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 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

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. There is a required 1.5 hour laboratory section associated with this course. 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. There is a required 1.5 hour laboratory section associated with this course. 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. There is a required 1.5 hour laboratory section associated with this course. 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. There is a required 1.5 hour laboratory section associated with this course. 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. There is a required 1.5 hour laboratory section associated with this course. 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. There is a required 1.5 hour laboratory section associated with this course. 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. There is a required 1.5 hour laboratory section associated with this course. 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. There is a required 1.5 hour laboratory section associated with this course. Prerequisite(s): COMP 123 or permission of instructor.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

Digital devices and the activities that they afford permeate many aspects of our everyday lives and social institutions. In this course, we will consider a wide array of ethical issues connected to these devices and activities, including algorithmic bias, robotic rights, privacy, dataveillance, and the ethical responsibilities of social media companies. The questions we will ask will be both small and big—including ones about the ethical impacts of “digital life” on our relations with others, the quality of our lives, and the very meaning of being human itself. In general, this course aspires to do three things. One is to provide you with an opportunity to build a critical awareness of key concerns and debates involved in the rapidly growing field of digital ethics. Another is to introduce you to the general dimensions of philosophy as an activity—what it is to “do” philosophy—beginning with forming a philosophical question and leading up to the structured presentation of philosophical ideas. The third is to give you an opportunity to do philosophy itself through bettering your skills at close reading, imaginative and critical thinking, and argumentative writing. You will have the chance to write both informally and formally, and to engage in different kinds of philosophical writing, including a “public” philosophy essay and a joint research paper.

General Education Requirements:
Writing WA

Distribution Requirements:
Humanities

Course Materials

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?

General Education Requirements:
Writing WA

Distribution Requirements:
Humanities

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 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 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 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 permission of instructor.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

This course familiarizes the student with the internal design and organization of computers. Topics include number systems, internal data representations, microarchitectures, the functional units of a computer system, memory, processor, and input/output structures, instruction sets and assembly language, addressing techniques, system software, and concurrency and parallelism. Prerequisite(s): COMP 127 or permission of instructor.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

This course familiarizes the student with the internal design and organization of computers. Topics include number systems, internal data representations, microarchitectures, the functional units of a computer system, memory, processor, and input/output structures, instruction sets and assembly language, addressing techniques, system software, and concurrency and parallelism. 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.

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.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

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. Prerequisite(s): MATH 137 or permission of instructor.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

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. Prerequisite(s): MATH 137 or permission of instructor.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

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. Prerequisite(s): (COMP 128 or COMP 221) and MATH 279, or permission of instructor.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

We write software using programming languages: Python, Java, C, R. Where do these languages come from? Who creates them? Why are there different ones? What characteristics do they have in common, and what design decisions differentiate them? What problems and constraints drive those decisions, and what tradeoffs do those decisions incur? How do languages affect the style of code we write, our development processes, and the way we think about software? In this course, we will examine a wide variety of programming languages — many briefly, a few in depth — comparing their approaches to topics such as type systems, abstractions, function / method dispatch, closures / higher-order functions, metaprogramming, access control, concurrency, memory management, compilation, and runtime environment. Coursework will include programming exercises, student-led presentations, and a final project. Prerequisites: COMP 124 required; COMP 240 and COMP 261 recommended.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

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. Prerequisite(s): COMP 240; Linear Algebra recommended but not required.

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 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. This course counts as a capstone for the Computer Science major.

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 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. There is a required 1.5 hour laboratory section associated with this course. 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. There is a required 1.5 hour laboratory section associated with this course. 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. There is a required 1.5 hour laboratory section associated with this course. 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. There is a required 1.5 hour laboratory section associated with this course. 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. There is a required 1.5 hour laboratory section associated with this course. 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. There is a required 1.5 hour laboratory section associated with this course. 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

What are the truths and falsehoods that data and databases tell us about contemporary culture? Increasingly, computational methods are being used to ask questions and look for patterns in cultural data (from museums, libraries, archives, and elsewhere). This class provides an introduction to some of the methods and tools used to investigate data and datasets, while encouraging critical engagement with the humanities-based research questions that surround them. Students will consider ethical and design questions encompassing the collection, analysis, and presentation of data through class conversations and the process of creating their own digital projects.

General Education Requirements:
Quantitative Thinking Q2

Distribution Requirements:
Humanities

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 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 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 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 permission of instructor.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

This course familiarizes the student with the internal design and organization of computers. Topics include number systems, internal data representations, microarchitectures, the functional units of a computer system, memory, processor, and input/output structures, instruction sets and assembly language, addressing techniques, system software, and concurrency and parallelism. Prerequisite(s): COMP 127 or permission of instructor.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

This course familiarizes the student with the internal design and organization of computers. Topics include number systems, internal data representations, microarchitectures, the functional units of a computer system, memory, processor, and input/output structures, instruction sets and assembly language, addressing techniques, system software, and concurrency and parallelism. 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.

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): COMP 120 or COMP 123, and MATH 236 .

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): COMP 120 or COMP 123, and MATH 236 .

General Education Requirements:

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

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 assignments requiring practice of security exploits and tools in a Linux environment. Emphasis will also be placed on communication and research, and students will read and lead discussions on research papers in security- and privacy-related topics. Prerequisite: COMP 240.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

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. Prerequisite(s): COMP 240 and COMP 221, or permission of instructor.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials