关于此 专项课程

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R is a programming language and a free software environment for statistical computing and graphics, widely used by data analysts, data scientists and statisticians. This Specialization covers R software development for building data science tools. As the field of data science evolves, it has become clear that software development skills are essential for producing and scaling useful data science results and products.

This Specialization will give you rigorous training in the R language, including the skills for handling complex data, building R packages, and developing custom data visualizations. You’ll be introduced to indispensable R libraries for data manipulation, like tidyverse, and data visualization and graphics, like ggplot2. You’ll learn modern software development practices to build tools that are highly reusable, modular, and suitable for use in a team-based environment or a community of developers.

This Specialization is designed to serve both data analysts, who may want to gain more familiarity with hands-on, fundamental software skills for their everyday work, as well as data mining experts and data scientists, who may want to use R to scale their developing and programming skills, and further their careers as data science experts.

学生职业成果
46%
完成此 专项课程 后开始了新的职业。
13%
加薪或升职。
可分享的证书
完成后获得证书
100% 在线课程
立即开始,按照自己的计划学习。
灵活的计划
设置并保持灵活的截止日期。
初级
完成时间大约为6 个月
建议 4 小时/周
英语(English)
字幕:英语(English), 法语(French), 俄语(Russian), 中文(简体), 阿拉伯语(Arabic), 越南语, 德语(German), 格鲁吉亚语, 爱沙尼亚语, 泰语, 日语, 尼泊尔语...
学生职业成果
46%
完成此 专项课程 后开始了新的职业。
13%
加薪或升职。
可分享的证书
完成后获得证书
100% 在线课程
立即开始,按照自己的计划学习。
灵活的计划
设置并保持灵活的截止日期。
初级
完成时间大约为6 个月
建议 4 小时/周
英语(English)
字幕:英语(English), 法语(French), 俄语(Russian), 中文(简体), 阿拉伯语(Arabic), 越南语, 德语(German), 格鲁吉亚语, 爱沙尼亚语, 泰语, 日语, 尼泊尔语...

此专项课程包含 5 门课程

课程1

课程 1

The R Programming Environment

4.4
981 个评分
262 条评论
课程2

课程 2

Advanced R Programming

4.3
485 个评分
121 条评论
课程3

课程 3

Building R Packages

4.1
191 个评分
51 条评论
课程4

课程 4

Building Data Visualization Tools

3.9
142 个评分
38 条评论

提供方

约翰霍普金斯大学 徽标

约翰霍普金斯大学

常见问题

  • If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.

  • Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate, or you can audit it to view the course materials for free. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.

  • Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.

  • 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. If you only want to read and view the course content, you can audit the course for free. If you cannot afford the fee, you can apply for financial aid.

  • This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.

  • Time to completion can vary based on your schedule, but most learners are able to complete the Specialization in 3-6 months.

  • Some programming experience (in any language) is recommended. We also suggest a working knowledge of mathematics up to algebra (neither calculus or linear algebra are required).

  • We strongly recommend that you take the courses in order.

  • Coursera courses and certificates don't carry university credit, though some universities may choose to accept Specialization Certificates for credit. Check with your institution to learn more.

  • You will be able to use R to create new data science tools as part of a team or a community of developers. You will be able to build R packages, develop custom visualizations, and apply modern software development tools to create reusable code for solving data science problems.

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