MET AD 616 Enterprise Risk Analytics

MET AD 616, Enterprise Risk Analytics, offers a quantitative approach to estimating and managing risk across various industries. The major risk categories of enterprise risk management—financial risk, strategic risk, and operational risk—will be discussed, and risk analytics approaches for each of these risks will be covered. Students will learn how to use interlinked data-inputs, analytics models, business statistics, optimization techniques, simulation, and decision-support tools. This course extensively utilizes statistical concepts along with an in-depth treatment of risk using R programming language. Specifically, the course will focus on covering Input Modeling techniques with uncertainty, Stochastic Optimization, Decision Trees with uncertainty, and Bayesian Inference in determining causality and input processes. The course also covers introductory level Stochastic Programming concepts associated with 2-stage stochastic decision problems. Finally, the course has a final team project where each team will take up a real business case with data across industries ranging from Private Equity, Healthcare, Venture Capital, and Supply Chain; solve the case as a team; and make a presentation on the decisions made, taking uncertainty and risk into consideration. [4 cr.]

Prerequisites

Prerequisite Courses

    MET AD 571 Business Analytics Foundations

Preparatory Labs

    AD 100

    ADR 100 Introduction to R for Business

    Other self-paced labs are recommended but not required for AD 616

Technical Notes

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Syllabus

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Course Description

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Learning Objectives

During this course you will be able to:

    learn to use interlinked data-inputs

    learn about different analytics models

    model decisions

    learn about optimization techniques

    extensively work in R to include Uncertainty in Decision making

    build your own decision support tools

By successfully completing this course you will be able to:

    use interlinked data-inputs

    discuss different analytics models

    explain business statistics

    use optimization techniques over uncertainty

    recognize different simulations

    build your own quantitative repertoire