CSAS - Computer Science (CSAS)

General overview of the many facets of computer science and information technology: Data, hardware, software, networks. System software in including operating systems and programming environments. Software engineering; program development using data structures, algoriths, files, and databases. Exposure to other topics and issues in computer science, such as data compression, security, theory of computation, computational complexity. Prerequisites: MATH 0012 or appropriate placement.

Problem solving using computers. The design and implementation of computer programs. Major areas and issues in computer science including social and ethical concerns. Problem solving and pseudocode. Formal specification and verification. Basic software engineering techniques and software reuse. Data structures. Structured types: arrays, records, files. Objects and methods. Programming in a high-level language, such as C++ or Java. Corequisite: MATH 1015.

Major issues, areas, and applications of computer science. Data structures and algorithms. Linked lists, trees and graphs. Stacks, queues, and heaps. Object-oriented programming. Problem solving and software engineering. Algorithm design, induction, recursion, and complexity. Social, economic, and ethical concerns. Programming in a high-level language, such as C++ or Java. Prerequisite: CSAS 1111. Corequisite: MATH 1501/1401.

A course in programming in C++ with emphasis on applications to the sciences and to numeric algorithms. Basics of software development (variables, control structures, functions), data structures (records, arrays, lists), dynamic structures (pointers, linked lists) and principles of object-oriented programming (fields and methods, classes, inheritance). The course will focus on creating programs for topics of interest in the natural sciences. Corequisite: MATH 1015 or equivalent.

This course is an introduction to systematic program design and computer programming. The course teaches students how to make plans, to organize their thoughts, to pay attention to detail, and to be self-critical. The focus of the course is the design process that leads students from a problem statement and a blank page to a well-organized solution using the development of a large software system such as a video game. Topics include primitive data processing, compound data of finite size processing, compound data of arbitrary size processing, type-driven design, the process of abstraction, and distributed programming. This course assumes no prior computer programming experience.

This course continues the study of program design started in CSAS 1114. Building on the abstraction skills acquired in CSAS 1114, the course focuses on new programming design techniques such as generative recursion, accumulator recursion, tail-recursion, and mutation, and includes distributed programming. The disciplined introduction to mutation prepares students to study modern object-oriented design and programming. The course includes a major team project such as the development of a video game.

This course is an introduction to object-oriented design and programming, with a commercial programming language such as Java. Building on the knowledge gained in CSAS 1114-CSAS 1115, students learn to design a system of classes to represent information. Given a system of classes and a piece of information, students will be able to create objects and represent this information with data. Conversely, given an instance of a class in the system, students will be able to interpret this object as information in the real world. Topics include varieties of data, functional methods, and abstraction with classes.

This course continues the investigation of object-oriented design and programming started in CSAS 2123, using a commercial programming language such as Python. Topics include circular objects, imperative methods, abstraction over data definitions, and the use of commercial programming environments for object-oriented programs. By the end of this course, students will have a solid grasp on the principles and practice of object-oriented programming. The course includes a project.

This course introduces the basic design of computing systems: CPU, memory, instruction processing and interrupts, and input and output. In addition, it provides an introduction to assembly language and the compilation process through experience in assembly language programming. During the course, the student will see system calls and interrupt-driven programming emphasizing the interaction with the operating system. Other topics include machine representation of integers, characters, floating point numbers, and virtual memory. Overview of modern architectures and parallelism, including the general-purpose use of the Graphics Programming Unit (GPU).

This course discusses data structures such as arrays, stacks, queues, lists, trees, and graphs and the algorithms that manipulate these structures. Algorithm analysis for asymptotic complexity with respect to space and time are introduced. Students will learn essential tools for designing efficient software applications, needed in all application areas of computer science, such as industrial and scientific computation and database management. The course exposes the students to information management in applications. It covers the local and global impacts of computing solutions on individuals, organizations, and society.

This course introduces the foundations of applied data mining. There is a need for extracting useful information from raw data in fields such as social and health sciences, business, the natural sciences and engineering. This course covers the fundamental ideas and algorithms of data mining. Furthermore, it teaches applying data mining techniques in order to extract useful information from data. Standard software for data mining will be used. The course is intended for any student desiring an introduction to data mining.

Signature III course with substantial computer science or related content, typically interdisciplinary and perhaps team-taught, taught on an experimental basis with topics to be determined by the instructor(s) in cooperation with the University Core Curriculum process. See Co-op Adviser.Crosslisted with PSYC 3698 and CORE 3490 Engaging the World

See Co-op Adviser.

The course is an introduction to operating systems, such as Linux, Unix, Windows, and Android, which provide the foundations for executing application software. The course studies computing-based systems at varying levels of abstraction and explains how software and operating systems interface with the underlying computing architectures. The course covers topics such as evolution of microprocessors and operating systems, process control, concurrency of processes and threads, memory management, resource scheduling, I/O management, file storage, and principles and practices of security and privacy in computing. The course exposes the students to networking and communication, and includes a major team project.

Introduction to principles of programming languages and their implementation. Concepts introduced include the implementation of interpreters based on the syntax of a programming language, higher-order functions, recursion, tail-recursion, iteration, and mutation; issues of pure versus impure languages in relation to performance, implementation, and ease of abstraction; environments, parameter passing, and scoping. The course emphasizes programming in a modern programming language, individual and team programming assignments, and a major project that requires integration and application of knowledge and skills acquired in earlier course work.

The course presents an overview of topics in and related to logic, including development of formal logic and an axiomatic first-order logic. It explores the history of mathematics and logic in the Catholic Intellectual and wider Western Traditions, as well as the mutual interactions of mathematics, philosophy and religion. It then considers extensions of first-order logic, and provable limits to knowledge: the three unsolvable problems of Euclidean geometry, and examples from Gödel, Turing, Arrow, quantum physics, and others

Principles of computer and networking. The layered model of a computer network and its implementation. Standard protocols. Applications. Mathematical principles and theory. Team and individual programming projects. Prerequisite: CSAS 2122 or permission of instructor.

This course introduces discrete graphs and their applications, with emphasis on applications. It covers the fundamental structures of and algorithms on discrete graphs, teaching students how to use graph algorithms to extract useful information from graph and network data, how to model complex processes using graph theoretic techniques, and how to investigate and validate resulting models in order to test graph models and make predictions.

Special topics and problems in various branches of computer science. Prerequisites: At least five CSAS courses, including CSAS 2122, or permission of chair.

Prerequisites: At least five CSAS courses, including CSAS 2122, or permission of chair.

Prerequisites: At least five CSAS courses, including CSAS 2122, or permission of chair.

Prerequisites: At least five CSAS courses, including CSAS 2122, or permission of chair.

Prerequisites: At least five CSAS courses, including CSAS 2122, or permission of chair.

Prerequisites: At least five CSAS courses, including CSAS 2122, or permission of chair.

Different definitions of and approaches to artificial intelligence. Problems, problems spaces and search techniques; special emphasis on heuristic search, including hill climbing, best-first search and A*. The role of knowledge and knowledge representation issues. Programming and AI application. Introductory survey paper.

The course introduces formal languages and automata theory through a programming-based approach. The course introduces regular languages, context-free languages, context-sensitive languages, and computability. Topics include finite state automata, regular expressions, and regular grammars; pushdown automata and context-free languages; context-sensitive grammars and Turing machines; determinism and nondeterminism; programming-based construction proofs, issues of complexity including P and NP; and computability issues including undecidable problems such as the halting problem and reduction proofs. The course includes individual programming assignments, team programming projects and a major team project that requires integration and application of knowledge and skills acquired in earlier course work. Course instruction covers the necessary mathematical foundations of automata and computational theory that are not covered by prior courses.

Modern relational databases. Relational algebra, views and queries, normal forms and normalization, tuning and optimization. The entity-relationship model and database design. Overview of other approaches, especially object-oriented databases, data warehouses and data mining, distributed databases and very large applications. Group project, both design and implementation, in an SQL-based environment, such as an SQL Workbench. Prerequisites: CSAS 2121, MATH 1611 or permission of department chair. MATH 2611 recommended.

The software universe and the role of software engineering. Project, process, and product. Approaches to system and software engineering; software architectures, including component-oriented and service-oriented architectures. Traditional and object-oriented approaches to software engineering; the modern approach, modeling languages and patterns; agile and extreme programming. Requirements elicitation and analysis and system specification; risk analysis; use cases. Knowledge management for requirements elicitation and risk analysis. Design of a software system using patterns and incremental iterative refinement. Complementary approaches, including aspects and interfaces with databases. Security and other non-behavioral considerations. Development of an initial prototype.

Design and implementation of a software application. Design patterns and aspects. User and component interfaces. Approaches for software quality assurance: validation and verification, testing, static analysis and model checking. Verification, validation, and testing. Approaches to verification – theorem proving, model checking, and others. Principles and theory of testing; white box and black box testing. Unit, integration, stress, and acceptance tests. Test metrics and test coverage. Testing tools. Maintenance: corrective, preventative, adaptive, and perfective changes. Software configuration management. Technical and business management of large software projects. Technical and business metrics. Cost estimation, scheduling, and staffing – connection to risk analysis. Subcontractors, vendors and collaborators; outsourcing in software projects. Software engineering for web applications and real-time systems.

Computer Graphics Visualization is used throughout society, including science, engineering, enterprises, politics, art, etc., for visualizing data and processes. Visualization is crucial for mining usable information from the ever increasing amounts of data and ever more complex procedural relationships of today’s society. This course introduces the foundations for computer graphics visualization: basics of visual thinking and perception, techniques for visualization, such as maps, time series, trees, graphs, etc., and applications, such as in medical imaging, biochemistry, social sciences, etc. The course also teaches developing visualizations using a standard programming system. Visualizations will be demonstrated using online material, such as Many Eyes or Google Maps. Prerequisite: CSAS 4121 or permission or instructor.

A survey of the field of “Big Data” and its connections to data analysis and visualization, to databases and data warehouses, and to document repositories. Application areas selected from among business, government, science, health, medicine and allied fields, social science, digital humanities, and software engineering, and the characteristic uses and challenges of Big Data in these areas are considered. Techniques including data mining and visualization, programming languages and development processes such as Hadoop, statistical approaches, and implementations such as Telemet. Concerns in development and use, including ethics, security, privacy, and intellectual property, on the one hand, and concurrency and distribution, operating systems, and the cloud, on the other; applications of Big Data in addressing these concerns. Issues in software engineering and collaboration for Big Data applications. Guest lectures and/or on-line presentations by on-campus and off-campus experts in Big Data or application areas.

The course covers algorithms and software frameworks that are used for automating data analysis of big data. The course topics include Python for data science, big data stack, data analytics architecture, MapReduce, Hadoop and case studies such as recommendation engines. The course teaches practical skills in implementing big data analytics using industry-standard software, such as Python and MapReduce, and cloud computing services. Cross-listed with DASC 6911. Prerequisites: DASC 3010 and (MATH 2111 or MATH 2711) and have at least junior level and demonstrate basic Python programming skills (such as CSAS 4124 or ISCI 1117.) 3 credits.