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Online Master of Science in Computer Science Curriculum

Curriculum Details

  • 13 courses
  • 34 credits
  • 8-week courses

In the online MS in Computer Science curriculum, you’ll master the technical foundations and applied skills driving the AI era — from machine learning and deep learning to large language models, computer vision and reinforcement learning.

The computer science master’s online program is structured as 28 credits of core coursework plus 6 credits of electives you select based on your goals. You’ll finish through one of two pathways: an applied capstone project (professional track) or a faculty-mentored thesis (research track).

By taking accelerated eight-week courses, students typically complete the online MS degree in computer science in as few as 12 to 24 months. Students entering without a computer science background may complete bridge courses in Python programming and foundational mathematics before beginning the core sequence.

Competency Course

Credits

This intensive micro-credential course provides comprehensive Python programming training specifically designed for students entering the MS in Data Science and AI program. The course covers Python fundamentals, data structures, object-oriented programming, and essential libraries for data manipulation, analysis, and visualization. Students will develop proficiency in NumPy, Pandas, Matplotlib, and introductory machine learning with scikit-learn. Emphasis is placed on writing clean, efficient, and reproducible code following software engineering best practices. Through hands-on projects and real-world datasets, students will build the computational foundation necessary to succeed in advanced data science and AI coursework. This self-paced or accelerated course prepares students to confidently tackle programming challenges in subsequent graduate courses.

This course is designed to bridge knowledge gaps in Calculus and Linear Algebra for incoming Data Science MS students who do not have a strong mathematics background.

Core

Credits

The main purpose of the course is to provide students with systematic overview of techniques for analysis and design of algorithms and to familiarize the students with notions related to computational complexity, intractability and approximation algorithms. Students will become more capable of designing efficient algorithms for specific tasks in computer sciences and their applications, including but not limited to computational geometry, image processing, video surveillance analysis, data mining, etc.

Mathematics of Data Science and Machine Learning introduces core mathematical concepts essential for graduate study in computer science, data science, and artificial intelligence. The course develops practical competence in multivariable calculus, probability and statistics, discrete structures, and numerical computation, with emphasis on the mathematical reasoning that underpins modern machine learning and data-driven algorithms. Students learn to formulate, analyze, and solve computational problems using these mathematical tools, and to connect theory with implementation through Python-based laboratory work. By the end of the term, students will be prepared for graduate-level coursework in machine learning, data mining, optimization, artificial intelligence, and related fields.

This course provides a comprehensive foundation in database systems and modern data wrangling techniques essential for applied data science and artificial intelligence workflows. Students will reinforce their knowledge of the relational model-based database design and SQL, and will learn non-relational data models, ETL processes, data cleaning, integration, and scalable data processing. Emphasis will be placed on building robust, efficient, and ethical data pipelines that support machine learning and advanced analytics. Through hands-on projects, students gain practical experience developing end-to-end data workflows from ingestion to transformation to storage.
This course introduces computational techniques inspired by natural systems and biological intelligence. Students will explore the fundamental areas of Computational Intelligence, including evolutionary computation, neurocomputing, approximate reasoning (fuzzy and rough logic), as well as hybrid intelligent systems. Emphasis will be placed on understanding principles of natural intelligence, adaptation, emergence, and self-organization, and on applying these principles to solve complex problems in optimization, data modeling, pattern recognition, and decision-making. Students will gain experience designing and implementing nature-inspired algorithms and evaluating their performance in comparison to traditional computational methods.
This graduate-level course provides a mathematically rigorous introduction to modern Artificial Intelligence, integrating foundational theory with practical system design. Students build competency in the core mathematical tools that support AI while studying key algorithms such as heuristic and graph search, probabilistic inference, classification, graph search, and deep reinforcement learning. The course emphasizes the development of AI agents capable of autonomous decision-making, planning under uncertainty, policy optimization, and multi-agent coordination. Students also examine contemporary AI paradigms, including generative models and advanced neural architectures, to understand how intelligent behavior emerges in real-world systems.
This course provides a fundamental and practical understanding of how deep learning works, how to implement and apply modern architectures including variational autoencoders, convolutional neural networks, LSTMs, and recurrent neural networks, transformers and their applications in AI. The course also explores emerging topics such as geometric deep learning, reinforcement learning with Foundation Models, diffusion models and Generative AI. Through real-world case studies, students will develop the practical skills needed to design, train, and evaluate deep learning models for complex tasks in AI research and industry. The course is taught using the Pytorch and Tensorflow deep learning framework.
This class will provide a graduate introductory survey of machine learning, through lecture, readings from textbooks, readings from the research literature, and programming projects. Topics include, what is machine learning, information theoretic methods, probabilistic methods, discrete and continuous distributions, max-likelihood estimation, regularization, the inference problem, feature spaces, considerations for model validation, generative models, discriminative models, model validation, considerations for building practical systems.
In this seminar we survey across a number of research areas in Computer Science in order to give student knowledge and breadth in understanding the discipline. This will help students in selecting a research topic for their graduate degree program.

This culminating experience provides students with the opportunity to integrate and apply advanced knowledge in computer science, data science, machine learning, and artificial intelligence through either a research-oriented thesis or an applied project. Under the supervision of a faculty advisor, students identify a significant problem, conduct independent investigation or system development, and demonstrate the ability to design, implement, analyze, and evaluate computational solutions using modern methodologies and technologies. Students pursuing the thesis option engage in original research that contributes to knowledge in computer science or related AI and data-driven fields and produce a formal written thesis with an oral defense. Students pursuing the project option complete an applied, industry-oriented, or development-focused project that demonstrates technical depth, innovation, and practical problem-solving skills through the creation, deployment, or evaluation of a computational system, AI application, or data-driven solution. Both pathways emphasize technical communication, critical thinking, ethical considerations, and professional-level competency in advanced computing.

Students will enroll in CSCI 695 twice for a total of 6 credits.

Electives (select 2)

Credits

This course introduces graduate students to the core statistical methods used in modern data science. The course emphasizes understanding how to explore, summarize, and model data, and how to make sound inferences from real-world datasets. Students learn through hands-on analysis using Python or R, working directly with code, data, and reproducible workflows. The course prepares students for advanced topics in data analytics, machine learning, and predictive modeling by building a strong statistical foundation and emphasizing practical application, ethical analysis, and clear communication.

Data mining is active research area of computer science with substantial applications in sciences and commerce. Students in computer sciences need this course to become familiar with techniques that can be subsequently used for identifying interesting phenomena in observed data and/or for design and implementation of stand-alone real-time applications including applications in military and homeland security. This course provides the study of techniques for analysis and visualization of massive amounts of data. Includes hands-on experience in developing and using data mining software
Computer vision is the study of systems that derive information from visual data (pictures, video, sensors). This course is a graduate introduction to computer visual algorithms, models, and techniques. Topics include image formation, projective geometry, color spaces and representations, kernels and visual features, segmentation, motion estimation, video feature, considerations for practical systems, models, and case studies.
This course examines the ethical, legal, and societal dimensions of data science and AI. Students learn to evaluate algorithmic fairness, privacy risks, governance practices, and regulatory compliance; explore privacy-preserving technologies; and communicate responsible, human-centered, and explainable approaches to AI deployment. Through case studies and hands-on technical work, students develop the skills needed to design and assess ethical, transparent, and accountable data-driven systems.
This course explores the emerging discipline of AI-powered software engineering, focusing on the design, implementation, and deployment of intelligent software development systems. Students learn to build Python-based AI agents capable of autonomous data processing, continuous integration tasks, code refactoring, documentation generation, and automated testing. The course provides hands-on experience using Claude Code, GitHub/GitLab automation, and specialized GPT-based agents engineered for domain-specific professional tasks. Through a combination of lectures, applied labs, and project-based learning, students will develop the ability to integrate large language models (LLMs) into software workflows, build multi-agent systems, orchestrate automated development pipelines across multiple Git branches, and design custom AI tools that augment or fully automate engineering processes. Emphasis is placed on ethical practice, reproducibility, responsible agent behavior, and the secure deployment of AI-driven development systems.
This graduate-level course explores the principles, architectures, and practices for deploying artificial intelligence and machine learning models in cloud environments. Students will gain hands-on experience with cloud platforms (AWS, Azure, Google Cloud) to design, deploy, and manage scalable AI systems. The course covers containerization, orchestration, serverless architectures, model serving, MLOps pipelines, monitoring, and cost optimization. Emphasis is placed on building production-ready AI systems that are scalable, efficient, reliable, and maintainable. Students will work with real-world datasets and modern cloud-native tools to deploy end-to-end ML solutions, while considering ethical implications of cloud-based AI systems including data privacy, model fairness, and environmental impact.
Main purpose of the course is to provide students with systematic overview of advanced statistical techniques that can be useful in their research and future careers. The statistical techniques are applicable in various fields including video surveillance analysis, data mining, natural resources, finance, etc.

This course is intended for students interested in artificial intelligence. Reinforcement learning is an area of machine learning in which an agent learns how to behave in an environment by performing actions and assessing the results. Specific topics include Markov decision processes, tabular reinforcement learning, policy gradient methods, and function approximation such as deep reinforcement learning. Optional topics are distributional reinforcement learning, model-based methods, offline learning, inverse reinforcement learning and multi-agent reinforcement learning.

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