Computer Science

(Mathematics, Statistics, and Computer SCIENCE)

http://www.stolaf.edu/depts/cs/

Director, 2013-14: Olaf Hall-Holt (Mathematics, Statistics and Computer Science), computational geometry, computer graphics and vision

Faculty, 2013-14: Richard J. Allen (Mathematics, Statistics and Computer Science), bioinformatics, programming languages, intelligent tutoring systems; Richard A. Brown (Mathematics, Statistics and Computer Science), parallel and distributed computing systems,interdisciplinary applications of computer science; Charles W. Huff, Jr. (Psychology), social psychology, ethical and social issues in computing; Steven C. McKelvey (Mathematics, Statistics and Computer Science), operations research, wildlife modeling; Matthew P. Richey (Mathematics, Statistics and Computer Science), computational mathematics, software engineering

Computer science (CS) is the academic discipline that focuses on creative computing-related problem solving and develops valuable analytical skills. St. Olaf’s CS program employs “hands-on” personal experience to build up valuable technical skills while learning powerful computing concepts in a liberal arts context. Beginning with the introductory courses, the program’s curriculum draws connections with applications in other disciplines ranging from the natural sciences to the humanities. The program offers an authentic and satisfying education in the concepts and practices of computer science; the courses below represent national expectations for an undergraduate computer science curriculum. Undergraduate research appears throughout the program, from foundation courses that develop valuable project skills to advanced courses such as the senior capstone seminar. St. Olaf is a national leader in incorporating instruction in parallel and distributed computing throughout the curriculum. The program also incorporates a distinct liberal arts perspective, including emphasis on teamwork and communication skills, examination of ethical and social issues in computing, and collaboration in upper-level interdisciplinary projects.

Although CS differs from other computing areas, such as programming, information technology (IT), and information systems (IS), the study of computer science serves as excellent preparation for careers in such fields, because the concepts of computer science provide insights into all forms of computing. While specific computing systems come and go, the principles of CS endure for the long term, and persons with awareness of those principles perceive them in all forms of computing. The program's emphases on development of creative problem-solving and analytical thinking abilities, interpersonal skills, and ethical analysis and awareness enhance any career, whether one becomes a computing professional or an occasional user.

overview of the major

The CS Program emphasizes the concepts and practices of computer science, as well as applications to other disciplines. The major begins with foundation courses that present the nature of CS through hands-on experience. The foundation and subsequent core courses together span the national expectations for an undergraduate computer science major curriculum, and advanced courses and electives provide options for depth. Several themes appear throughout the major: breadth-first introductory courses; team collaboration (often interdisciplinary); development of communication skills; thoughtful, structured analysis of ethical and social issues in computing; and undergraduate research, beginning with project skills in early courses and continuing through advanced experiences such as CS 390: Senior Capstone Seminar.

INTENDED LEARNING OUTCOMES FOR THE MAJOR

REQUIREMENTS FOR THE MAJOR

A student arranges for a computer science major by individual contract with the computer science faculty. This provides some latitude for choice according to individual interests and background and allows the computer science faculty to update the curriculum easily as the field of computer science evolves. Most contracts adhere to the guidelines below, which derive from prominent national recommendations for undergraduate computer science majors.

Foundation courses: one of Computer Science 121 or 125; Computer Science 241, 251, and 252; one of Computer Science 231 or Math 232 or Math 252.

Core courses: Computer Science 253; Computer Science 263; either Computer Science 276 or 333; and either Computer Science 273, 284, or 300 with parallel and distributed computing.

Electives and capstone: Computer Science 390 and two approved electives.

These guidelines represent four levels:

  • choice of introductory courses (Computer Science 121 or 125) and three “second courses” (Computer Science 231, 241, 251) that may be taken in any order (note that the foundation courses offer non-majors a variety of one- to four-course samples of computer science);
  • core courses in standard computer science topics, including algorithms and data structures, ethics of computing, computer languages, and computer systems;
  • a deeper exposure to selected aspects of the discipline of computer science through electives; and
  • a senior-level capstone integrative experience (Computer Science 390).

DISTINCTION

SPECIAL PROGRAMS

Certain courses in computer science count toward a concentration in linguistics and toward majors in mathematics and biology. Also, certain courses in mathematics and physics can count toward requirements in a computer science major contract. Speak with the computer science program director for details.

The computer science program offers many opportunities to participate in undergraduate research, often integrated within courses, or through extracurricular activities such as summer research. Ongoing efforts in high-performance cluster and parallel computing, graphics, 3D computer vision, and declarative approaches to language design provide a foundation for many student projects.

Collaborative interdisciplinary projects apply computer science to many fields across campus, including environmental studies, linguistics, management studies, physics, and history.

RECOMMENDATIONS FOR GRADUATE STUDY

Students considering graduate study in computer science should pursue opportunities that add both breadth and depth in their majors. Standard course offerings provide breadth, in that taking all foundation and core courses plus Computer Science 336 collectively includes material from all 63 required units in the current ACM/IEEE national curricular recommendations, plus over 20 optional units. Graduate-school-bound students are strongly encouraged to pursue undergraduate research involving computer science, and to take courses beyond the minimal major requirements.

COURSES

Courses in computer science satisfy the following general education requirements: AQR, WRI, ORC, IST, and EIN. See the Class and Lab Schedule for details. At most one of the two introductory courses, Computer Science 121 and 125 may be taken for credit.

117 Gateways to Computer Science

Students engage fundamental concepts and diverse capabilities of computing and also explore the many layers of computing, including information, hardware, programming, application, and communication layers. The course focuses on algorithmic thinking and the representation and analysis of algorithms. Students investigate ideas through technical and non-technical reading introducing them to computer science and programming. The course is intended for all students. Does not count toward the computer science major.

121 Principles of Computer Science

This course introduces students to computer science (CS), a field devoted to creative problem solving with computers. Students explore fundamental concepts, including recursion, iteration, object-oriented software design, algorithm efficiency, levels of naming, computer design, and computing ethics. Students apply these concepts daily, and produce applied projects, individually and in teams. Special application areas include digital humanities. Offered each semester.

125 Computer Science for Scientists and Mathematicians

This course focuses on handling data: visualization, finding patterns, and communicating with data. Exploration of fundamental concepts, including recursion, iteration, algorithm efficiency, loops, decision structures, encapsulation, and computing ethics. Students work individually and in teams to apply basic principles and structures to create programs that model graphical, mathematical, and scientific processes. Prerequisite: Calculus or consent of the instructor.

231 Mathematical Foundations of Computing

Students learn mathematical topics that form an essential background for the study of computer science, including predicate calculus and formal reasoning, elementary number theory, methods of proof, mathematical induction, probability, recursion, efficiency of algorithms, graphs, trees, regular expressions, and automata. Prerequisites: Mathematics 120 or Computer Science 121 or Computer Science 125 or permission of instructor. Alternates annually with Mathematics 232. Counts toward neuroscience concentration.

241 Hardware Design

This course offers students a structured descriptive survey of the organizational principles of computer hardware, emphasizing trade-offs among architectural choices and representative examples. Programming exercises explore how these topics relate to fine contrasting programming languages. Topics include virtual machines, overview of computer organization, forms of parallelism, machine-level implementation of programming language features, memory organization, digital logic, microprogrammed and RISC architectures, multi-core architectures, performance enhancements, assembly programming, and architecture of networks and their protocols. Prerequisite: Computer Science 121 or Computer Science 125 or consent of the instructor. Offered annually.

251 Software Design and Implementation

This course provides an introduction to the structure and creation of computer software, using the C++ programming language and emphasizing object-oriented programming and software life-cycle methodology. Concepts and skills are applied in a team project based on the waterfall model of software development. Topics include object-oriented programming, specification, high-level memory management, indirect addressing, formal methods, tools including UML, team software process, requirements analysis, software design strategies, and elementary ethical analysis of software systems. Prerequisite: Computer Science 121 or Computer Science 125 or Computer Science 241 or Physics 130 or Math 252 or permission of instructor. Concurrent registration in Computer Science 252 is required. Offered both semesters.

252 Software Design and Implementation Lab

Students investigate the implementation of software using strategies and concepts presented in Computer Science 251, explore standard technologies for creation and management of multi- module software systems, and carry out stages of a life cycle-based team software project, through hands-on computational exercises and with direct support provided in a small group context. Prerequisite: concurrent registration in Computer Science 251. Offered both semesters.

253 Algorithms and Data Structures

This course surveys standard algorithms and data structures with emphasis on implementation experience and complexity analysis. Topics include algorithmic strategies, fundamental computer algorithms, stacks, queues, lists, trees, hash tables, heaps and priority queues, compression, and decompression. Prerequisites: Computer Science 231 (or Math 232 or Math 252) and Computer Science 251, or consent of the instructor. Math 220 is strongly recommended. Offered annually. Counts toward neuroscience concentration.

263 Ethical Issues in Software Design

The software we design has real effects in people's lives. This course explores the ethical and social considerations inherent in computer-based systems, develops skills in thinking about those considerations and in collecting data to determine their effects, and expands students' abilities to integrate these issues and skills into software development procedures, largely through an extensive team analysis of a "live" software project. Coursework uses extended case studies and surveys topics such as professional and ethical responsibilities, risk, liability, intellectual property, privacy, and computer crime. Prerequisites: Computer Science 251 and completion of BTS-T, or permission of instructor. Offered annually.

273 Operating Systems

This course examines the features of modern operating systems, including detailed consideration of Linux and other example systems. Projects range from system-level programming to kernel modifications. Topics include operating system principles, implementation as system calls, process scheduling and dispatch, inter-process communication, low-level memory management, device management, file systems, security and protection mechanisms, and scripting. Prerequisites: completion of or concurrent enrollment in Computer Science 241 and Computer Science 251, or permission of instructor. Offered alternate years.

276 Programming Languages

Students study features commonly found in computer programming languages and construct their own interpreters for an example programming language incorporating various language features they study throughout the course. Topics include programming language semantics, programming language translation, parsing, implementation of control structures and memory structures, abstraction mechanisms, and language translation systems and types. Prerequisites: Computer Science 241 and Computer Science 251, or permission of instructor. Offered alternate years.

284 Mobile Computing Applications

Mobile devices will soon overtake desktop computers as the most common way to connect to the Internet. This course explores mobile computing technology, including the creation of applications for the Android platform, including a final team project. The course introduces Java language and provides exposure to graphics user interfaces (GUIs), event-driven programming, APIs, databases, SQL query language, and agile team programming methodologies. Prerequisite: Computer Science 251 or permission of the instructor. Offered alternate years.

294 Internship

This is an intermediate-level version of Computer Science 394.

298 Independent Study

300 Topics in Computer Science

Recent and planned topics include parallel and distributed computing, computer graphics, relational database systems, and real-time systems. May be repeated if topics are different. Offered alternate years.

315 Bioinformatics

Students study computational problems arising from the need to store, access, transform, and utilize DNA-related data. Topics from computer science include: exhaustive search; algorithms (including dynamic programming, divide-and-conquer, graph and greedy algorithms) for fragment reassembly, sequence alignment, phylogenetic trees; combinatorial pattern matching; clustering and trees; and hidden Markov models. Prerequisites: Computer Science 253, or one of Computer Science 121 or 125 and one of Biology 125 or Mathematics 220, or permission of instructor. Offered alternate years. Counts toward neuroscience concentration.

333 Theory of Computation

Students learn about formal languages, automata, and other topics concerned with the theoretical basis and limitations of computation. The course covers automata theory including regular languages and context-free languages, computability theory, complexity theory including classes P and NP, and cryptographic algorithms. Prerequisite: Computer Science 231 or Mathematics 244 or 252, or permission of instructor. Offered alternate years. Counts toward neuroscience concentration.

336 Logic Programming

Students learn a widely-used style of programming based on first order predicate logic. Topics include declarative programming, Horn clauses, declarative and procedural semantics of logic programs, clauses as relations, goals, backtracking, and resolution. Programming projects and exercises use Prolog, the most significant logic programming language. Additional topics include the relationship of Prolog to logic and applications to artificial intelligence. Prerequisite: Computer Science 251 or permission of instructor. Offered alternate years.

350 Advanced Team Project

This course is devoted to team research/development projects, employing established software development methodologies. Projects frequently have an interdisciplinary nature, involving consultation with faculty and/or students in other fields, and taking advantage of the particular backgrounds of team members. One or more research papers, posters, etc., on results are ordinarily expected. Prerequisites: One core course with implementation in computer science or permission of instructor. Offered alternate years.

Mathematics, Statistics, and Computer Science 389 Research Methods (0.5 credit)

Students focus on writing scientific papers, preparing scientific posters, and giving presentations in the context of a specific, year-long, interdisciplinary research project. In addition, this weekly seminar series builds collaborative research skills such as working in teams, performing reviews of math, statistics, and computer science literature, consulting effectively, and communicating proficiently. Exposure to post-graduate opportunities in math, statistics, and computer science disciplines is also provided. Open to students accepted into the Center for Interdisciplinary Research

390 Senior Capstone Seminar

Class members participate in undergraduate research, including readings from the research literature, team development of project software, ethical analysis of their project applying Computer Science 263 principles, and writing a research paper for public presentation. Projects are frequently interdisciplinary in nature, and build on prior undergraduate research experiences among class members. Prerequisites: major in computer science with senior standing, and completion of or concurrent enrollment in computer science core courses, ordinarily including Computer Science 263, or permission of instructor. Offered annually.

394 Internship

Students gain experience in computer-industry positions. Projects have included contributions to team programming, implementation of solid modeling (NURBS), documentation, business applications of computing, and applications of computer graphics in medical research. Internship experiences (whether for credit or not) are strongly encouraged for anyone considering a career in computing.

396 Directed Undergraduate Research: "Topic Description"

This course provides a comprehensive research opportunity, including an introduction to relevant background material, technical instruction, identification of a meaningful project, and data collection. The topic is determined by the faculty member in charge of the course and may relate to his/her research interests. Prerequisite: determined by individual instructor. Offered based on department decision. See also Computer Science 350 and Computer Science 390. May be offered as a 1.00 credit course or .50 credit course.

398 Independent Research

Recent projects, usually executed by individuals and coordinated with ongoing undergraduate research projects, include cluster-assisted computer vision for robots, parallel computing in computer science education, neural networks, applications of genetic algorithms to scheduling, computational Bayesian image reconstruction, and design of software to assist choreographers.