Graduate Data Science Courses
###### COS501 - Computational Foundation for Data Science (3 credits)
This course covers advanced topics on the latest advancement in machine learning. The course focuses on application of deep neural networks (deep learning) to the areas of computer vision and natural language processing.
###### COS531 - Modern Applied Statistical Learning (3 credits)
Modern statistical learning focuses on the application of complex computing techniques and statistical inference to extract information from data to help address trends and patterns, forecast potential future problems, and make business decisions. This course is designed to provide students with hands-on, practical experience in statistical learning methods such that they can apply them to solving real-world problems. Students enhance their understanding of statistical analysis and inference while getting trained on industry-standard software packages such as R.
###### COS536 - Applied Machine Learning (3 credits)
This is a required course for the Data Science program. It focuses on the theoretical basis as well as applications of the state-of-the-art machine learning algorithms. Students will get familiar with Python machine learning tools and use them for projects.
COS541 - Big Data and Data Engineering (3 credits)
This course introduces students to the fundamental concepts, techniques, and tools used in managing and analyzing large volumes of data, focusing on data engineering principles and practices. Topics covered include data ingestion, storage, processing, and analysis in the context of big data technologies and platforms.
###### COS643 - Computer Vision and Natural Language Processing (3 credits)
This course covers advanced topics on the latest advancement in machine learning. The course focuses on application of deep neural networks (deep learning) to the areas of computer vision and natural language processing.
DAS501 - Mathematical Foundation for Data Science (3 credits)
This course provides students with the mathematical foundations necessary for understanding and working with data in Python. The course covers fundamental concepts in mathematics such as linear algebra, calculus, probability, and statistics, and shows how these concepts are relevant to data science.
DAS502 - Probability for Data Science (3 credits)
This course teaches fundamental concepts of probability theory, discrete and continuous random variables, standard distributions, moments and moment-generating functions, laws of large numbers and the central limit theorem.
DAS512 - Statistical Inference and Modeling (3 credits)
This is a graduate level statistics course. It will explore the underlying theories and methods of statistics and deepen students' understanding of the statistical concepts learned at undergraduate level.
###### DAS522 - Exploratory Data Analysis and Visualization (3 credits)
Students will learn the art and science of converting data into graphical representations that make complex information accessible and actionable. This course covers the basics of R programming, data cleaning, transformation techniques, and the use of Tableau for creating compelling dashboards. The curriculum is designed to help students master the skills needed to present data in visual formats that reveal patterns, trends, and correlations.
###### DAS541 - Data Mining for Business (3 credits)
This course seeks to equip students with a solid understanding of opportunities, techniques, and critical challenges in using data mining and predictive modeling in a business setting. The focus is to enable students to develop the ability to translate business challenges into data mining problems and apply predictive modeling technologies to improve business decisions.
###### DAS548 - Ethics in Computer and Data Science (3 credits)
This course seeks to orient the student and future technologist with the ethical issues arising from the rapidly increasing role of technology in our lives. In designing systems, developing requirements, and deploying systems technologists need to be thoroughly aware of historical precedent regarding the ethical use of information, but also aware of ethical issues arising daily around us. For example how can a user limit the amount of personal information Google or Facebook collects, shares with others, or sells for a fee? How ethical is it to collect personal information from users/customers and who actually own the rights to that personal information? How do technology companies responsibly pursue the advancement of AI technology and its incorporation into daily life? All these issues and more involve computing, ethics, and the internet.
DAS631 - Generative AI: Foundation and Application (3 credits)
This graduate-level course provides a comprehensive introduction to the foundations and applications of Generative AI, with a focus on Transformer-based models and Diffusion Models. Students will explore how these state-of-the-art generative models can be used to generate novel and realistic content, including text, images, and other modalities. The course will cover the underlying principles, architectures, and training techniques of these generative models. Students will gain a deep understanding of the theoretical and practical aspects of generative AI, including how these models work, their strengths and limitations, and their real-world applications. Additionally, the course will explore the societal impact, ethical considerations, and future directions of generative AI, addressing issues such as bias, privacy, and responsible development. Students will have the opportunity to work on hands-on projects, implement generative models, and explore the latest advancements in the field.
###### DAS761 - Capstone Project for Data Science (6 credits)