Advancing knowledge from diverse and rigorous courses.
CS128 Programming and Problem Solving (3 credits)
An introduction to computers, computer science and programming with an emphasis on problem analysis and algorithmic solutions.
CS130 Symbolic Logic (3 credits)
The study of formal, deductive logic emphasizing the methods for demonstrating the validity of arguments. Includes truth functional propositional logic and quantification theory through the logic of relations. Also listed as MATH 130 and PHIL 130.
CS 195 Math Toolkit (2 credits)
An introduction to the principal topics in mathematics needed by a Computer Science major, and intended for students of CS. Topics include writing numbers in various bases, set theory, proof by induction, relations and functions, logic, matrices, complex numbers, recursion and recurrences, and rates of growth of various functions. Also listed as MATH 195.
CS 256 Data Structures (4 credits)
A systematic introduction to the methodology of problem solving with computers. Emphasizes the design and development process, data abstraction and fundamental data structures, programming for reuse and the development of large programs. Introduces the basic notions of software engineering and analysis of algorithms. Discusses ethical issues in computing. Prerequisite: CS 128. Co-Requisite: CS 195.
CS 266 Computing Skills (1 credit)
(3) Batch computing and scheduling (4) GNU auto-tools
Additional modules from which students could choose as their second module include: (1) Tools for data analysis and visualization (2) Tools for natural language processing (3) Cybersecurity tools (4) Learn a programming environment
Prerequisite: CS128 or enrollment in 3-2 engineering. Registration for this class requires the consent of the instructor.
CS 281 Applied Groups (0-1 credit)
Limited to members of the CS Applied Groups. Working under the direction of a faculty or staff member, groups of CS students provide infrastructure support for the CS Department and the College. Current groups include: CS System and Network Administrators, Hardware Interfacing Project, CS Content Administration Group, Pedagogical Tools Group, Database Integration Group (WebDB) and Green Science Group. No more than three credits total in an academic career. Prerequisite: Consent of the instructor.
CS 282 Web Application Development (3 credits)
CS 290 Computational Modeling (3 credits)
Designed for students majoring in any of the natural sciences. An introduction to the tools and techniques of interdisciplinary computationally based research in the natural sciences. Computational research uses computers to simulate laboratory experiments or to perform experiments which have no laboratory analog. Lab exercises come from a variety of disciplines. Recommended prerequisites: CS 128 or a lab science course.
CS 310 Algorithms (3 credits)
A study of algorithms and the data structures on which they are based, with a focus on the analysis of their correctness and complexity in terms of running time and space. Prerequisites: MATH 180, MATH 190 and CS 256.
CS 320 Principles of Computer Organization (3 credits)
An introduction to the structure and function of computing machines. The concept that computing machines consist of layers of virtual machines is an organizing principle. Topics include information representation, automata, assembly language programming, register machines, microprogramming, conventional machines and language processors. Prerequisite: CS 310.
CS 330 Functional Programming (3 credits)
An introduction to Functional Programming, one of the three major programming paradigms. Focuses on well-structured interactive program development using a modern functional programming language. Introduces the formal study of data types and the meaning of programs. Prerequisite: CS 256.
CS 335 Advanced Data Structures (3 credits)
Data structures are a central topic in computer science. Building on the material developed in CS 256 Data Structures, this course covers more advanced approaches to organizing databased on network, tree and string based structures. Problems are chosen from data-intensive domains, motivating students to solve complex problems by using efficient data structures. Prerequisite: CS 256.
Introduces computer science tools and techniques that support computational science and high performance computing. Computational methods are an integral part of modern science, including multidisciplinary research into climate change, the origins of the universe and the underlying cause of diseases such as Alzheimer’s. Topics include scientific libraries and kernels, parallel distributed and grid resources, and the principle software patterns found in this domain. Prerequisites: CS 310 or consent of the instructor.
The theory, techniques and technologies associated with the design, construction, and testing of software systems, particularly large software systems. Students learn various approaches to procedural decomposition and system architecture. Explores the tools used for building and testing software systems, particularly in the context of open source software. Prerequisite: CS 310.
A laboratory-oriented course dealing with analog and digital circuits. Circuit theory is developed for diodes, transistors, operational amplifiers and simple digital circuits. Components are used to construct a range of devices, including power supplies, oscillators and amplifiers. Lab. Prerequisite: PHYS 230 or 235. Also listed as PHYS 350.
The application of parallel programming and problem-solving techniques to solve computationally intensive problems in a variety of disciplines. Parallel computation invites new ways of thinking about problems and is an increasingly important skill in corporate and research environments. Students learn about programming paradigms used in parallel computation, the organization of parallel systems, and the application of programs and systems to solving problems in mathematics, physics, chemistry and other areas. Prerequisite: CS 310.
An introduction to computer graphics with an emphasis on Open-GL and the mathematical foundations of modeling and rendering. Experientially oriented with frequent small projects. Requires good coding skills in C++ or, with considerably more work, C. Mathematical aspects based in Linear Algebra. Prerequisite: CS 256 or consent of the instructor.
A study of computability and non-computability from a perspective that views the problems to be solved as formal languages. Study of automata-theoretic (finite state automata, pushdown automata and Turing machines) and generative (regular languages, regular, context-free and unrestricted phrase structure grammars) mechanisms along with the properties of the classes of languages they can define. Prerequisite: CS 310 or consent of the instructor.
CS 383 Bioinformatics (4 credits)
Bioinformatics is the application of statistics and computer science to the field of biology. This course is a wide ranging introduction to the field, the tools, and the techniques used to work with large datasets, and will principally concentrate on the analysis and visualization of novel genomic and metagenomic data. The course is centered around doing research and using tools, with much of the course time dedicated to active learning. Prerequisite: BIOL 111, 112, CS 128 or CS 290. Also listed as BIOL 383.
CS 410 Networks and Networking (3 credits)
A study of the hardware and software technology and standards which support local area networks, wide area networks and the Internet. Emphasizes the TCP/IP protocol suits and the associated tools that provide universal connectivity to a wide variety of systems around the world. Explores the network hierarchy, from the physical level (transmission media) up through client/server applications such has HTTP servers and the domain name system. Prerequisite: CS 320.
CS 420 Operating Systems (3 credits)
A study of the software that manages the hardware and provides the interface between application programs and system resources. Topics include scheduling, memory management, persistent storage, resource contention, locking and multi-processor synchronization. Using open source software, students explore a production quality operating system and learn by modifying it. Prerequisite: CS 320.
CS 430 Database Systems (3 credits)
An introduction to database management systems. Database design and development are viewed from the perspective of a user, an application program and the database kernel itself. Focuses primarily on relational and object-oriented data models and related software. Prerequisite: CS 256. Co-Requisite: CS 310.
CS 440 Programming Languages (3 credits)
The nature of programming languages and the programs that implement them. Focuses on the abstract structures programming languages provide for expressing algorithms and the methods by which they are realized on concrete hardware. Prerequisite: CS 256. Co-Requisite: CS 310.
CS 481 Internships, Field Studies and Other Field Experiences (1-3 credits)
CS 482 Special Topics (3 credits)
Recent offerings include robotics, visualizing scientific data, Android app development, hardware interfacing with Arduino, and tech for social good.
CS 483 Teaching Assistants (1-3 credits)
CS 484 Ford/Knight Research Project (1-4 credits)
Collaborative research with faculty funded by the Ford/Knight Program.
CS 485 Independent Study (1-3 credits)
Investigation of a specific topic conceived and planned by the student in consultation with a faculty supervisor. Culminates in a comprehensive report prepared in the style of a thesis or research paper.
CS 486 Student/Faculty Research
CS 488 Senior Capstone Experience (3 credits)
Each participant completes a semester-long capstone project in a research group setting. Weekly meetings with the instructor individually and with the group as a whole. In addition, explores topics from the cultural, ethical, historical or broader scientific context of computer science in readings and discussion. Culminates in a public seminar and student presentation. Prerequisite: Consent of the instructor.
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