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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/regression-analysis-simplify-complex-data-relationships
课程评论：没有评论
课程名称：回归分析：简化复杂数据关系 概述：本课程是谷歌高级数据分析证书七个课程中的第五个。数据专业人士使用回归分析来发现数据集中不同变量之间的关系，并识别影响商业绩效的关键因素。在本课程中，您将实践建模变量关系，学习不同的数据建模方法，并如何将其应用于商业问题。课程中您还将探索线性回归、方差分析等多种方法。 课程大纲： 1. **复杂数据关系的介绍**：您将开始了解构建回归模型的主要步骤，从识别假设到解释结果。接下来，您将探讨两种主要的回归类型：线性回归和逻辑回归，了解数据专业人士如何利用它们解决不同的商业问题。 2. **简单线性回归**：您将探索如何使用模型描述复杂的数据关系，重点关注相关性的关系。然后，您将在Python中构建简单线性回归模型，并解读结果。 3. **多元线性回归**：在学习完简单回归后，您将继续学习更复杂的回归模型：多元线性回归。您将考虑多元回归如何在建模过程中每一步都建立在简单线性回归之上，并预览机器学习中的一些关键主题：选择、过拟合和偏差-方差权衡。 4. **高级假设检验**：您将基于之前的假设检验知识，探索另外两种统计检验：卡方检验和方差分析（ANOVA）。您将学习数据专业人士如何使用这些检验来分析不同类型的数据，最终进行两种卡方检验以及单因素和双因素ANOVA检验。 5. **逻辑回归**：您将研究二项逻辑回归，这是一种将数据分类为两类的回归分析。您将学习如何构建二项逻辑回归模型，以及数据专业人士如何利用此类模型从数据中获取洞见。 6. **课程项目**：最后，您将通过构建一个回归模型来分析一个工作场景数据集，完成本课程的项目。 通过上述学习，您将掌握回归分析的基本技术，并能够在复杂的数据环境中进行有效的数据建模和分析。
Name:Introduction to complex data relationships
Description:You’ll begin by exploring the main steps for building regression models, from identifying your assumptions to interpreting your results. Next, you’ll explore the two main types of regression: linear and logistic. You’ll learn how data professionals use linear and logistic regression to approach different kinds of business problems.
Name:Simple linear regression
Description:You’ll explore how to use models to describe complex data relationships. You’ll focus on relationships of correlation. Then, you’ll build a simple linear regression model in Python and interpret your results.
Name:Multiple linear regression
Description:After simple regression, you’ll move on to a more complex regression model: multiple linear regression. You’ll consider how multiple regression builds on simple linear regression at every step of the modeling process. You’ll also get a preview of some key topics in machine learning: selection, overfitting, and the bias-variance tradeoff.
Name:Advanced hypothesis testing
Description:You’ll build on your prior knowledge of hypothesis testing to explore two more statistical tests: Chi-squared and analysis of variance (ANOVA). You’ll learn how data professionals use these tests to analyze different types of data. Finally, you’ll conduct two kinds of Chi-squared tests, as well as one-way and two-way ANOVA tests.
Name:Logistic regression
Description:You’ll investigate binomial logistic regression, a type of regression analysis that classifies data into two categories. You’ll learn how to build a binomial logistic regression model and how data professionals use this type of model to gain insights from their data.
Name:Course 5 end-of-course project
Description:You’ll complete an end-of-course project by building a regression model to analyze a workplace scenario dataset.
This is the fifth of seven courses in the Google Advanced Data Analytics Certificate. Data professionals use regression analysis to discover the relationships between different variables in a dataset and identify key factors that affect business performance. In this course, you’ll practice modeling variable relationships. You'll learn about different methods of data modeling and how to use them to approach business problems. You’ll also explore methods such as linear regression, analysis of vari
