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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/analytics-excel
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课程名称：掌握 Excel 数据分析 课程概述：本课程重点在于数学，特别是数据分析的概念和方法，而不是单纯的 Excel 技能。课程中使用 Excel 进行计算，并提供相关的Excel电子表格公式，但不涉及 Excel 宏、Visual Basic、数据透视表或其他中高级功能。 通过本课程，您将学习如何设计和实施基于数据的现实预测模型。在最终项目（第六模块）中，您将扮演一家银行的商业数据分析师，开发两种不同的预测模型，以决定哪些信用卡申请者应被批准或拒绝。第一个模型专注于最小化违约风险，第二个模型则聚焦于最大化银行利润。这两个模型将帮助您实践理解商业指标选择如何驱动最佳模型的选择。 课程的另一个重要理念是，数据分析结果不能也不应该消除所有的不确定性。您的角色是通过财务上有价值的增量来减少决策者的不确定性，同时量化剩余的不确定性。您将学习如何计算和应用商业中使用的最重要的不确定性度量，包括分类错误率、信息熵以及线性回归的置信区间。 课程提供所有所需数据，所有作业均在MS Excel中完成，并且您将学习足够的Excel知识以完成所有作业。课程将使您熟悉Excel中最常用的商业函数，并为将来学习其他Excel功能做好准备（模块一）。 课程不包括Visual Basic或数据透视表，您不需要掌握这些才能完成作业。所有高级概念均在单独的Excel电子表格模板中演示，帮助您回答相关问题。您将掌握如何应用基于二元分类的商业数据分析方法（模块二）、信息论和熵度量（模块三）以及线性回归（模块四和五）的实用知识。 课程大纲： 1. **Excel基础入门**：学习应对常见商业问题所需的基本Excel技能，实现对后续视频中附带的Excel电子表格的理解。 2. **二元分类**：学习如何将数据分为两类，并运用各种度量标准来比较和优化分类算法的性能。 3. **信息度量**：学习计算和应用不确定性度量“熵”，量化所有可能结果的总体不确定性。 4. **线性回归**：理解线性相关性度量及其在减少不确定性、预测未来结果方面的运用，以及如何利用中心极限定理解决实际问题。 5. **模型构建的附加技能**：获取与高质量模型构建相关的额外概念和技能。 6. **最终课程项目**：综合评估所有课程材料，包括四个测试和一个同侪评审作业，预计完成时间为10-12小时。
Part: 1
Title:Excel Essentials for Beginners
Description:In this module, will explore the essential Excel skills to address typical business situations you may encounter in the future. The Excel vocabulary and functions taught throughout this module make it possible for you to understand the additional explanatory Excel spreadsheets that accompany later videos in this course.
Part: 2
Title:Binary Classification
Description:Separating collections into two categories, such as “buy this stock, don’t but that stock” or “target this customer with a special offer, but not that one” is the ultimate goal of most business data-analysis projects. There is a specialized vocabulary of measures for comparing and optimizing the performance of the algorithms used to classify collections into two groups. You will learn how and why to apply these different metrics, including how to calculate the all-important AUC: the area under the Receiver Operating Characteristic (ROC) Curve.
Part: 3
Title:Information Measures
Description:In this module, you will learn how to calculate and apply the vitally useful uncertainty metric known as “entropy.” In contrast to the more familiar “probability” that represents the uncertainty that a single outcome will occur, “entropy” quantifies the aggregate uncertainty of all possible outcomes.
Part: 4
Title:Linear Regression
Description:The Linear Correlation measure is a much richer metric for evaluating associations than is commonly realized. You can use it to quantify how much a linear model reduces uncertainty. When used to forecast future outcomes, it can be converted into a “point estimate” plus a “confidence interval,” or converted into an information gain measure. You will develop a fluent knowledge of these concepts and the many valuable uses to which linear regression is put in business data analysis. This module also teaches how to use the Central Limit Theorem (CLT) to solve practical problems. The two topics are closely related because regression and the CLT both make use of a special family of probability distributions called “Gaussians.” You will learn everything you need to know to work with Gaussians in these and other contexts.
Part: 5
Title:Additional Skills for Model Building
Description:This module gives you additional valuable concepts and skills related to building high-quality models.
Part: 6
Title:Final Course Project
Description:The final course project is a comprehensive assessment covering all of the course material, and consists of four quizzes and a peer review assignment. For quiz one and quiz two, there are learning points that explain components of the quiz. These learning points will unlock only after you complete the quiz with a passing grade. Before you start, please read through the final project instructions. From past student experience, the final project which includes all the quizzes and peer assessment, takes anywhere from 10-12 hours.
Important: The focus of this course is on math - specifically, data-analysis concepts and methods - not on Excel for its own sake. We use Excel to do our calculations, and all math formulas are given as Excel Spreadsheets, but we do not attempt to cover Excel Macros, Visual Basic, Pivot Tables, or other intermediate-to-advanced Excel functionality. This course will prepare you to design and implement realistic predictive models based on data. In the Final Project (module 6) you will assume the role of a business data analyst for a bank, and develop two different predictive models to determine which applicants for credit cards should be accepted and which rejected. Your first model will focus on minimizing default risk, and your second on maximizing bank profits. The two models should demonstrate to you in a practical, hands-on way the idea that your choice of business metric drives your choice of an optimal model. The second big idea this course seeks to demonstrate is that your data-analysis results cannot and should not aim to eliminate all uncertainty. Your role as a data-analyst is to reduce uncertainty for decision-makers by a financially valuable increment, while quantifying how much uncertainty remains. You will learn to calculate and apply to real-world examples the most important uncertainty measures used in business, including classification error rates, entropy of information, and confidence intervals for linear regression. All the data you need is provided within the course, all assignments are designed to be done in MS Excel, and you will learn enough Excel to complete all assignments. The course will give you enough practice with Excel to become fluent in its most commonly used business functions, and you’ll be ready to learn any other Excel functionality you might need in the future (module 1). The course does not cover Visual Basic or Pivot Tables and you will not need them to complete the assignments. All advanced concepts are demonstrated in individual Excel spreadsheet templates that you can use to answer relevant questions. You will emerge with substantial vocabulary and practical knowledge of how to apply business data analysis methods based on binary classification (module 2), information theory and entropy measures (module 3), and linear regression (module 4 and 5), all using no software tools more complex than Excel.
