Learn how to move from exploring data to modeling it with confidence. In this course, you’ll build and interpret linear and logistic regression models in R to uncover relationships, make predictions, and quantify uncertainty.
Data Modeling and Prediction with R
您将学到什么
Fit and interpret linear and logistic regression models to examine relationships between predictors and outcomes.
Evaluate model performance and recognize limitations such as overfitting.
Apply bootstrapping and hypothesis testing to quantify and communicate uncertainty in model results.
您将获得的技能
要了解的详细信息
可分享的证书
添加到您的领英档案
最近已更新！
October 2025
作业
4 项作业
授课语言：英语（English）
了解顶级公司的员工如何掌握热门技能
积累特定领域的专业知识
本课程是 Data Science with R 专项课程 专项课程的一部分
在注册此课程时，您还会同时注册此专项课程。
- 向行业专家学习新概念
- 获得对主题或工具的基础理解
- 通过实践项目培养工作相关技能
- 获得可共享的职业证书
该课程共有4个模块
In this module, you will learn how to describe relationships between variables using simple linear regression. You’ll practice fitting models, interpreting coefficients, and visualizing patterns to uncover meaningful insights from data. By the end of this module, you’ll know how to make predictions and identify when your model might not fit as well as you think.
涵盖的内容
6个视频8篇阅读材料1个作业1个插件
Real-world data is rarely simple. In this module, you’ll extend regression modeling to include multiple predictors and interaction effects. You’ll explore how adding variables improves model accuracy, how to interpret complex relationships, and how to avoid overfitting as your models become more sophisticated.
涵盖的内容
3个视频4篇阅读材料1个作业1个插件
Not all outcomes are numerical. In this module, you’ll learn how to model categorical outcomes (e.g., “yes/no” or “spam/not spam”) using logistic regression. You’ll discover how to calculate probabilities, classify outcomes, and assess the performance of your models. Along the way, you’ll explore how overfitting affects classification and reflect on how to interpret and communicate model predictions responsibly.
涵盖的内容
5个视频6篇阅读材料1个作业1个插件
Every model comes with uncertainty and understanding and communicating that uncertainty is what makes you a thoughtful data scientist. In this final module, you’ll explore bootstrapping and randomization methods to measure confidence in your results, conduct hypothesis tests, and communicate findings transparently. By the end, you’ll bring together your modeling and inference skills to draw clear, data-driven conclusions.
涵盖的内容
4个视频5篇阅读材料1个作业1个插件
获得职业证书
将此证书添加到您的 LinkedIn 个人资料、简历或履历中。在社交媒体和绩效考核中分享。
从 Data Analysis 浏览更多内容
状态：预览
状态：免费试用Johns Hopkins University
人们为什么选择 Coursera 来帮助自己实现职业发展
Felipe M.
自 2018开始学习的学生
''能够按照自己的速度和节奏学习课程是一次很棒的经历。只要符合自己的时间表和心情，我就可以学习。'
Jennifer J.
自 2020开始学习的学生
''我直接将从课程中学到的概念和技能应用到一个令人兴奋的新工作项目中。'
Larry W.
自 2021开始学习的学生
''如果我的大学不提供我需要的主题课程，Coursera 便是最好的去处之一。'
Chaitanya A.
''学习不仅仅是在工作中做的更好：它远不止于此。Coursera 让我无限制地学习。'
常见问题
To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
更多问题
提供助学金，