We offer credit and noncredit learning opportunities in a variety of subjects, from more traditional disciplines such as literature and philosophy, to business-oriented courses, to master’s degrees. Our courses are conveniently located in-person at the University of Chicago Gleacher Center and NBC Tower in downtown Chicago, and are primarily in the evening and on weekends, to fit the schedule of working adults. We also offer online courses, for those not located in Chicago, or who wish to study from home.
This course introduces students to how optimization and simulation techniques can be used to solve many real-life problems.
The focus of this course is on foundations of Bayesian approach, its applications via hierarchical models, linear and generalized linear models, mixed models and various types of Bayesian decision making.
This course introduces students to the science of web analytics while casting a keen eye toward the artful use of numbers found in the digital space.
The goal of this course is to fill this gap and teach students to think about real problems by analyzing big data in new data analysis ecosystems.
Students will learn foundations of natural language processing, including: concept extraction; text summarization and topic modeling; part of speech tagging; named entity recognition; semantic roles and sentiment analysis.
This course is an introduction to reinforcement learning, also known as neuro-dynamic programming. It discusses basic and advanced concepts in reinforcement learning and provides several practical applications.
The capstone project implementation course is an independent study offered during the second quarter of the three-quarters long capstone process.
Capstone Project writing is the last course in which teams complete the capstone process by writing a report and developing a presentation that describe the analytical solution they devised to address a problem posed by their client industry partners.
This course introduces the essential general programming concepts and techniques to a data analytics audience without prior programming experience.
This course in Deep Learning and Image Recognition will provide a practical, hands-on set of lectures on Deep Learning and Image Processing tools and techniques.
This four-session course will focus on two of the more critical Big Data ethical issues at hand: bias and privacy. Its goal is to equip students with the ability to identify, understand, and discuss these the ethics of bias and privacy in the context of their work.
This course in python starts with introduction to the python programming language basic syntax and environment. It methodically builds up the learner's experience from the level of simple python statements and expressions to writing succinct, efficient and fast Python expressions and package the code in methods and classes.
This 3-hour course is an introduction to ethical issues surrounding data analytics, machine learning, and artificial intelligence. As a stand-alone offering, the course has no formal syllabus outlining weekly topics, reading, and assignments.
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