Requirements:
Writing WC

Distribution Requirements:

Course Materials

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. Prerequisite(s): COMP 240 or permission of instructor.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

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. Prerequisite(s): MATH 236; one of: MATH 135 or MATH 137 or MATH 237 ; one of: COMP 120 or COMP 123 or COMP 127 or COMP 128; or permission of instructor.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

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. Prerequisite(s): MATH 236; one of: MATH 135 or MATH 137 or MATH 237 ; one of: COMP 120 or COMP 123 or COMP 127 or COMP 128; or permission of instructor.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

In a world driven by digital communication, how do scholars in linguistics and computer science analyze language data from social media? This course provides you with the opportunity to learn how to apply recent Natural Language Processing (NLP) techniques to understand the dynamics of online discourse. We will cover the following topics in this course: topic modeling and text classification to uncover trends and patterns in large-scale social data, automated detection of harmful content and evaluation of such systems, techniques for identifying and tracking misinformation across platforms, assessment of large language models for bias and fairness, and approaches to building equitable and trustworthy AI systems. We will engage with recent academic papers, critically discussing their methods and findings. Hands-on sessions will provide experience replicating these studies and extending them using different NLP tools. By the end of the course, students will be expected to develop their own research project focused on studying social media language. Proficiency in Python programming is required for this course. Prerequisite(s): COMP 123 or permission of the instructor.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

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. Prerequisite(s): COMP 123, MATH 236, MATH 279 and one of: COMP 221, MATH 354/STAT 354, MATH 375, or MATH 379.

General Education Requirements:
Writing WP
Quantitative Thinking Q2

Distribution Requirements:
Natural science and mathematics

Course Materials

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. Prerequisite(s): COMP 221 or permission of instructor.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

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.

General Education Requirements:
Quantitative Thinking Q2

Distribution Requirements:
Natural science and mathematics

Course Materials

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.

General Education Requirements:
Quantitative Thinking Q2

Distribution Requirements:
Natural science and mathematics

Course Materials

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.

General Education Requirements:
Quantitative Thinking Q2

Distribution Requirements:
Natural science and mathematics

Course Materials

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.

General Education Requirements:
Quantitative Thinking Q2

Distribution Requirements:
Natural science and mathematics

Course Materials

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.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

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.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

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. Prerequisite(s): COMP 123 or permission of instructor.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

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. Prerequisite(s): COMP 123 or permission of instructor.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

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. Prerequisite(s): COMP 123 or permission of instructor.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

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. Prerequisite(s): COMP 127 or permission of instructor.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

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. Prerequisite(s): COMP 127 or permission of instructor.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

The sudden recent rise of LLM-based artificial intelligence (AI), notably but not only ChatGPT, is having deep sociocultural, political, economic, and educational effects. In this course we will think about, and with, AI in a global frame. After asking “what is AI?” and reviewing its history, we will explore AI topics including environment, labor, higher education, cognitive surrender, consciousness, personhood, recursivity, hallucination, geopolitics, warfare, alignment, existential risk, the post-Mac professions, and AI in film and literature. Rather than keep AI at a distance, we’ll work with it: carefully, skeptically, critically, together. But often we’ll avoid screens altogether.

General Education Requirements:
Writing WP
Internationalism

Distribution Requirements:

Course Materials

Artificial Intelligence is one of the most exciting and fastest-moving technological fields today. This course examines philosophical questions related to AI. We will explore big-picture questions such as whether AIs can be conscious, whether we can control them, whether there might be an intelligence explosion. What, if anything, distinguishes intelligence from computation, prediction, or pattern recognition? How should we think about AI agency, autonomy, and responsibility? We will also consider ethical and social issues involving large language models, transparency and explainability, the ethical status of robots, and the ethics of deploying AI in high-stakes domains. No programming experience required.

General Education Requirements:

Distribution Requirements:
Humanities

Course Materials

This hybrid theory/practice seminar will trace the history of radical, activist, and countercultural uses of software and digital technology. From the milieu surrounding the Whole Earth Catalog, to hacker collectives such as Anonymous, to recent art and activism, we will explore efforts that have attempted to deploy software and its networks as anti-authoritarian or counter-hegemonic techniques. We will examine concepts that undergird the commitments of such actions, such as liberalism, freedom, sovereignty, prefigurative politics, and direct action. We will consider industry discourse to ask whether and how we might distinguish between radical software and corporate marketing messages around innovation and “disruption.” Readings will include texts by authors such as McKenzie Wark, Fred Turner, Gabriella Coleman, Alex Galloway, and Ruha Benjamin. Coursework will include hands-on projects comprising creative and critical use and misuse of software, such as coding custom web browser extensions and USB keystroke injection attacks. No prior computer programming experience is necessary.

General Education Requirements:

Distribution Requirements:

Course Materials

Varies by semester. Consult the department or class schedule for current listing.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

Varies by semester. Consult the department or class schedule for current listing.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

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. Prerequisite(s): STAT 112 and COMP 123 and STAT 155; STAT 253 recommended but not required.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

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. Prerequisite(s): COMP 128 (or COMP 124, if previously taken) and MATH 279, or permission of instructor.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

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. Prerequisite(s): COMP 128 (or COMP 124, if previously taken) and MATH 279, or permission of instructor.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

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. Prerequisite(s): COMP 127 (COMP 128 recommended), or COMP 124 if previously taken, or permission of instructor.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

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. Prerequisite(s): COMP 127 or permission of instructor.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

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. Prerequisite(s): COMP 123 or permission of instructor.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

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. Prerequisite(s): COMP 128  (COMP 240 and COMP 361 useful but not required)

General Education Requirements:

Distribution Requirements:

Course Materials

Those who pursue technology-based education like Computer Science learn a multitude of technical practices, techniques, logic,and languages to build and design future technologies. These technologies are solutions to world problems or may be hobbyist innature. Regardless, artifacts produced by Computer Scientists, Engineers, Programmers, and other similar stakeholders havesuffered immeasurably through lack of training and awareness on social issues. As a result, we observe in society the numeroustechnologies that currently exist to perpetuate harmful ideologies (-isms), oppressive power structures, and injustices onvulnerable and minority populations. Should these technologies be redesigned or thrown out all together? How can we connectthe design and development of technologies to the behaviors of minoritized identities? This course introduces students to criticalthinking through design frameworks and social constructs, while also developing their ability to analyze how software, tools, andassociated processes and practices can reinforce and exacerbate inequality in society. Once we confront the ways in whichtechnologies contribute to systems of inequality, we will begin to identify relevant problems and solutions to contribute to amore equal, just society. (Formerly known as Computer Science and Social Justice.) Offered every other spring. Prerequisite(s): COMP 123

General Education Requirements:
Writing WP

Distribution Requirements:
Natural science and mathematics

Course Materials

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. Prerequisite(s): COMP 221; or both COMP 127 and STAT 253

General Education Requirements:
Quantitative Thinking Q3

Distribution Requirements:
Natural science and mathematics

Course Materials

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. Prerequisite(s): COMP 212 and STAT 253

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

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. Prerequisite(s): COMP 221; or (COMP 127 and STAT 253); or permission of instructor.

General Education Requirements:
Writing WP

Distribution Requirements:
Natural science and mathematics

Course Materials

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. Prerequisite(s): COMP 240

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials