Fundamentals of Quantitative Modeling

所在平台: Coursera

课程主页: https://www.coursera.org/learn/wharton-quantitative-modeling

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课程简介

课程名称：定量建模基础 课程概述：如何有效利用数据？具体而言，电子表格中的数字如何反映过去和现在的商业活动，以及我们如何利用这些数据预测未来？答案在于建立定量模型， 本课程旨在帮助您理解这一关键的核心商业技能。通过一系列简短的讲座、示范和作业，您将学习定量建模的关键概念和流程，以便开始为自己的企业创建模型。在本课程结束时，您将接触到多种实用的常用定量模型，以及构建您自己模型所需的基本构件。这些基本构件将在本专项课程的其他课程中得到应用。 课程大纲： 模块1：模型简介 您将学习如何定义模型，模型的常见用途，以及建模过程的核心步骤、四种关键的数学函数和描述模型所需的基本术语。完成此模块后，您将能够识别四种最常见的模型及其使用时机，并掌握建模的核心术语，为进一步学习打下基础，提升您参与定量模型相关讨论的能力。 模块2：线性模型与优化 本模块介绍了线性模型，这是几乎所有建模的基础。通过对线性模型的常见用途进行详细研究，您将学习如何将线性模型（包括成本函数和生产函数）应用于自己的业务。此外，本模块还包括关于离散时间和连续时间的增长与衰退过程的讲解，以及优化技术的经典讨论。完成此模块后，您将能够识别和理解线性模型的关键结构，并提出改善业务结果的建议，同时能够进行与估值指标相关的现值计算。 模块3：概率模型 本模块讲解了概率模型，这是捕捉过程风险的一种方式。当您无法确定所有输入时，概率模型便派上用场。您将研究概率模型如何融入不确定性，以及这种不确定性如何传递到模型输出中。你会发现，通过传播不确定性，可以确定预测的值范围。本模块还介绍了最常用的风险模型，包括回归模型、基于树的模型、Monte Carlo模拟和马尔可夫链，以及这些概率模型的基本构件，如随机变量、概率分布等。完成此模块后，您将能够定义概率模型，并识别和理解常用的概率模型，判断哪些模型最适合用来捕捉和探讨您自己业务中的风险。 模块4：回归模型 本模块探讨了回归模型，这些模型使您能够从数据出发，发现潜在的过程。回归模型是预测分析的关键工具，并用于在处理数据中的不确定性时。您将深入了解回归模型的定义、功能及其局限性、所能回答的问题，以及回归系数的解释、线性关联的相关性和最优拟合线的方法。您还将学习多重回归和逻辑回归，并了解逻辑回归如何帮助估算成功的概率。完成此模块后，您将能够识别回归模型及其关键组成部分，理解其使用场合，并能够解读模型，以便与他人讨论并证明您的模型具有合理性，以实现实施的目标。

课程大纲

Name:Module 1: Introduction to Models

Description:In this module, you will learn how to define a model, and how models are commonly used. You’ll examine the central steps in the modeling process, the four key mathematical functions used in models, and the essential vocabulary used to describe models. By the end of this module, you’ll be able to identify the four most common types of models, and how and when they should be used. You’ll also be able to define and correctly use the key terms of modeling, giving you not only a foundation for further study, but also the ability to ask questions and participate in conversations about quantitative models.

Name:Module 2: Linear Models and Optimization

Description:This module introduces linear models, the building block for almost all modeling. Through close examination of the common uses together with examples of linear models, you’ll learn how to apply linear models, including cost functions and production functions to your business. The module also includes a presentation of growth and decay processes in discrete time, growth and decay in continuous time, together with their associated present and future value calculations. Classical optimization techniques are discussed. By the end of this module, you’ll be able to identify and understand the key structure of linear models, and suggest when and how to use them to improve outcomes for your business. You’ll also be able to perform present value calculations that are foundational to valuation metrics. In addition, you will understand how you can leverage models for your business, through the use of optimization to really fine tune and optimize your business functions.

Name:Module 3: Probabilistic Models

Description:This module explains probabilistic models, which are ways of capturing risk in process. You’ll need to use probabilistic models when you don’t know all of your inputs. You’ll examine how probabilistic models incorporate uncertainty, and how that uncertainty continues through to the outputs of the model. You’ll also discover how propagating uncertainty allows you to determine a range of values for forecasting. You’ll learn the most-widely used models for risk, including regression models, tree-based models, Monte Carlo simulations, and Markov chains, as well as the building blocks of these probabilistic models, such as random variables, probability distributions, Bernoulli random variables, binomial random variables, the empirical rule, and perhaps the most important of all of the statistical distributions, the normal distribution, characterized by mean and standard deviation. By the end of this module, you’ll be able to define a probabilistic model, identify and understand the most commonly used probabilistic models, know the components of those models, and determine the most useful probabilistic models for capturing and exploring risk in your own business.

Name:Module 4: Regression Models

Description:This module explores regression models, which allow you to start with data and discover an underlying process. Regression models are the key tools in predictive analytics, and are also used when you have to incorporate uncertainty explicitly in the underlying data. You’ll learn more about what regression models are, what they can and cannot do, and the questions regression models can answer. You’ll examine correlation and linear association, methodology to fit the best line to the data, interpretation of regression coefficients, multiple regression, and logistic regression. You’ll also see how logistic regression will allow you to estimate probabilities of success. By the end of this module, you’ll be able to identify regression models and their key components, understand when they are used, and be able to interpret them so that you can discuss your model and convince others that your model makes sense, with the ultimate goal of implementation.

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