The online Data Analytics for Business Professionals curriculum capitalizes on the University of Chicago’s expertise and resources in teaching analytical skills that enable students to accelerate their careers. Students learn by analyzing real business problems individually and in virtual teams while benefitting from the knowledge and experience of their instructors and peers.
*Order of Completion: DABP12000 > DABP13000 > DABP14000
The courses include online course content, activities, and assessments. In addition, classes will meet virtually in a weekly one-hour synchronous session where students interact with the course instructor and each other. This combination of content accessible anytime combined with weekly synchronous sessions provides flexibility, while assuring that students enjoy the benefits of the interactive learning experience.
Courses follow the same dataset and use one case throughout program completion. This structure is deliberately built to provide students with a repeatable and actionable toolkit to be able to frame business challenges into a data science solution and build executable business plans.
In Data Visualization and Storytelling, the second core course in the program, students learn about the roles visualization can play in making sense of their data and persuading others about what it means. They study how to conduct exploratory data analysis and locate patterns and trends in data by visualizing relationships within it.
This non-required elective focuses on the process and skills necessary for gaining business understanding, conducting a business discovery and developing an analytical project plan.
Data Understanding and Preparation, the first core course in the program, guides students through sourcing, preparing, and manipulating their data.
Advanced Analytics and Machine Learning, the final core course in the program, introduces students to the theory and practice of machine learning. Students will learn how to implement the most popular machine-learning techniques in use today to discover patterns in their data and develop models to predict future outcomes.
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