Bachelor of Science
Computer and Information SciencesThe ABET-accredited Computer and Information Science Bachelor of Science degree program focuses on the science behind the coolest gadgets and gear on the market.
Technology at Harrisburg University is more than just computer screens and keyboards. It’s a hands-on study of the complex information systems and peripherals that power our world and shape our future.
Choosing to major in computer and information sciences helps the student gain core knowledge of information sciences in order to integrate business and real-world perspectives, interpersonal and team skills, and the competencies that lead to a career in computer and information sciences. The ability to devise a solution and execute it is at the core of this program. The design of these solutions is what we call “creating a computation,” which involves the integration of a few key design notions of data representation, algorithms, programming, knowledge in systems, data security, and software engineering within one unified framework. The ultimate goal is to enable the student to understand a problem and build a general-purpose computer system from the ground up through a program of study that provides a sound computer information system engineering design experience.
The CAC Accreditation Commission of ABET accredited the Computer and Information Science program in October 2019.
Program Goals
Graduates of the ABET-accredited Bachelor of Science in Computer and Information Sciences program have to:
- Apply theoretical constructs of mathematical analysis and sound reasoning to develop and deploy practical solutions for real-world problems;
- Evaluate computing system requirements sufficient for developing efficient computational solutions for real-world problems;
- Assimilate new methodologies and advances in computer technology in an ever-evolving discipline;
- Master the elements of effective written and oral communication of results to technical and non-technical audiences;
- Work independently and in collaboration with colleagues;
- Live up to the ethical standards of the profession and professional knowledge and skills to contribute to society; and,
- Develop an understanding of the code of ethics of the software industry (social contract); and, therefore a sense of what it means to be a good citizen.
Program Student Outcomes
The Computer Information Sciences program adopted the six ABET student learning outcomes (SLO) for framing the program’s curriculum development and decisions.
| SLO 1: | Analyze a complex computing problem and to apply principles of computing and other relevant disciplines to identify solutions. |
| SLO 2: | Design, implement, and evaluate a computing-based solution to meet a given set of computing requirements in the context of the program’s discipline. |
| SLO 3: | Communicate effectively in a variety of professional contexts. |
| SLO 4: | Recognize professional responsibilities and make informed judgements in computing practices based on legal and ethical principles. |
| SLO 5: | Function effectively as a member or leader of a team engaged in activities appropriate to the program’s discipline. |
| SLO 6: | Apply computer science theory and software development fundamentals to produce computing-based solution. |
Five-Year Accelerated Bachelor of Science/Master of Science
HU’s Five-Year Accelerated BS/MS options in Computer & Information Sciences are designed to result in a baccalaureate degree in Computer and Information Sciences and a master of science degree in Information Software Engineering and Management. The student who demonstrates exceptional academic performance in the undergraduate Bachelor of Science program in Computer and Information Sciences may apply to this accelerated dual-degree program. These programs facilitate the student completing both degree programs in five years rather than the usual six.
Industry Highlights
Program Courses
This program requires a total of 50 semester hours: 35 semester hours from the core courses listed below and 15 semester hours completed in the Individualized Concentration. The semester hour value of each course appears in parentheses( ).
This course introduces the concepts and techniques of computer programming. Emphasis is placed on developing the student’s ability to apply problem-solving strategies to design algorithms and to implement these algorithms in a modern, structured programming language. Topics include fundamental programming constructs, problem solving techniques, simple data structures, Object-Oriented Programming (OOP), program structure, data types and declarations, control statements, algorithm strategies and algorithm development.
This lecture and laboratory course further develops the concepts and techniques of computer programming. Emphasis is placed on structured programming, top-down design, more advanced data structures, and the proper use of the programming language and development tools. Topics include abstract data types (ADTs), sets, records, recursion, problem solving and algorithms, fundamental computing algorithms, searching, introductory sorting, hash tables, basic algorithm analysis, Object-Oriented Programming (OOP), files, linked lists, queues, stacks, and binary trees.
The goal of the course is to teach the design and operation of a digital computer. It serves students in two ways. First, for those who want to continue studying computer architecture, embedded systems, and other low-level aspects of computer systems, it lays the foundation of detailed implementation experience needed to make the quantitative tradeoffs in more advanced courses meaningful. Second, for those students interested in other areas of computer science, it solidifies an intuition about why hardware is as it is and how software interacts with hardware. The subject matter covered in the course includes technology trends and their implications, performance measurement, instruction sets, computer arithmetic, design and control of a datapath, pipelining, memory hierarchies, input and output, and brief introduction to multiprocessors. Offered Semester II (Spring), annually.
This course is a foundation for database design and database security courses. Systems Analysis and Design is a fundamental, active field in which analysts continually learn new techniques and approaches to develop systems more effectively and efficiently. There is a core set of skills that all analysts need to know no matter what approach or methodology is used. All information systems projects move through the four phases of planning, analysis, design, and implementation; all projects require analysts to gather requirements, model the business needs, and create blueprints for how the systems should be built; and all projects require an understanding of organizational behavior concepts like change managememnt and team building. This course captures the dynamic aspects of the field by keeping the student focused on doing Systems Analysis and Design while presenting the core set of skills that the analysts needs to know.
This course covers the basic techniques used to analyze problems and algorithms, including asymptotic, upper/lower bounds, and best/average/worst case analysis. Amortized analysis, complexity, and basic techniques are used to design algorithms (including divide & conquer/greedy/dynamic programming/heuristics, choosing appropriate data structures) and important classical algorithms (including sorting, string, matrix, and graph algorithms). The goal for the student is to be able to apply all of the above to design solutions for real-world problems.
This course provides an introduction to the design and implementation of operating systems. The student is exposed to different operating systems on various computer platforms and is expected to develop a significant operating system programming project in this area. Topics include operating systems principles, computer architecture, concurrency threads, CPU scheduling and dispatching, memory management techniques, computer security and system administration using Windows XP, Unix and Linux.
This course introduces the student to the fundamental concepts in design and implementation of programming languages. We examine the evolution and the theory of language design with respect to syntax and semantics of programming language. Different approaches to programming paradigms and feature inclusions are discussed both with respect to their utility for developers as well as the underlying programming models which enable these features. This course examines the full range of programming models and language paradigms, starting from imperative, functional, logic programming to the latest object-oriented programming languages.
This course presents a study of formal languages and the correspondence between language classes and the automata that recognize them. Formal definitions of grammars and acceptors, deterministic and non-deterministic systems, grammar ambiguity, finite state and push-down automata, and normal forms will be discussed.
This course introduces techniques to evaluate limits and covers continuity, special trigonometric limits, absolute value limits and differentiation of algebraic, trigonometric, and logarithmic functions. The course explores intermediate value theorem, mean value theorem, and extreme value theorem. Other topics for exploration are application and formal definition of derivative average rate of change versus instantaneous rate of change, velocity, and the introduction of the definite integral and its applications. A graphing calculator is required for this course.
As science and engineering disciplines grow so does the use of mathematics; new mathematical problems are encountered, and new mathematical skills are required. In this respect, linear algebra has an essential role in various engineering and scientific disciplines. This course develops the fundamental algebraic tools involving matrices and vectors to study linear systems of equations and Gaussian elimination, linear transformations, orthogonal projection, least squares, determinants, eigenvalues and eigenvectors and their applications. This course develops concrete computational skills along with theoretical considerations.
Discrete Mathematics II builds on the principles introduced in Discrete Mathematics I, with a deeper focus on number theory, relations, and specialized counting techniques. After exploring the foundational elements of number theory and the dynamics of relations, the course addresses the fundamentals of counting and probability. These topics are essential for modeling and solving problems where counting distinct configurations or understanding probabilistic events is required. The course concludes with advance counting techniques, enhancing students’ abilities to tackle complex problems in combinatorics and beyond, preparing them for further studies in computer science and other relevant disciplines.
