Master of Science in Data Science and AI (MSDS)

MSDS 515. Preparing for MSDS and AI Success. 1 Credit Hour.

This course is designed to provide entering MSDS students with the skills necessary to be successful in a graduate data science program. Spanning two full days, it focuses on foundational knowledge in statistics, programming, data visualization, and communication. Moreover, the course offers insights into program expectations and introduces students to the available computing resources.
Session Cycle: Every Semester.

MSDS 610. AI I: Deep Learning. 3 Credit Hours.

This course introduces the basic concepts of Neural Networks and Deep Learning. Students will learn the fundamental principles, formulations, underlying mathematics and deep learning implementation details in Pytorch. The course will also explore different deep learning model suitability for different data domains such as text, images and videos to deal with different tasks such as Natural Language Processing, Computer Vision, Decision Making, Healthcare and Financial Applications.
Prerequisites: ISA 510 and ISA 530
Session Cycle: Spring
Yearly Cycle: Yearly.

MSDS 620. AI II: Natural Language Processing. 3 Credit Hours.

There are many business and artificial intelligence applications that need to process unstructured text data. This course teaches students how to overcome the unique challenges of working with unstructured text in machine learning and deep learning models. Students learn about how to create text representations, embeddings, and features for modeling purposes. Natural language processing applications include sentiment classification, topic modeling, text generation, and named entity recognition. Students in this course will implement these artificial intelligence models in Python, gaining experience with libraries such as NLTK and Hugging Face.
Prerequisites: ISA 530
Session Cycle: Spring
Yearly Cycle: Yearly.

MSDS 630. Large Scale Data Analytics in the Age of AI. 3 Credit Hours.

The rise of social media, IoT, and digital transformation has generated vast amounts of structured and unstructured data. Advances in AI, computing power, and cloud storage have transformed data processing and analytics. This course equips students with the skills to manipulate, store, analyze, and visualize big data using AI-driven techniques. A key focus is mastering Apache Spark and PySpark for large-scale data processing. Students will learn data pre-processing, exploratory analysis, feature engineering, and model building. The course also covers tuning and managing machine learning/AI models. As part of the course, students will have the opportunity to work with real-world datasets and implement machine learning/AI algorithms on a cloud computing platform. This hands-on experience provides a practical understanding of leveraging big data analytics for decision-making and innovation.
Prerequisites: ISA 530, ISA 540
Session Cycle: Summer Term 1
Yearly Cycle: Yearly.

MSDS 640. Data Science and AI Capstone. 3 Credit Hours.

Students will execute a full data science/AI project, developing their skills as data scientists with a focus on real-world applications and situations. The final project provides an opportunity to integrate all of the core skills and concepts learned throughout the program and prepares students for long-term professional success in the field. It provides experience in formulating and carrying out a sustained, coherent, and influential course of work resulting in a tangible data science/AI project using real-world data. This capstone project will test student skills in data pre-processing, data preparation, data transformation, feature engineering, machine learning/deep learning, data visualization, data communication, and presentation. Projects will be drawn from real-world problems and will be conducted with industry, government, and academic partners. Emphasis will be placed on problem-solving via state-of-the-art data science pipelines and practices and on the ability to “tell a story "Using verbal, analytical, written, and visualization skills.
Prerequisites: ISA 530 and MSDS 630
Session Cycle: Summer Term II
Yearly Cycle: Yearly.