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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/marketing-analytics-project
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课程名称：营销分析顶点项目 概述：本顶点项目将为您提供一个机会，以应用我们在营销分析基础课程中所涵盖的内容。到项目结束时，您将进行探索性数据分析，检查不同变量之间的双重关系，并开发和测试预测模型，以解决营销分析问题。强烈建议您在开始顶点课程之前完成营销分析基础课程中的所有课程。 课程大纲： 1. **营销分析项目简介** 本模块将定义营销分析顶点项目的目标和活动。 2. **探索性分析** 在此模块中，我们将开始审查单个变量及其与贷款状态的关系。本模块包含来自专业化课程的复习材料，虽然不是必需的，但推荐您重温这些内容。 3. **数据准备与模型构建** 本模块将重点讨论如何使用逻辑回归构建分类模型，这是解决二元因变量营销问题的常用工具。我们将从选择单一预测变量开始，然后确定需要添加到分析中的其他变量，着重开发所有包含单一预测变量的备选模型。 4. **模型验证与比较** 在上一模块中，我们估计了与家庭拥有权相关的模型，判断贷款风险。在本模块中，我们将首先评估该模型相对于简单模型的准确性，并利用电子表格评估在使用不同预测变量时模型的表现。 5. **整合多个预测变量** 在本模块中，我们将推广之前开发的逻辑回归工具，以纳入多个预测变量。此外，我们还将考虑评估模型性能的替代方法。 6. **恭喜您！** 本模块提供了大卫·施韦德尔教授的最后祝贺视频。 通过此顶点项目，您将全面应用所学知识，提升营销分析技能，并为未来的分析挑战做好准备。
Part: 1
Title:Marketing Analytics Project Description
Description:This module will define the goals and activities for the marketing analytics capstone project.
Part: 2
Title:Exploratory Analysis
Description:In this module, we will begin to examine individual variables and their relationship to the status of the loan. Note, this module includes review items from previous courses in the specialization. This content is not required, but recommended as content to revisit.
Part: 3
Title:Data Preparation and Model Building
Description:While there are many ways to build a classification model, we will focus on using logistic regression, a common tool for marketing problems in which the dependent variable is binary. We will begin by choosing a single predictor variable and then determine which other variables need to be added to our analysis. In this module, we will focus on developing alternative models that all have a single predictor.
Part: 4
Title:Model Validation and Comparison
Description:In the previous module, we estimated a model linking home ownership to whether or not a loan is considered risky. In this module, we will begin by assessing the accuracy of this model relative to a naïve model. We will then use this spreadsheet as a means of assessing how well the model performs when different predictors are used.
Part: 5
Title:Incorporating Multiple Predictor Variables
Description:In this module, we will generalize the logistic regression tool that was developed to include multiple predictor variables. We will also consider an alternative means of evaluating the performance of the model.
Part: 6
Title:Congratulations!
Description:This module provides a final congratulatory video from Professor David Schweidel.
This capstone project will give you an opportunity to apply what we have covered in the Foundations of Marketing Analytics specialization. By the end of this capstone project, you will have conducted exploratory data analysis, examined pairwise relationships among different variables, and developed and tested a predictive model to solve a marketing analytics problem. It is highly recommended that you complete all courses within the Foundations of Marketing Analytics specialization before starting the capstone course.
