Modeling Risk and Realities

4.6
919 ratings
136 reviews

Course 3 of 5 in the Business and Financial Modeling Specialization

Useful quantitative models help you to make informed decisions both in situations in which the factors affecting your decision are clear, as well as in situations in which some important factors are not clear at all. In this course, you can learn how to create quantitative models to reflect complex realities, and how to include in your model elements of risk and uncertainty. You’ll also learn the methods for creating predictive models for identifying optimal choices; and how those choices change in response to changes in the model’s assumptions. You’ll also learn the basics of the measurement and management of risk. By the end of this course, you’ll be able to build your own models with your own data, so that you can begin making data-informed decisions. You’ll also be prepared for the next course in the Specialization.
Globe

Curso 100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.
Clock

Aprox. 9 horas para completar

Sugerido: 4 weeks of study, 1-3 hours/week
Comment Dots

English

Subtítulos: English, Portuguese (Brazilian)

Habilidades que obtendrás

SimulationStatisticsData AnalysisFinancial Modeling
Globe

Curso 100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.
Clock

Aprox. 9 horas para completar

Sugerido: 4 weeks of study, 1-3 hours/week
Comment Dots

English

Subtítulos: English, Portuguese (Brazilian)

Syllabus - What you will learn from this course

1

Section
Clock
2 hours to complete

Week 1: Modeling Decisions in Low Uncertainty Settings

This module is designed to teach you how to analyze settings with low levels of uncertainty, and how to identify the best decisions in these settings. You'll explore the optimization toolkit, learn how to build an algebraic model using an advertising example, convert the algebraic model to a spreadsheet model, work with Solver to discover the best possible decision, and examine an example that introduces a simple representation of risk to the model. By the end of this module, you'll be able to build an optimization model, use Solver to uncover the optimal decision based on your data, and begin to adjust your model to account for simple elements of risk. These skills will give you the power to deal with large models as long as the actual uncertainty in the input values is not too high....
Reading
4 videos (Total 60 min), 2 readings, 1 quiz
Video4 videos
1.1 How To Build an Optimization Model: Hudson Readers Ad Campaign13m
1.2 Optimizing with Solver, and Alternative Data Inputs26m
1.3 Adding Risk: Managing Investments at Epsilon Delta Capital18m
Reading2 readings
PDFs of Slides for Week 110m
Excel Files for Week 110m
Quiz1 practice exercises
Week 1: Modeling in Low Uncertainty Quiz20m

2

Section
Clock
2 hours to complete

Week 2: Risk and Reward: Modeling High Uncertainty Settings

What if uncertainty is the key feature of the setting you are trying to model? In this module, you'll learn how to create models for situations with a large number of variables. You'll examine high uncertainty settings, probability distributions, and risk, common scenarios for multiple random variables, how to incorporate risk reduction, how to calculate and interpret correlation values, and how to use scenarios for optimization, including sensitivity analysis and the efficient frontier. By the end of this module, you'll be able to identify and use common models of future uncertainty to build scenarios that help you optimize your business decisions when you have multiple variables and a higher degree of risk. ...
Reading
3 videos (Total 51 min), 2 readings, 1 quiz
Video3 videos
2.2 Common Scenarios for Multiple Random Variables, Risk Reduction, and Calculating and Interpreting Correlation Values18m
2.3 Using Scenarios for Optimizing Under High Uncertainty, Sensitivity Analysis and Efficient Frontier15m
Reading2 readings
PDFs of Lecture Slides for Week 210m
Excel Files for Week 210m
Quiz1 practice exercises
Week 2: Modeling in High Uncertainty Quiz20m

3

Section
Clock
2 hours to complete

Week 3: Choosing Distributions that Fit Your Data

When making business decisions, we often look to the past to make predictions for the future. In this module, you'll examine commonly used distributions of random variables to model the future and make predictions. You'll learn how to create meaningful data visualizations in Excel, how to choose the the right distribution for your data, explore the differences between discrete distributions and continuous distributions, and test your choice of model and your hypothesis for goodness of fit. By the end of this module, you'll be able to represent your data using graphs, choose the best distribution model for your data, and test your model and your hypothesis to see if they are the best fit for your data....
Reading
4 videos (Total 81 min), 2 readings, 1 quiz
Video4 videos
3.2, pt 1: Choosing Among Distributions: Discrete Distributions25m
3.2, pt 2: Choosing Among Distributions: Continuous Distributions11m
3.3 Hypothesis Testing and Goodness of Fit22m
Reading2 readings
PDFs of Lecture Slides for Week 310m
Excel Files for Week 310m
Quiz1 practice exercises
Week 3: Choosing Fitting Distributions Quiz20m

4

Section
Clock
2 hours to complete

Week 4: Balancing Risk and Reward Using Simulation

This module is designed to help you use simulations to enabling compare different alternatives when continuous distributions are used to describe uncertainty. Through an in-depth examination of the simulation toolkit, you'll learn how to make decisions in high uncertainty settings where random inputs are described by continuous probability distributions. You'll also learn how to run a simulation model, analyze simulation output, and compare alternative decisions to decide on the most optimal solution. By the end of this module, you'll be able to make decisions and manage risk using simulation, and more broadly, to make successful business decisions in an increasing complex and rapidly evolving business world....
Reading
4 videos (Total 53 min), 2 readings, 1 quiz
Video4 videos
4.2 Connecting Random Inputs and Random Outputs in a Simulation23m
4.3 Analyzing and Interpreting Simulation Output: Evaluating Alternatives Using Simulation Results10m
Course Conclusion0m
Reading2 readings
PDFs of Lecture Slides10m
Excel files for Week 410m
Quiz1 practice exercises
Week 4: Using Simulations Quiz20m
4.6
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20%

started a new career after completing these courses
Briefcase

83%

got a tangible career benefit from this course

Top Reviews

By JNApr 13th 2018

covers good amount of material and exactly what is in the outline, presented with enough detail to follow. Good walk-through of the spreadsheets helps understanding, easy to follow along and practice.

By LCDec 19th 2016

Material was very well presented. Week 3 was challenging, but taking time to print out the slides, work through them rigorously proved very helpful. I found all sections very, very informative.

Instructors

Avatar

Sergei Savin

Associate Professor of Operations, Information and Decisions
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Senthil Veeraraghavan

Associate Professor of Operations, Information and Decisions

About University of Pennsylvania

The University of Pennsylvania (commonly referred to as Penn) is a private university, located in Philadelphia, Pennsylvania, United States. A member of the Ivy League, Penn is the fourth-oldest institution of higher education in the United States, and considers itself to be the first university in the United States with both undergraduate and graduate studies. ...

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