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  • 3.00 Credits

    Introduction to a variety of advanced database topics and on-going trends in modern database systems and design. (Spring - 2nd Session) [Graded (Standard Letter)] Registration Restriction(s): Software Development students only.
  • 3.00 Credits

    This course provides students with a comprehensive overview of the principles, processes, and practices of agile software development. Students learn techniques for initiating, planning, and executing on software development projects using agile methodologies. Students will obtain practical knowledge of agile development frameworks and be able to distinguish between agile and traditional project management methodologies. Students will learn how to apply agile tools and techniques in the software development lifecycle from project ideation to deployment, including establishing an agile team environment, roles and responsibilities, communication and reporting methods, and embracing change. (Fall - 1st Session) [Graded (Standard Letter)] Registration Restriction(s): Software Development students only.
  • 3.00 Credits

    This course focuses on the processes, methods, and techniques for developing quality software and maintaining quality software. Software testing processes at the unit, module, subsystem, and systems levels are discussed. Testing methods covered include automatic and manual generation of test data, static vs. dynamic analysis, functional testing, inspections, and reliability assessment. (Fall - 2nd Session) [Graded (Standard Letter)] Registration Restriction(s): Software Development students only.
  • 3.00 Credits

    This course provides an advanced exploration of essential methodologies and tools for data analysis. Students will gain expertise in data preprocessing, statistical modeling, machine learning, and data visualization. The course emphasizes real-world applications, preparing students to address complex data-related challenges in diverse domains. (Spring - 1st Session) [Graded (Standard Letter)] Registration Restriction(s): Software Development students only.
  • 3.00 Credits

    Concentrated on computer networks, students will learn the fundamentals of networking, network management, and network design. Areas will include, but are not limited to, the abstraction layers of network communication, wireless networks, network security, and network architecture. Students will learn how to analyze and build basic computer networks that meet the needs of changing computing environments. (Spring - 2nd Session) [Graded (Standard Letter)] Registration Restriction(s): Software Development students only.
  • 3.00 Credits

    Concepts and techniques for testing and modifying software in evolving environments. Topics include software testing at the unit, module, subsystem, and system levels; developer testing; automatic and manual techniques for generating test data; testing concurrent and distributed software; designing and implementing software to increase maintainability and reuse; evaluating software for change; and validating software changes. (Summer - 1st Session) [Graded (Standard Letter)] Registration Restriction(s): Software Development students only.
  • 3.00 Credits

    An overview of Artificial Intelligence (AI), approaching the concept from its origins to expectations for the future. The course will focus on different technologies and utilizing the concepts/skills widely accepted for AI. (Summer - 2nd Session) [Graded (Standard Letter)] Registration Restriction(s): Software Development students only.
  • 3.00 Credits

    This course will cover a series of important Big-Data-related problems and their solutions. Specifically, we will introduce the characteristics and challenges of Big Data, state-of-the-art computing paradigms and platforms, programming tools, and techniques for managing, processing, and visualizing large data sets. (Fall - 1st Session) [Graded (Standard Letter)] Registration Restriction(s): Artificial Intelligence & Machine Learning and Computer Science students only
  • 3.00 Credits

    This course is about designing algorithms for computational problems and how to think clearly about analyzing correctness and running time. The course explores fundamental algorithm design techniques such as greedy, divide and conquer, dynamic programming, network flow, reduction, approximation, linear programming, and randomization for efficient algorithm construction. (Fall - 2nd Session) [Graded (Standard Letter)] Registration Restriction(s): Artificial Intelligence & Machine Learning and Computer Science students only.
  • 3.00 Credits

    This course covers some of the main data structures used in industry today. (Spring - 1st Session) [Graded (Standard Letter)] Registration Restriction(s): Artificial Intelligence & Machine Learning and Computer Science students only.