CISC 520 Data Engineering and Mining (3 semester hours)
Prerequisites: Baccalaureate degree in Computer and Information Sciences with a concentration in 
Software Engineering and Systems Analysis or the equivalent.

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 data and its processing; languages to describe data, define access, and manipulate databases; and 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 semester hours)
Prerequisites: Baccalaureate degree in Computer Information Systems, Computer Sciences, or related field.

Description: 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 Computer Architecture for Software Engineers (3 semester hours)
Prerequisites: Baccalaureate degree in Computer and Information Sciences with a concentration in 
Software Engineering and Systems Analysis or the equivalent.

Description: Modern computer information systems are ever-increasing in complexity and sophistication. As a result, software engineers must be able to make effective decisions 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 560 Secure Computer Systems (3 semester hours)
Prerequisites: None

Description: This course focuses on the design principles for secure computer systems. Topics regarding authentication, access control and authorization, discretionary and mandatory security policies, secure kernel design, secure operating systems, and secure databases are covered from a systems architecture perspective. Emphasis is on the design of security measures for critical information infrastructures. Upon completion of this course, the student is able to design, implement, and manage secure computer systems through the design of a security awareness program.

 

CISC 600 Scientific Computing I (3 semester hours)
Prerequisites: A baccalaureate degree in computer science or a related technical field (e.g. electrical and 
computer engineering, information science or operations research).

Description: Scientific Computing I covers: solution of linear algebraic equations, interpolation and extrapolation, integration an evaluation of functions, random numbers, and sorting. The course uses C++ programming language as a base language to solve the problem sets or the student can choose 
another programming language. The course is intensely practical with fully-worked examples and graded exercises.

 

CISC 610 Data Structures and Algorithms (3 semester hours)
Prerequisites: A baccalaureate degree in computer science or a related technical field (e.g. electrical and 
computer engineering, information science, operations research).

Description: This course emphasizes fundamental algorithms and advanced methods of algorithmic design, analysis and implementation. 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.

 

CISC 612 Elements of Computing Systems (3 semester hours)
Prerequisites: CISC 530

Description: This course is an integration process of key notions from algorithms, computer architecture, 
operating systems, compilers, and software engineering into one unified framework. This is done constructively, by building a general-purpose computer system from the ground up. In the process, many 
ideas and techniques are used in the design of modern hardware and software systems, and discuss major trade-offs and future trends. This is a hands-on course, evolving around building the full set of HW and SW modules including the chip set of simple computer using a simulator, developing the assembler, building part of the virtual machine translator and a simple compiler all the way to a simple programming language and a simple game.

 

CISC 620 Principles of Machine Learning (3 semester hours)
Prerequisites: CISC 530, CISC 600, and CISC 610

Description: This course introduces the basic idea of machine learning and the application to data from real world problems. Topics include: Classification as a Problem Solving Tool, Similarity Measures and Clustering, The Classification Process, Classification for Sentiment Analysis, Advanced Recommendations, FFT Classifiers, Computer Vision & Pattern Recognition, Dimensionality Reduction, and Big Data & Machine Learning.

 

CISC 661 Principles of Cybersecurity & Cyber Warfare (3 semester hours)
Prerequisites: None

Description: The course introduces the student to the interdisciplinary field of cybersecurity. Topics include the evolution of information security into cybersecurity and exploring the relationship of cybersecurity to organizations and society. The analyses of the threats and risks to/in these environments are examined. The ultimate goal of this course is for the student to acquire the advanced knowledge required to develop the skills needed to integrate knowledge from this course into a workplace environment.