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In this course, students explore a variety of pre-processing, predictive modeling, and post-processing concepts, techniques and methodologies within a software environment that is commonly used in business. In particular, students learn advanced methods of pre-processing which is an essential step in developing supervised and unsupervised models for successful applications in business. In addition, students learn a variety of advanced methods of post-processing which help to extract insights from the data that they explore.

This course provides a broad overview of business intelligence and data management including database fundamentals, business intelligence approaches, data management/data governance strategies, data mining and other business/data analytics techniques. Our primary emphasis will be on the managerial perspective, focusing on how you can design, implement and leverage business intelligence systems and strategies in a management role.

Prescriptive analytics includes a variety of topics related to decision-making within the context of business problems and applications. The primary goal of this course is to develop a model of a business scenario within a software environment. Students employ mathematical techniques that support or automate a course of action that should be taken by decision-makers. In this hands-on course, students are introduced to concepts related to constructing, testing and applying prescriptive models and techniques that are common in business settings. From this perspective, students utilize software tools in order to apply a variety of decision-making techniques, including optimization and simulation.