A student looks at a computer screen

Computer Science students solve advanced problems with a mix of theory and practice. Topics include:

  • Computer graphics
  • Database programming
  • Artificial intelligence
  • Language processing
  • Web development
  • Networks
A student looks at a computer screen

About Computer Science

Computer Science studies the computational structures and processes that appear throughout the natural and human worlds. The study of those processes (known as algorithms) can lend insight into the functioning of our brains, the structure of our genes, the mechanisms by which people form communities, and many other questions in a wide range of disciplines. At the same time, an understanding of algorithms and the structure of data can help us create a tremendous variety of useful software tools. Carleton’s computer science curriculum is designed to provide students with a balance between theoretical study and the practical application of theory to the design and construction of software.

Since computer science plays a key role in our world, we recommend Introduction to Computer Science (CS 111) for all Carleton students.

Those who choose to major in computer science find many opportunities following graduation. Carleton Computer Science graduates who pursue employment in the computing industry find jobs in many different roles and at a wide variety of companies. In addition to graduate programs in computer science, Carleton CS majors seeking further education have pursued graduate study in areas such as bioinformatics, linguistics, cognitive science, and law.

Requirements for the Computer Science Major

72 credits required for the major.

  • Courses required for the major. Because the content of CS 202 and MATH 236 have sufficient overlap, we will accept MATH 236 in lieu of the CS 202 requirement.
  • CS 111: Introduction to Computer Science
  • CS 201: Data Structures
  • CS 202: Mathematics of Computer Science
  • CS 208: Introduction to Computer Systems
  • CS 251: Programming Languages: Design and Implementation
  • CS 252: Algorithms
  • CS 254: Computability and Complexity
  • CS 257: Software Design
  • MATH 111: Introduction to Calculus
  • Twelve additional credits from Computer Science
    • Courses numbered 200 or above (excluding independent study, senior seminar and comps).
  • Integrative Exercise: Majors must complete a capstone experience (CS 399 and CS 400) during the senior year. The student will participate on a team of four to seven students working on a faculty-specified project.

Potential majors should take Computer Science 111, Mathematics 111, and at least one of Computer Science 201 and 202 by the end of the sophomore year.

Although only one mathematics course is required for the CS major, we encourage our students to take mathematics and statistics courses beyond Mathematics 111. Students contemplating graduate study should consider taking the full Calculus sequence plus Statistics 120 and Mathematics 232, and additional computer science electives. Those interested in computer engineering should consider taking physics courses through Electricity & Magnetism and Electronics.

Computer Science Courses

  • CS 099 Summer Computer Science Program

    Computer science is a rich academic field that seeks to systematically study the processes for solving problems and untangle the complexities in the concrete physical world and the abstract mathematical world. The Summer Liberal Arts Institute (SLAI) at Carleton focuses on understanding how to think about these processes, how to program computers to implement them, and how to apply computer science ideas to real problems of interest. 

    Not offered in 2023-24

  • CS 099 Summer Computer Science Program

    Not offered in 2023-24

  • CS 100 Ethics of Technology

    What should technology know about us? What actions should technology be allowed to conduct on our behalf? Who makes these decisions, and whose voices are excluded from these conversations? Can algorithms ever be truly fair, just, and unbiased, or are they forever doomed to perpetuate existing inequities? We’ll address these questions, and many more, as we explore the history, present, and possible futures of the design, implementation, deployment, and usage of algorithms, apps, systems, devices, and all things tech. This course will equip you to perform the complex ethical reasoning required of living in a technically-focused society.

  • CS 111 Introduction to Computer Science

    This course will introduce you to computer programming and the design of algorithms. By writing programs to solve problems in areas such as image processing, text processing, and simple games, you will learn about recursive and iterative algorithms, complexity analysis, graphics, data representation, software engineering, and object-oriented design. No previous programming experience is necessary. Students who have received credit for Computer Science 201 or above are not eligible to enroll in Computer Science 111.

  • CS 200 Data Structures with Problem Solving

    Think back to your favorite assignment from Introduction to Computer Science. Did you ever get the feeling that “there has to be a better/smarter way to do this problem”? The Data Structures course is all about how to store information intelligently and access it efficiently. How can Google take your query, compare it to billions of web pages, and return the answer in less than one second? How can one store information so as to balance the competing needs for fast data retrieval and fast data modification? To help us answer questions like these, we will analyze and implement stacks, queues, trees, linked lists, graphs, and hash tables. This version of Data Structures includes extra class time to support students’ problem solving by meeting five days per week, and is encouraged for students who may have struggled in CS111 or otherwise believe they would benefit from extra support. This course fulfills all requirements of CS 201, and students should take only one of CS 200 or CS 201.

  • CS 201 Data Structures

    Think back to your favorite assignment from Introduction to Computer Science. Did you ever get the feeling that “there has to be a better/smarter way to do this problem”? The Data Structures course is all about how to store information intelligently and access it efficiently. How can Google take your query, compare it to billions of web pages, and return the answer in less than one second? How can one store information so as to balance the competing needs for fast data retrieval and fast data modification? To help us answer questions like these, we will analyze and implement stacks, queues, trees, linked lists, graphs, and hash tables. Students who have received credit for a course for which Computer Science 201 is a prerequisite are not eligible to enroll in Computer Science 201.

  • CS 202 Mathematics of Computer Science

    This course introduces some of the formal tools of computer science, using a variety of applications as a vehicle. You’ll learn how to encode data so that when you scratch the back of a DVD, it still plays just fine; how to distribute “shares” of your floor’s PIN so that any five of you can withdraw money from the floor bank account (but no four of you can); how to play chess; and more. Topics that we’ll explore along the way include: logic and proofs, number theory, elementary complexity theory and recurrence relations, basic probability, counting techniques, and graphs.

  • CS 208 Introduction to Computer Systems

    Are you curious what’s really going on when a computer runs your code? In this course we will demystify the machine and the tools that we use to program it. Our broad survey of how computer systems execute programs, store information, and communicate will focus on the hardware/software interface, including data representation, instruction set architecture, the C programming language, memory management, and the operating system process model.

  • CS 231 Computer Security

    Hackers, phishers, and spammers–at best they annoy us, at worst they disrupt communication systems, steal identities, bring down corporations, and compromise sensitive systems. In this course, we’ll study various aspects of computer and network security, focusing mainly on the technical aspects as well as the social and cultural costs of providing (or not providing) security. Topics include cryptography, authentication and identification schemes, intrusion detection, viruses and worms, spam prevention, firewalls, denial of service, electronic commerce, privacy, and usability.

    Not offered in 2023-24

  • CS 232 Art, Interactivity, and Microcontrollers

    In this hands-on course, taught (in an art studio) by a sculpture professor and computer science professor, we’ll explore and create interactive three dimensional art. Using basic construction techniques, microprocessors, and programming, this class brings together sculpture, engineering, computer science, and aesthetic design. Students will engage the nuts and bolts of fabrication, learn to program microcontrollers, and study the design of interactive constructions. Collaborative labs and individual projects will culminate in a campus-wide exhibition. No prior building experience is required.

  • CS 251 Programming Languages: Design and Implementation

    What makes a programming language like “Python” or like “Java”? This course will look past superficial properties (like indentation) and into the soul of programming languages. We will explore a variety of topics in programming language construction and design: syntax and semantics, mechanisms for parameter passing, typing, scoping, and control structures. Students will expand their programming experience to include other programming paradigms, including functional languages like Scheme and ML.

  • CS 252 Algorithms

    A course on techniques used in the design and analysis of efficient algorithms. We will cover several major algorithmic design paradigms (greedy algorithms, dynamic programming, divide and conquer, and network flow). Along the way, we will explore the application of these techniques to a variety of domains (natural language processing, economics, computational biology, and data mining, for example). As time permits, we will include supplementary topics like randomized algorithms, advanced data structures, and amortized analysis.

  • CS 254 Computability and Complexity

    An introduction to the theory of computation. What problems can and cannot be solved efficiently by computers? What problems cannot be solved by computers, period? Topics include formal models of computation, including finite-state automata, pushdown automata, and Turing machines; formal languages, including regular expressions and context-free grammars; computability and uncomputability; and computational complexity, particularly NP-completeness.

  • CS 257 Software Design

    It’s easy to write a mediocre computer program, and lots of people do it. Good programs are quite a bit harder to write, and are correspondingly less common. In this course, we will study techniques, tools, and habits that will improve your chances of writing good software. While working on several medium-sized programming projects, we will investigate code construction techniques, debugging and profiling tools, testing methodologies, UML, principles of object-oriented design, design patterns, and user interface design.

  • CS 294 CS Tea Colloquium

    Students earn credit by attending at least five of the research-based events in the Computer Science department’s weekly colloquium series. Speakers come from academia, industry, nonprofits, and government, and present on a variety of topics, within and adjacent to computer science. Students will submit brief written reports after each talk that they attend.

  • CS 301 History of Computing in England Program: History of Computing

    In the mid-1800s, Charles Babbage’s analytical engine, inspired by programmable looms, was the first conception of an automated programmable computing device. A century later, British researchers built some of the first physical computers—particularly WWII-era code-breaking work, and programmable machines developed immediately after the war. We will explore those two eras, through historical writings (including Babbage and Ada Lovelace, who wrote programs for the analytical engine, and Alan Turing) and visits to relevant museums and archives.  We will also study some of the more recent history of computing, particularly the major advances in the 1960s and 1970s.

    Not offered in 2023-24

    • 6
    • Formal or Statistical Reasoning
    • Computer Science 201 and 202 (Math 236 will be accepted in lieu of Computer Science 202)
  • CS 304 Social Computing

    The last decade has seen a vast increase in the number of applications that connect people with one another. This course presents an interdisciplinary introduction to social computing, a field of study that explores how computational techniques and artifacts are used to support and understand social interactions. We will examine a number of socio-technical systems (such as wikis, social media platforms, and citizen science projects), discuss the design principles used to build them, and analyze how they help people mobilize and collaborate with one another. Assignments will involve investigating datasets from online platforms and exploring current research in the field.

  • CS 311 Computer Graphics

    Scientific simulations, movies, and video games often incorporate computer-generated images of fictitious worlds. How are these worlds represented inside a computer? How are they “photographed” to produce the images that we see? What performance constraints and design trade-offs come into play? In this course we learn the basic theory and methodology of three-dimensional computer graphics, including both triangle rasterization and ray tracing. Familiarity with vectors and matrices is recommended but not required.

    Not offered in 2023-24

  • CS 312 Audio Programming

    Students will learn the basics of MIDI and Digital Audio programming using C++. In the MIDI portion of the course, you’ll learn to record, play, and transform MIDI data. During the Digital Audio portion of the course, you’ll learn the basics of audio synthesis: oscillators, envelopes, filters, amplifiers, and FFT analysis. Weekly homework assignments, two quizzes, and two independent projects.

    Not offered in 2023-24

  • CS 314 Data Visualization

    Understanding the wealth of data that surrounds us can be challenging. Luckily, we have evolved incredible tools for finding patterns in large amounts of information: our eyes! Data visualization is concerned with taking information and turning it into pictures to better communicate patterns or discover new insights. It combines aspects of computer graphics, human-computer interaction, design, and perceptual psychology. In this course, we will learn the different ways in which data can be expressed visually and which methods work best for which tasks. Using this knowledge, we will critique existing visualizations as well as design and build new ones.

  • CS 318 Computational Media

    How does computation enable new forms of creative expression? What kinds of media artifacts and experiences can only happen on computers? In this course, we’ll explore these notions through a hands-on survey of various forms of computational media, such as: computer simulation, computer-generated visual art, poetry generation, story generation, chatbots, Twitter bots, explorable explanations, and more. For each topic in the survey, students will learn about the past, present, and future of a given form through short readings and direct engagement with major works. Assignments and a final project will center on the creation of novel media artifacts and also reimplementations of lost or defunct historical programs.

    Not offered in 2023-24

  • CS 320 Machine Learning

    What does it mean for a machine to learn? Much of modern machine learning focuses on identifying patterns in large datasets and using these patterns to make predictions about the future. Machine learning has impacted a diverse array of applications and fields, from scientific discovery to healthcare to education. In this artificial intelligence-related course, we’ll both explore a variety of machine learning algorithms in different application areas, taking both theoretical and practical perspectives, and discuss impacts and ethical implications of machine learning more broadly. Topics may vary, but typically focus on regression and classification algorithms, including neural networks.

  • CS 321 Making Decisions with Artificial Intelligence

    There are many situations where computer systems must make intelligent choices, from selecting actions in a game, to suggesting ways to distribute scarce resources for monitoring endangered species, to a search-and-rescue robot learning to interact with its environment. Artificial intelligence offers multiple frameworks for solving these problems. While popular media attention has often emphasized supervised machine learning, this course instead engages with a variety of other approaches in artificial intelligence, both established and cutting edge. These include intelligent search strategies, game playing approaches, constrained decision making, reinforcement learning from experience, and more. Coursework includes problem solving and programming.

    Not offered in 2023-24

  • CS 322 Natural Language Processing

    Computers are poor conversationalists, despite decades of attempts to change that fact. This course will provide an overview of the computational techniques developed in the attempt to enable computers to interpret and respond appropriately to ideas expressed using natural languages (such as English or French) as opposed to formal languages (such as C++ or Lisp). Topics in this course will include parsing, semantic analysis, machine translation, dialogue systems, and statistical methods in speech recognition.

    Not offered in 2023-24

  • CS 330 Introduction to Real-Time Systems

    How can we prove that dynamic cruise control will brake quickly enough if traffic suddenly stops? How must a system coordinate processes to detect pedestrians and other vehicles to ensure fair sharing of computing resources? In real-time systems, we explore scheduling questions like these, which require provable guarantees of timing constraints for applications including autonomous vehicles. This course will start by considering such questions for uniprocessor machines, both when programs have static priorities and when priorities can change over time. We will then explore challenges introduced by modern computers with multiple processors. We will consider both theoretical and practical perspectives.

    Not offered in 2023-24

    • 6
    • Formal or Statistical Reasoning
    • Computer Science 200 or 201 and Computer Science 202 (Mathematics 236 will be accepted in lieu of Computer Science 202).
  • CS 331 Computer Networks

    The Internet is composed of a large number of heterogeneous, independently-operating computer networks that work together to transport all sorts of data to points all over the world. The fact that it does this so well given its complexity is a minor miracle. In this class, we’ll study the structure of these individual networks and of the Internet, and figure out how this “magic” takes place. Topics include TCP/IP, protocols and their implementations, routing, security, network architecture, DNS, peer-to-peer networking, and Wi-Fi along with ethical and privacy issues.

  • CS 332 Operating Systems

    If you’re working in the lab, you might be editing a file while waiting for a program to compile. Meanwhile, the on-screen clock ticks, a program keeps watch for incoming e-mail, and other users can log onto your machine from elsewhere in the network. Not only that, but if you write a program that reads from a file on the hard drive, you are not expected to concern yourself with turning on the drive’s motor or moving the read/write arms to the proper location over the disk’s surface. Coordinating all this hardware and software is the job of the operating system. In this course we will study the fundamentals of operating system design, including the operating system kernel, scheduling and concurrency, memory management, and file systems.

  • CS 334 Database Systems

    Database systems are used in almost every aspect of computing, including managing data for websites and apps, but also large-scale data science archives. Why, and how? This course takes a multi-pronged approach. From a systems perspective, we will look at the low-level details of how a database system works internally, studying data storage, indexing, and query optimization. From a theory perspective, we will examine ideas such as normal forms and relational algebra. From a utilization perspective, we will look at how query languages such as SQL interface with the database system, and understand how SQL queries really work.

  • CS 338 Computer Security

    When hackers can disable gas pipelines, national hospital systems, and electrical grids, and data brokers can create a largely unregulated world-wide surveillance system, there’s a clear need for people who understand the mechanisms of computer security and insecurity. Towards that end, in this course we will study technical and social aspects of computer and network security. Topics will include threat modeling, cryptography, secure network protocols, web security, ethical hacking and penetration testing, authentication, authorization, historical hacking incidents, usability, privacy, and security-related law.

  • CS 341 History of Computing in England Program: Cryptography

    Modern cryptographic systems allow parties to communicate in a secure way, even if they don’t trust the channels over which they are communicating (or maybe even each other). Cryptography is at the heart of a huge range of applications: online banking and shopping, password-protected computer accounts, and secure wireless networks, to name just a few. In this course, we will introduce and explore some fundamental cryptographic primitives. Topics will include public-key encryption, digital signatures, code-breaking techniques (like those used at Bletchley Park during WWII to break the Enigma machine’s cryptosystem), pseudorandom number generation, and other cryptographic applications.

    Not offered in 2023-24

  • CS 344 Human-Computer Interaction

    The field of human-computer interaction addresses two fundamental questions: how do people interact with technology, and how can technology enhance the human experience? In this course, we will explore technology through the lens of the end user: how can we design effective, aesthetically pleasing technology, particularly user interfaces, to satisfy user needs and improve the human condition? How do people react to technology and learn to use technology? What are the social, societal, health, and ethical implications of technology? The course will focus on design methodologies, techniques, and processes for developing, testing, and deploying user interfaces.

  • CS 347 Advanced Software Design

    This course helps students to strengthen their ability to design modular, extensible and maintainable software. The focus of the course is on the design of modern cloud applications. Students will learn how to decompose complex applications into a set of back-end services, develop and debug these services, and deploy them in the cloud. This class is structured around a large project that will be extended over the course of the term.

  • CS 348 Parallel and Distributed Computing

    As multi-core machines become more prevalent, different programming paradigms have emerged for harnessing extra processors for better performance. This course explores parallel computation for both shared memory and distributed parallel programming paradigms. In particular, we will explore how these paradigms affect the code we write, the libraries we use, and the advantages and disadvantages of each. Topics will include synchronization primitives across these models for parallel execution, debugging concurrent programs, fork/join parallelism, example parallel algorithms, computational complexity and performance considerations, computer architecture as it relates to parallel computation, and related theory topics.

    Not offered in 2023-24

  • CS 352 Advanced Algorithms

    A second course on designing and analyzing efficient algorithms to solve computational problems. We will survey some algorithmic design techniques that apply broadly throughout computer science, including discussion of wide-ranging applications. A sampling of potential topics: approximation algorithms (can we efficiently compute near-optimal solutions even when finding exact solutions is computationally intractable?); randomized algorithms (does flipping coins help in designing faster/simpler algorithms?); online algorithms (how do we analyze an algorithm that needs to make decisions before the entire input arrives?); advanced data structures; complexity theory. As time and interest permit, we will mix recently published algorithmic papers with classical results.

    Not offered in 2023-24

  • CS 358 Quantum Computing

    Quantum computing is a promising technology that may (or may not) revolutionize computer science over the next few decades. By exploiting quantum phenomena such as superposition and entanglement, quantum computers can solve problems in a fundamentally different way from that of conventional computers. This course surveys the computer science and mathematics of quantum algorithms, including Shor’s and Grover’s algorithms, error correction, and cryptography. No prior experience with quantum theory is needed.

    Not offered in 2023-24

    • 6
    • Formal or Statistical Reasoning
    • Computer Science 200 or 201, Mathematics 232, and Computer Science 202 (Mathematics 236 will be accepted in lieu of Computer Science 202)
  • CS 361 Artificial Life and Digital Evolution

    The field of artificial life seeks to understand the dynamics of life by separating them from the substrate of DNA. In this course, we will explore how we can implement the dynamics of life in software to test and generate biological hypotheses, with a particular focus on evolution. Topics will include the basic principles of biological evolution, transferring experimental evolution techniques to computational systems, cellular automata, computational modeling, and digital evolution. All students will be expected to complete and present a term research project recreating and extending recent work in the field of artificial life.

    Not offered in 2023-24

  • CS 362 Computational Biology

    Recent advances in high-throughput experimental techniques have revolutionized how biologists measure DNA, RNA and protein. The size and complexity of the resulting datasets have led to a new era where computational methods are essential to answering important biological questions. This course focuses on the process of transforming biological problems into well formed computational questions and the algorithms to solve them. Topics include approaches to sequence comparison and alignment; molecular evolution and phylogenetics; DNA/RNA sequencing and assembly; and specific disease applications including cancer genomics.

    • Winter 2024
    • 6
    • Formal or Statistical Reasoning
    • Computer Science 200 or 201 and Computer Science 202 (Mathematics 236 will be accepted in lieu of Computer Science 202)
  • CS 399 Senior Seminar

    As part of their senior capstone experience, majors will work together in teams (typically four to seven students per team) on faculty-specified topics to design and implement the first stage of a project. Required of all senior majors.

  • CS 400 Integrative Exercise

    Beginning with the prototypes developed in the Senior Seminar (CS 399), project teams will complete their project and present it to the department. Required of all senior majors. Each CS 400 is paired with a particular section of CS 399, and the prerequisite for CS 400 must be filled by satisfactory completion of that CS 399.