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.
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教學大綱 - 您將從這門課程中學到什麼
週
1完成時間為 2 小時
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 個視頻 （總計 60 分鐘）, 2 個閱讀材料, 1 個測驗
4 個視頻
1.1 How To Build an Optimization Model: Hudson Readers Ad Campaign13分鐘
1.2 Optimizing with Solver, and Alternative Data Inputs26分鐘
1.3 Adding Risk: Managing Investments at Epsilon Delta Capital18分鐘
2 個閱讀材料
PDFs of Slides for Week 110分鐘
Excel Files for Week 110分鐘
1 個練習
Week 1: Modeling in Low Uncertainty Quiz20分鐘
週
2完成時間為 2 小時
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 個視頻 （總計 51 分鐘）, 2 個閱讀材料, 1 個測驗
3 個視頻
2.2 Common Scenarios for Multiple Random Variables, Risk Reduction, and Calculating and Interpreting Correlation Values18分鐘
2.3 Using Scenarios for Optimizing Under High Uncertainty, Sensitivity Analysis and Efficient Frontier15分鐘
2 個閱讀材料
PDFs of Lecture Slides for Week 210分鐘
Excel Files for Week 210分鐘
1 個練習
Week 2: Modeling in High Uncertainty Quiz20分鐘
週
3完成時間為 2 小時
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 個視頻 （總計 81 分鐘）, 2 個閱讀材料, 1 個測驗
4 個視頻
3.2, pt 1: Choosing Among Distributions: Discrete Distributions25分鐘
3.2, pt 2: Choosing Among Distributions: Continuous Distributions11分鐘
3.3 Hypothesis Testing and Goodness of Fit22分鐘
2 個閱讀材料
PDFs of Lecture Slides for Week 310分鐘
Excel Files for Week 310分鐘
1 個練習
Week 3: Choosing Fitting Distributions Quiz20分鐘
週
4完成時間為 2 小時
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 個視頻 （總計 53 分鐘）, 2 個閱讀材料, 1 個測驗
4 個視頻
4.2 Connecting Random Inputs and Random Outputs in a Simulation23分鐘
4.3 Analyzing and Interpreting Simulation Output: Evaluating Alternatives Using Simulation Results10分鐘
Course Conclusion25
2 個閱讀材料
PDFs of Lecture Slides10分鐘
Excel files for Week 410分鐘
1 個練習
Week 4: Using Simulations Quiz20分鐘
4.6
186 個審閱29%
完成這些課程後已開始新的職業生涯
24%
通過此課程獲得實實在在的工作福利
熱門審閱
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.
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.
講師
Sergei Savin
Associate Professor of Operations, Information and DecisionsThe Wharton School
Senthil Veeraraghavan
Associate Professor of Operations, Information and DecisionsThe Wharton School
關於 宾夕法尼亚大学
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.
關於 商业与金融建模 專項課程
Wharton's Business and Financial Modeling Specialization is designed to help you make informed business and financial decisions. These foundational courses will introduce you to spreadsheet models, modeling techniques, and common applications for investment analysis, company valuation, forecasting, and more. When you complete the Specialization, you'll be ready to use your own data to describe realities, build scenarios, and predict performance.
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