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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/tidyverse-modelling-data
课程评论：没有评论
课程名称：在Tidyverse中建模数据 概述：有效的数据建模和分析是获取有关组织、业务或研究项目洞察的关键。成功的模型构建需要理解可提问的不同问题类型，并将这些问题映射到数据上。本课程将涵盖你可以向数据提出的各种问题及各类建模方法，包括假设检验、线性回归、非线性建模与机器学习等。通过掌握这些工具和在此专业化课程中学到的技术，你将能够从数据中发现关键见解，以提高组织的决策效率。 我们假设学习者熟悉R编程语言，如果尚不熟悉R，建议先完成《R编程》课程后再返回学习本课程。 课程大纲： 1. **建模数据基础**：介绍有效建模和分析数据的基本要求，理解不同问题类型与数据之间的关系。 2. **推断分析**：在描述和探索数据集后，使用统计测试进行推断和预测分析的基本方法。 3. **线性建模**：探讨线性模型的普遍使用及其在数据分析中的重要性，专项介绍tidyverse的相关工具。 4. **多重线性回归**：展示如何在模型中包含混杂因素或其他预测因子，使用lm()函数提供的公式接口。 5. **超越线性回归**：讨论除线性回归之外的其他分析方法，及其与线性回归的关联。 6. **假设检验**：介绍用于确定数据是否支持未知参数值的统计技术，强调在推断时考虑数据变异性的重要性。 7. **预测建模**：构建基于已观察数据进行预测的系统，强调其灵活性和捕捉不同关系的能力。 8. **tidymodels生态系统**：介绍RStudio开发的tidymodels R包，将许多机器学习算法纳入单一框架，从而简化使用。 9. **案例研究**：展示如何建立预测模型，以预测美国的户外空气污染浓度。 10. **tidymodels总结**：提供tidymodels包的快速总结图表，帮助用户导航及使用。 11. **项目：在Tidyverse中建模数据**：实践应用tidyverse构建消费者投诉数据的分类模型，包含同行评审和测验环节。 通过本课程，学习者将获得实用的建模技能，提升其在数据科学领域的决策能力。
Part: 1
Title:Modeling Data Basics
Description:Developing insights about your organization, business, or research project depends on effective modeling and analysis of the data you collect. Building effective models requires understanding the different types of questions you can ask and how to map those questions to your data. Different modeling approaches can be chosen to detect interesting patterns in the data and identify hidden relationships.
Part: 2
Title:Inference
Description:Inferential Analysis is what analysts carry out after they’ve described and explored their dataset. After understanding your dataset better, analysts often try to infer something from the data. This is done using statistical tests. We discussed a bit about how we can use models to perform inference and prediction analyses. What does this mean?
Part: 3
Title:Linear Modeling
Description:Linear models are the most commonly used models in data analysis because of their computational efficiency and their ease of interpretation. Having a solid understanding of linear models and how they work is critical for any work in data science. The tidyverse provides a set of tools for making linear modeling more efficient and streamlined.
Part: 4
Title:Multiple Linear Regression
Description:Multiple linear regression is needed when you want to include confounding factors or other predictors in your model for the response. R provides a straightforward way to do this via the formula interface to the lm() function.
Part: 5
Title:Beyond Linear Regression
Description:While we’ve focused on linear regression in this lesson on inference, linear regression isn’t the only analytical approach out there. However, it is arguably the most commonly used. And, beyond that, there are many statistical tests and approaches that are slight variations on linear regression, so having a solid foundation and understanding of linear regression makes understanding these other tests and approaches much simpler. For example, what if you didn’t want to measure the linear relationship between two variables, but instead wanted to know whether or not the average observed is different from expectation?
Part: 6
Title:Hypothesis Testing
Description:Hypothesis testing describes a family of statistical techniques for determining whether the data you collect provides evidence for the value of an unknown parameter of interest. The goal of hypothesis tests is to make inferences while accounting for variability in the data that can lead to spurious results.
Part: 7
Title:Prediction Modeling
Description:Prediction modeling is an essential activity in data science and involves building systems for making predictions based on previously observed data. These models are typically very flexible and can capture a range of different relationships.
Part: 8
Title:The tidymodels Ecosystem
Description:There are incredibly helpful packages available in R thanks to the work of RStudio. As mentioned above, there are hundreds of different machine learning algorithms. The tidymodels R packages have put many of them into a single framework, allowing you to use many different machine learning models easily.
Part: 9
Title:Case Studies
Description:This case study will demonstrate an approach to building a prediction model for predicting outdoor air pollution concentrations in the United States.
Part: 10
Title:Summary of tidymodels
Description:The tidymodels collection of packages can be overwhelming at first glance. Here, we provide a quick summary chart to help navigate all of the packages and when they should be used.
Part: 11
Title:Project: Modeling Data in the Tidyverse
Description:In this project, you will practice building models with the tidyverse for classifying consumer complaints data from the Consumer Financial Protection Bureau (CFPB). This project includes both a Peer Review step in which you'll upload R Markdown and knitted HTML files AND a Quiz step in which you'll answer questions about the predictions made by your classification algorithm.
Developing insights about your organization, business, or research project depends on effective modeling and analysis of the data you collect. Building effective models requires understanding the different types of questions you can ask and how to map those questions to your data. Different modeling approaches can be chosen to detect interesting patterns in the data and identify hidden relationships. This course covers the types of questions you can ask of data and the various modeling approaches that you can apply. Topics covered include hypothesis testing, linear regression, nonlinear modeling, and machine learning. With this collection of tools at your disposal, as well as the techniques learned in the other courses in this specialization, you will be able to make key discoveries from your data for improving decision-making throughout your organization. In this specialization we assume familiarity with the R programming language. If you are not yet familiar with R, we suggest you first complete R Programming before returning to complete this course.
