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The master’s degree in Computer Information Sciences provides a solid foundation in the fundamental areas of computer science and cybersecurity.

The master’s degree in Computer Information Sciences provides a solid foundation in the fundamental areas of computer science and cybersecurity. The program includes courses to acquaint the student with current advances in the discipline, and their applications in business, health care and other areas. The ability to devise a solution and execute it is the heart of the practice of this program. Designing such solutions requires creating efficient computation, which involves the integration of few key design notions of data representation, algorithms, programming, and knowledge in systems, data security, and software engineering in one unified framework. A graduate of the program is able to integrate business, interpersonal and team skills, and the computational skills that lead to professional employment or pursue a doctoral degree in the field.

Program Goals

Graduates of the Computer Information Sciences graduate program are able to:

Program Concentrations

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Harrisburg University of Science and Technology is a designated National Center of Academic Excellence in Cyber Defense (CAE-CD) through the 2029 academic year.

The National Security Agency (NSA) has recognized the high academic standards of Harrisburg University’s Master of Science in Computer Information Sciences with a concentration in Cybersecurity program of study and its importance to national defense and economic development.

As a CAE-C Community member, Harrisburg University is committed to the long-term success and relevance of our cybersecurity programs and recognizes the crucial role higher education plays in national security and economic development.

Being a Center of Academic Excellence further puts Harrisburg University on the map as a world-class STEM university. HU’s Security Center of Excellence bridges academics and industry and will continue to support the validation of additional academic programs under CAE and NSA framework.

Program Lead

 Abrar  Qureshi, Ph.D.

Abrar Qureshi, Ph.D. Professor and Program Lead of Computer Science & Software Engineering

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Full Time Faculty

Abrar Qureshi, Ph.D.

Professor and Program Lead of Computer Science & Software Engineering

Bruce Young, DIA

Academic Content Lead for SCE, Instructor and Program Lead of Cybersecurity & Information Assurance

Glenn Williams

Instructor of Advanced Manufacturing, AR & Robotics

Khaled Iskandarani

Instructor of Biostatistics & Computational Science

Brian Grey

Instructor of Computer and Information Sciences and Program Lead of CISC Online

Tom Sarihan

Instructor of Computer Science

Corporate Faculty

Program Courses

This program requires a total of 36 semester hours: 15 semester hours from the core courses listed below, 6 semester hours of experiential courses, and 3) 15 semester hours of Concentration courses. The semester hour value of each course appears in parentheses ( ).

CISC 520 – Data Engineering and Mining (3 credits)

Description:This course addresses the emerging issues in designing, building, managing, and evaluating advanced data-intensive systems and applications. Data engineering is concerned with the role of data in the design, development, management, and utilization of complex computing/information systems. Areas of interest include database design; meta knowledge of the strategies and mechanisms for data access, security, and integrity control. Data mining is a rapidly growing field that is concerned with developing techniques to assist managers to make intelligent use of these data repositories. A number of successful applications have been reported in areas such as credit rating, fraud detection, database marketing, customer relationship management, and stock market investments. The field of data mining has evolved from the disciplines of statistics and artificial intelligence.

CISC 525 – Big Data Architectures (3 credits)

Government, academia and industry have spent a great deal of time, effort, and money dealing with increases in the volume, variety, and velocity of collected data. Collection methods, storage facilities, search capabilities, and analytical tools have all needed to adapt to the masses of data now available. Google paved the way for a new paradigm in Big Data, with two seminal white papers describing the Google File System, a distributed file system for massive storage, and MapReduce, a distributed programing framework designed to work on data stored in the distributed file system. This course introduces the student to the concepts of Big Data and describes the usage of distributed file systems and MapReduce programming framework to provide skills applicable to developers and the data scientist in any facet of industry.

CISC 530 – Computing Systems Architecture (3 credits)

Modern computer information systems are ever-increasing in complexity and sophistication. As a result, software engineers must be able to make effective deciions regarding the strategic selection, specification, design, and deployment of information systems. Therefore, this course addresses the topics of architectural design that can significantly improve the performance of computer information systems. The course introduces key architectural concepts, techniques, and guidance to software engineers to enable them to make more effective architectural decisions.

CISC 603 – Theory of Computation (3 credits)

Description:This course contains abstract models of computation and computability theory including formal languages, finite automata, regular expressions, context-free grammars, pushdown automata, Turing machines, primitive recursive and recursive functions, and decidability and un-decidability of computational problems.

CISC 610 – Data Structures & Algorithms (3 credits)

Description:This course is a first-year graduate course in algorithms. Emphasis is placed on fundamental algorithms and advanced methods of algorithmic design, analysis, and implantation. This class overs techniques used to analyze problems and algorithms (including asymptotic, upper/lower bounds, best/average/worst case analysis, amortized analysis, complexity), basic techniques used to design algorithms (including divide and conquer/greedy/dynamic programming/heuristics, choosing appropriate data structures), and important classical algorithms (including sorting, string, matrix, and graph algorithms), and data structures.

GRAD 695 – Research Methodology & Writing (3 credits)

This course guides the student to develop and finalize a selected research problem and to construct a proposal that effectively establishes the basis for either writing a thesis or launching an experiential capstone project. The course provides an overview of strategies for effective problem investigation and solution proposal. Research methodology is studies and applied as part of suggesting a solution to a problem. Writing and formatting techniques are also explored and applied as a communication tool for cataloging the investigation and recommending the solution.

International Admissions

Information for Students who want to come to the U.S.

The University is home to more than 5,000 international students representing 110 countries.

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