We offer credit and non-credit 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 at the University of Chicago Gleacher Center 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 will introduce students to the common algorithms: association and sequence rules discovery, memory-based reasoning, clustering, classification and regression decision trees, logistic models, and neural network models.
This course in advanced data mining will provide a practical, hands-on set of lectures surrounding modern predictive analytics and machine learning algorithms and techniques.
This course concentrates on the following topics: Review of statistical inference based on linear model, extension to the linear model by removing the assumption of Gaussian distribution for the output (Generalized Linear Model), extension to the linear model by allowing a correlation structure for the model residuals (mixed effect models), and
This course provides students with a thorough understanding of the fundamentals of data engineering platforms, for both operational and analytical use cases, while gaining expertise in building these platforms in a way to develop analytical solutions effectively.
Review of financial markets and assets traded on them; main characteristics of financial analytics; concept of arbitrage; principles of volatility analyses; correlation, cointegration and other relationships between various financial assets; market risk analytics and management of portfolios of financial assets.
This course focuses on marketing analytics methods and applications that are used to develop marketing strategies, and create a link between marketing, customer behavior and business outcome.
This course will provide an overview of the development and rapid expansion of analytics in healthcare, major and emerging topical areas, and current issues related to research methods to improve human health.
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.
This course teaches advanced programming concepts and techniques to students to develop more advanced Python skills related to streaming analytics.
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.
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.
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