About this course: 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.
Created by: University of Pennsylvania
Taught by: Sergei Savin, Associate Professor of Operations, Information and Decisions
The Wharton School
Taught by: Senthil Veeraraghavan, Associate Professor of Operations, Information and Decisions
The Wharton School
| Basic Info | Course 3 of 5 in the Business and Financial Modeling Specialization |
| Commitment | 4 weeks of study, 1-3 hours/week |
| Language | English, Subtitles: Portuguese (Brazilian) |
| How To Pass | Pass all graded assignments to complete the course. |
| User Ratings |
Syllabus
WEEK 1
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.
4 videos, 2 readings
- Video: Course Introduction
- Video: 1.1 How To Build an Optimization Model: Hudson Readers Ad Campaign
- Video: 1.2 Optimizing with Solver, and Alternative Data Inputs
- Video: 1.3 Adding Risk: Managing Investments at Epsilon Delta Capital
- Материал для самостоятельного изучения: PDFs of Slides for Week 1
- Материал для самостоятельного изучения: Excel Files for Week 1
Graded: Week 1: Modeling in Low Uncertainty Quiz
WEEK 2
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.
3 videos, 2 readings
- Video: 2.1 High Uncertainty Settings, Probability Distributions, Uncertainty and Risk
- Video: 2.2 Common Scenarios for Multiple Random Variables, Risk Reduction, and Calculating and Interpreting Correlation Values
- Video: 2.3 Using Scenarios for Optimizing Under High Uncertainty, Sensitivity Analysis and Efficient Frontier
- Материал для самостоятельного изучения: PDFs of Lecture Slides for Week 2
- Материал для самостоятельного изучения: Excel Files for Week 2
Graded: Week 2: Modeling in High Uncertainty Quiz
WEEK 3
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.
4 videos, 2 readings
- Video: 3.1 Data and Visualization: Graphical Representation
- Video: 3.2, pt 1: Choosing Among Distributions: Discrete Distributions
- Video: 3.2, pt 2: Choosing Among Distributions: Continuous Distributions
- Video: 3.3 Hypothesis Testing and Goodness of Fit
- Материал для самостоятельного изучения: PDFs of Lecture Slides for Week 3
- Материал для самостоятельного изучения: Excel Files for Week 3
Graded: Week 3: Choosing Fitting Distributions Quiz
WEEK 4
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.
4 videos, 2 readings
- Video: 4.1: Modeling Uncertainty: From Scenarios to Continuous Distributions
- Video: 4.2 Connecting Random Inputs and Random Outputs in a Simulation
- Video: 4.3 Analyzing and Interpreting Simulation Output: Evaluating Alternatives Using Simulation Results
- Video: Course Conclusion
- Материал для самостоятельного изучения: PDFs of Lecture Slides
- Материал для самостоятельного изучения: Excel files for Week 4
Graded: Week 4: Using Simulations Quiz
FAQs
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Creators
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.
Pricing
| Прослушать | Приобрести курс | |
|---|---|---|
| Получить доступ к материалам курса | Available | Available |
| Получить доступ к оцениваемым материалам курса | Not available | Available |
| Получить итоговую оценку | Not available | Available |
| Earn a shareable Course Certificate | Not available | Available |
Ratings and Reviews
Great course
Very helpful examples
Very useful for people are learning simulation or optimization.
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Exceptional delivery of course body of knowledge. Thank you to the team. Cheers!