Reinforcement Learning is a dynamic area in machine learning that allows an agent to learn by interacting with its environment. This enables learning when the ground truth is unavailable or …
Provides a foundation in discrete mathematics, data structures, algorithmic design and implementation, computational complexity, parallel computing, and data integrity and consistency. Case studies and exercises will be drawn from real-world …
Principles of interactivity in application and dashboard development using R, Python, and JavaScript programming languages. Design visually appealing and user-friendly interfaces, develop interactive applications for data visualization, and build dynamic …
Provides an in-depth exploration of probabilistic and statistical methods used to understand, quantify, and manage uncertainty. Learn foundational concepts in probability and statistics, simulation techniques, and modern approaches to parameter …
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Code an end-to-end data science project with core software engineering and automation to quickly integrate into a corporate environment. Use version control to focus on solutions, leverage automation at your …
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This course provides selected special topics in data science to graduate and undergraduate students.
Covers the practice of data science, including communication, exploratory data analysis, and visualization. Also covered are the selection of algorithms to suit the problem to be solved, user needs, and …
This course examines the ethical issues arising around big data and provides frameworks, context, concepts, and theories to help students think through and deal with the issues as they encounter …
Designed for capstone project teams to meet in groups with advisors and clients to advance work on their projects. Capstone course is for MSDS students.
Comprehensive introduction to predictive modeling, a cornerstone of data science and machine learning. Learn the fundamental concepts, techniques, and tools used to build models while emphasizing both theoretical understanding and …
This course covers fundamentals of data mining and machine learning within a common statistical framework. Topics include regression, classification, clustering, resampling, regularization, tree-based methods, ensembles, boosting, and Support Vector Machines. …
A graduate-level course on deep learning fundamentals and applications with emphasis on their broad applicability to problems across a range of disciplines. Topics include regularization, optimization, convolutional networks, sequence modeling, …
Many problems in data science essentially boil down to some mathematical relationships that are to be solved numerically. But have you ever wondered how computers could do math? This graduate-level …
Explores the mathematical foundations of inferential and prediction frameworks commonly used to learn from data. Frequentist, Bayesian, Likelihood viewpoints are considered. Topics include: principles of estimation, optimality, bias, variance, consistency, …
Fundamentals of data mining and machine learning within a common statistical framework. Topics include boosting, ensembles, Support Vector Machines, model-based clustering, forecasting, neural networks, recommender systems, market basket analysis, and …
Course content varies by section and is selected to fill timely, special interests and needs of students. May be repeated for credit when topic varies.
Combines topics in data ethics, critical data studies, public policy, governance, and regulation. Address challenges by topic (Health, Education, Culture & Entertainment, Security & Defense, Cities, Environment, Labor). Research how …
Specialized or advanced topics not in DS current course offerings. Requires (a) approval of the program director and (b) an SDS faculty member who will serve as instructor. Propose a …