Schedule of Classes

An introduction to programming concepts and languages for non-Computer Science (CS) majors. Topics include the structure and design of algorithms, variables, constants, data types, arithmetic operations, selection and repetition structures, functions, input/output, arrays, structures, files, libraries. Students will design, write, test and run computer programs using a modern programming language as the development tool.

Introduces the fundamental concepts of programming from an object-oriented perspective. Topics include simple data types, control structures (if-else loops, switch statements), introduction to array and string data structures, algorithms, debugging and testing techniques, and social implications of computing. The course emphasizes good software engineering principles and practices, breaking the programming process into analysis, design, implementation, and testing, with primary focus on implementation and development of fundamental programming skills.

Introduction to concepts of object-oriented programming with review of control structures and data types and array processing. Introduction to the object-oriented programming paradigm, focusing on the definition and use of classes along with the fundamentals of object-oriented design. Overview of programming principles, simple analysis of algorithms, searching and sorting techniques, and an introduction to software engineering issues.

Advanced programming concepts and languages appropriate to computer science and computer information systems. Topics include dynamic memory management, garbage collection, advanced object-oriented concepts, generic programming, exception handling, recursion, overloading.

Theory of computation and formal languages, grammars, computability, complexity, algorithms, heuristics, and foundations of intelligent systems.

Basics of logic circuit design, modern processor architecture, and assembly language. Overview of principle issues of internal system architecture, including memory, buses, and peripherals.

Fundamentals of operating systems concepts, design, and implementation. Topics include operating system components and structures, process and thread model, mutual exclusion and synchronization, scheduling algorithms, memory management, I/O controls, file systems, and security.

Introduction to the social and professional issues and practices that arise in the context of computing.

Applies the concepts and skills learned by undergraduate computer science majors at Bradley University. Students are required to work on a team on a significant software project.

Applies the concepts and skills learned by undergraduate computer science majors at Bradley University. Students are required to work on a team on a significant software project.

Individual study or research/development project under supervision of a CS&IS faculty member. May be repeated under a different topic once. Repeatable to a maximum of six semester hours.

Introduces the fundamental concepts of programming from an object-oriented perspective with emphasis on advanced programming skills and good software development principles in a closed laboratory setting. Covers topics including object-oriented paradigm, design and programming, fundamental data structures and computing algorithms, and software development principles. If needed, course should be taken during first regular semester at Bradley. Credit for this course does not count towards graduation requirements in any graduate program within the Department of Computer Science and Information Systems.

Design and analysis of algorithms. Dynamic structures maintenance and hashing. Searching, sorting, and traversal. Time and space requirements; simplification; computational complexity; proof theory and testing; NP-hard and NP-complete problems.

Fundamental computer sub-systems: central processing unit; memory systems; control and input/output units. General purpose computing systems design. Examples from existing typical computers.

Introduction to PERL/CGI, XHTML, XML, JavaScript and scripting languages. Web page design and layout. Client and server side development of web applications. Database connectivity, Java Database Connectivity (JDBC).

Gives the necessary background and practice for building intelligent systems using three of the most commercially successful applications of AI: the logical approach (expert systems, fuzzy logic, and fuzzy expert systems), the biological approach (neural networks, evolutionary programming, and genetic algorithms), and the statistical approach (Bayesian networks, belief networks, Markov chain, Hidden Markov models, and statistical and neural-based clustering). Students will have the opportunity to build integrated, hybrid intelligent systems to solve problems in a variety of applications including in the medical domain, financial domain and stock market, and autonomous robotics systems.

Brings together the latest research in statistics, databases, machine learning, and artificial intelligence that are part of the rapidly growing field of knowledge discovery and data mining. Topics covered include fundamental issues, classification and clustering, machine learning algorithms, trend and deviation analysis, dependency modeling, integrated discovery systems, next generation database systems, data warehousing, and OLAP and application case studies.

Relational database design, including entity relationship modeling and normalization. Structured query language (SQL) for creating and querying databases. Other topics include the theory of relational databases, including relational algebra, various loading and reporting utilities, and the implementation of database management systems, e.g., how query optimization works.

Designing and building enterprise-wide data warehouses. Techniques for analyzing data in data warehouses. Study different types of data models including logic and object-oriented databases. Advanced topics in relational databases such as multimedia databases, distributed databases, concurrency, security, etc.

Software engineering: software product; prescriptive process models; system engineering; analysis modeling; design engineering; architectural design; user interface design; testing strategies and techniques; software systems' implementation; software systems' maintenance.

Methods of software project management including systems view and systems methodology, project scope, initiation and planning, management concepts and types of management plans, project metrics and estimates, tools for project management, project reports and documentation. Cross listed with CIS 491 course. For cross listed undergraduate/graduate courses, the graduate level course will have additional academic requirements beyond those of the undergraduate course.

Advanced topics of complex Web-based and mobile software systems: programming methodology, software engineering, components, architectures, services, requirements analysis, design and development models, integrated development environments, testing, quality, platforms. Cross listed with CS 493. For cross listed undergraduate/graduate courses, the graduate level course will have additional academic requirements beyond those of the undergraduate course.

Advanced concepts in operating system design. Topics include process and thread management, virtual memory, interprocess communication, distributed systems, parallel and distributed file system designs, resource management, and security and protection.

Special projects under staff supervision on professional practicum in computer science, with near-term economic benefit. Repeatable to a maximum of 3 credit hours.

Individual study in an area of computer science relevant to the student's professional goals and not covered in a formal course offered by the department. May be repeated twice for a maximum of 6 credit hours.

Computer science research and thesis preparation. Required of candidates choosing the thesis option. Total of 6 semester hrs. to be taken in one or two semesters.