The Foundations of Data Structures and Algorithms pathway covers essential concepts in algorithm design and analysis, focusing on dynamic programming and greedy algorithms, which are fundamental for solving optimization problems. It also introduces approximation algorithms and linear programming techniques, valuable for addressing real-world computational challenges, and delves into advanced data structures, RSA algorithms, and quantum algorithms to enhance problem-solving skills in complex computing environments. The Network Systems Principles and Practice pathway offers a comprehensive understanding of network systems, beginning with foundational networking concepts and progressing to hands-on practice with Linux networking and cloud networking, equipping students with practical knowledge of modern networking systems. The Machine Learning pathway introduces students to core concepts of supervised learning, unsupervised learning, and deep learning, enabling them to apply machine learning techniques to real-world data and problems. In the Computing & Ethics and Society pathway, students explore the intersection of computing and ethics, examining foundational ethical issues in artificial intelligence and computing applications, as well as the professional ethics required in the tech industry. Lastly, the Foundations of Autonomous Systems pathway focuses on the modeling, requirement specifications, and verification of autonomous systems, providing students with a solid foundation for designing and evaluating these advanced technologies. Together, these pathways offer a well-rounded curriculum that prepares students for careers in various domains of computer science, from algorithms and machine learning to network systems, ethical computing, and autonomous technologies.

The Data Mining Foundations and Practice pathway offers an in-depth exploration of the data mining process, beginning with an understanding of the data mining pipeline and progressing to advanced methods and practical project applications. Students gain hands-on experience in extracting valuable insights from large datasets, preparing them for real-world data mining tasks. The Data Visualization pathway covers the fundamentals of presenting data effectively through visual means, enabling students to create compelling visual narratives that make complex data easier to understand. The Introduction to Human-Computer Interaction pathway provides essential knowledge in designing and testing user interfaces, with a particular focus on emerging technologies such as virtual reality, augmented reality, and artificial intelligence, offering students a comprehensive skill set in designing user-centered technologies. The Introduction to Robotics with Webots pathway covers essential robotic concepts, from basic behaviors and odometry to mapping, trajectory generation, and path planning, equipping students with the tools to design and control autonomous robots. The Natural Language Processing pathway delves into the fundamental and advanced aspects of processing and analyzing human language, incorporating deep learning techniques to improve language models and address common challenges in NLP. The Generative AI pathway introduces students to the rapidly evolving field of generative artificial intelligence, from the basics of creating AI models to exploring modern applications and cutting-edge advancements in the field. The Internet Policy pathway addresses critical issues in digital policy, including net neutrality, privacy protection, and cybersecurity, equipping students with an understanding of the regulatory landscape surrounding the internet. The Introduction to Computer Vision pathway offers students the opportunity to learn about the foundational techniques in computer vision, as well as advanced topics such as deep learning and computer vision applications in generative AI. The Software Architecture for Big Data pathway explores the architecture required for managing and processing massive datasets, teaching students fundamental patterns and practices for building scalable and efficient data systems. Lastly, the Object-Oriented Analysis and Design pathway focuses on the core principles and patterns of object-oriented design, emphasizing both theoretical foundations and practical application to develop robust and scalable software systems. Together, these pathways provide students with a well-rounded and comprehensive education in cutting-edge areas of computer science, preparing them for successful careers in technology, data science, and artificial intelligence.

Certain courses are not applicable to the Master of Science in Computer Science (MS-CS) requirements, including those that focus on specific areas such as cybersecurity for data science, ethical issues within the field of data science, and foundational algorithms related to searching, sorting, and indexing, as well as trees and graphs. Additionally, courses that focus on deep learning applications in computer vision are also excluded from fulfilling the MS-CS requirements. However, students have the flexibility to take credits from other programs, including the Master of Engineering in Engineering Management (ME-EM), the Master of Science in Data Science (MS-DS), and the Master of Science in Electrical Engineering (MS-EE), to enhance their academic experience and gain specialized knowledge in these fields.