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CPT Eligible

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

Program Lead

 Abrar  Qureshi, Ph.D.

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

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Full Time Faculty

Abrar Qureshi, Ph.D.

Professor of Computer Science & Software Engineering

Bruce Young

Lecturer of Cybersecurity & Information Assurance

Glenn Williams

Lecturer in Advanced Manufacturing, AR & Robotics

Khaled Iskandarani

Lecturer in Biostatistics & Computational Science

Thomas Plunkett

Assistant Professor of Blockchain Technologies

Steve Hernandez, Ph.D.

Corporate Faculty (Computer Information Sciences)

Caleb Druckemiller

Corporate Faculty (Computer Information Sciences)

Ying Lin, Ph.D.

Corporate Faculty (Computer Information Sciences)

Brian Grey

Lecturer in Computer & Information Sciences

Corporate Faculty

Renata McFadden, Ph.D.

Corporate Faculty (Computer Information Sciences)

Ira Weissberger, Ph.D.

Corporate Faculty (Computer Information Sciences)

Kiet Tran, Ph.D.

Corporate Faculty (Computer Information Sciences)

David Moore, Jr.

Corporate Faculty (Computer Information Sciences)

Muazzam Ali

Lecturer in Software Engineering

Kapila Molri

Corporate Faculty (Computer Information Sciences)

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.

Prerequisites: Baccalaureate degree in Computer Information Systems, Computer Sciences, or related field.
Corequisites: None

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.

Prerequisites: None
Corequisites: None

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.

Prerequisites: Baccalaureate degree in Computer and Information Sciences with a concentration in Software Engineering and Systems Analysis or the equivalent.
Corequisites: None

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.

Prerequisites: CISC 530, CISC 610
Corequisites: None

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.

Prerequisites: CISC 504, or Bachelor of Science in Computer Science or a related technical field
Corequisites: None

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.

Prerequisites: Completion of at least 18 graduate semester hours; must be taken prior to GRAD 699
Corequisites: None

International Admissions

Information for International Students

All of the University’s graduate programs are STEM approved, and curricular practical training (CPT) is offered for this program.

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Apply Now

Create an account and start your free online application to Harrisburg University today.

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