Anton Braverman joined the Operations group at Kellogg in 2017. He completed his PhD in Operations Research from Cornell University, and holds a Bachelor's degree in Mathematics and Statistics from the University of Toronto. Anton's research is focused on stochastic modelling and applied probability. Some application domains of interest include ridesharing services, as well as healthcare operations.
The value of analytics and artificial intelligence (AI) in today's business landscape cannot be overstated. These tools have become integral to the decision-making process for many organizations across a variety of industries, including services, marketing, transportation, online platforms, and finance. AI systems often utilize a range of analytics techniques to make data-driven, evidence-based decisions. The analytics tools can be broadly classified to three types:
Prescriptive analytics, in particular, plays a key role in the functionality of AI systems. This type of analytics involves the use of data-driven models to determine the best course of action in a given situation, based on data and analysis of past outcomes and trends. By utilizing prescriptive analytics, organizations can make informed, strategic decisions that optimize outcomes and drive business success. For example, an AI system might use prescriptive analytics to determine the best way to match drivers with riders, recommend the best portfolio of stocks, or to recommend the most effective marketing campaign for a new product. By leveraging the power of advanced analytics techniques, organizations can gain a competitive edge and make more informed, strategic decisions that can drive growth and success.
This course focuses on developing a holistic understanding of prescriptive analytics by introducing the basic principles and techniques of applied mathematical modeling for managerial decision-making. You will learn to use important analytic methods, such as spreadsheet modeling, optimization, and Monte Carlo simulation, to recognize their assumptions and limitations, and to employ them in decision-making. The emphasis will be on model formulation and interpretation of results, rather than on mathematical theory or coding. We will cover a wide range of prescriptive analytics models that are widely used in diverse industries and functional areas, including finance, operations, and marketing.