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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/wharton-risk-models
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课程名称：建模风险与现实 课程概述： 本课程将教授您如何使用有用的定量模型来在决策过程中考虑风险和不确定性，以便在因素清晰和不清晰的情况下做出明智的决策。您将学习如何构建反映复杂现实的定量模型，包括风险和不确定性元素。此外，您还将掌握创建预测模型的方法，以识别最佳选择及其在模型假设变化时的变动。课程结束时，您将能够使用自己的数据建立模型，从而作出基于数据的决策，为后续进阶课程做好准备。 课程大纲： 第一部分：第一周：低不确定性环境中的决策建模 本模块旨在帮助您分析低不确定性环境，并识别最佳决策。您将探索优化工具，学习如何使用广告示例构建代数模型，并将其转换为电子表格模型，运用Solver发现最佳决策，并引入简单的风险表示。通过本模块，您将具备建立优化模型的能力，并且能够利用Solver根据数据揭示最佳决策，同时开始调整模型以考虑简单的风险因素。 第二部分：第二周：风险与回报：高不确定性环境建模 在高不确定性设置下，您将学习如何创建模型以应对多变量情形。您将研究高不确定性设置、概率分布和风险，常见的多随机变量场景，如何减少风险，计算和解释相关值，以及如何运用敏感性分析和有效前沿进行优化。完成本模块后，您将能够识别并应用未来不确定性的常用模型来构建场景，以帮助优化在多变量和更高风险下的商业决策。 第三部分：第三周：选择适合您数据的分布 本模块将探讨常用的随机变量分布，以帮助预测未来。这包括创建有意义的数据可视化，选择适合您数据的最佳分布，区分离散分布和连续分布，并测试模型与假设的拟合优度。学习结束后，您将能使用图形表示数据，选择最合适的分布模型，并测试这些模型以验证其贴合度。 第四部分：第四周：运用模拟平衡风险与回报 本模块旨在帮助您使用模拟工具比较在连续分布中描述不确定性时的不同选择。通过深入研究模拟工具，您将学习如何在高不确定性环境中做出决策，分析模拟结果，并比较替代方案以确定最优解决方案。通过本模块的学习，您将能够运用模拟管理风险，并在日益复杂和快速发展的商业环境中做出成功的商业决策。
Part: 1
Title:Week 1: Modeling Decisions in Low Uncertainty Settings
Description: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.
Part: 2
Title:Week 2: Risk and Reward: Modeling High Uncertainty Settings
Description: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.
Part: 3
Title:Week 3: Choosing Distributions that Fit Your Data
Description: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.
Part: 4
Title:Week 4: Balancing Risk and Reward Using Simulation
Description: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.
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.
