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 will follow on from the Introduction to Bioinformatics and will include advanced topics such as: Linux and high performance computing; genomic data visualization; R programming in bioinformatics; and RNA sequencing data analysis.
In this class, students will learn about fundamental GIS concepts while building the basic skills necessary to integrate a GIS into a decision making process.
This interdisciplinary course will provide the fundamental knowledge for healthcare innovation and entrepreneurship.
This course will discuss the spectrum of evidence generated on emerging technology through the lens of popular culture, news and articles, primary research, and fundamental research methodologies.
This course provides both a hands-on introduction and conceptual foundation for public health informatics.
This introductory course will present an overview of the basics concepts, techniques and algorithms used in Machine Learning.
Exposure to basic statistical concepts that are necessary for students to understand the content presented in more advanced courses...
This course covers the analytics research process from the translation of business problems into researchable questions that can be addressed by using analytics...
Students learn how to work effectively in teams to identify, structure, and communicate the business value of data analytics to an organization.
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.
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