Genomics Foundations introduces core concepts in modern genomics and human genetics underlying computational biology and public health genomics. The course integrates key biological principles with quantitative reasoning and hands-on use …
Trends in hardware and software for Big Data Systems and applications. Cover principles driving data infrastructures, which enabled the training of AI models on datasets (speech, sounds, images, video, languages) …
This course focuses on making students more effective at identifying and designing AI use cases to create novel AI-powered products and services. Students will work with a variety of AI …
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 …
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. …
Bayesian inferential methods provide a foundation for machine learning under conditions of uncertainty. Bayesian machine learning techniques can help us to more effectively address the limits to our understanding of …
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, …
Introduces fundamental concepts of computation, data structures, algorithms, & databases, focusing on their role in data science. Covers both theoretical studies & hands-on learning activities. Includes basic data structures, advanced …
Covers the fundamentals of probability and stochastic processes. Students will become conversant in the tools of probability, clearly describing and implementing concepts related to random variables, properties of probability, distributions, …
Introduction to regression modeling. Topics will be discussed first in the context of linear regression, and then revisited in the context of logistic regression, ordinal regression, proportional hazards regression, and …
Covers data pipeline: techniques to collect data, organize, query & apply the data, and generate products that describe the insights. Topics include Python environments, containers using Docker, data wrangling with …
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 …
Learning tools and concepts for computing on big data. Learn how to use Spark for large-scale analytics and machine learning. Spark is an open-source, general-purpose computing framework that is scalable …
Covers advanced theoretical concepts for deep neural networks. Topics include convolutional neural networks and their design principles, encoder-decoder architectures, recurrent neural networks, transformers, bounding box detection, image segmentation, generative adversarial …
Introduces ways that data and information have historically been constructed in different realms--from medicine to public health to computing--to shed light on the power relationships embedded in some of our …
Transition into principal investigators and generators of data science-based knowledge. Develop practical skills necessary to conduct high quality data science research, advance development into producers and critical consumers of research, …
Engages students in identification of a research question, a review of the literature and the application of an existing data science tool or technique (algorithm) to that problem. This is …
PhD level Dissertation Research.