Data ScienceMachine LearningGithubR ProgrammingExploratory Data AnalysisRstudioData AnalysisDebuggingData ManipulationRegular Expression (REGEX)Data CleansingCluster Analysis
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提供方
您将学到的内容有
Use R to clean, analyze, and visualize data.
Learn how to ask the right questions, obtain data, and perform reproducible research.
Use GitHub to manage data science projects.
您将获得的技能
关于此 专项课程
101,025 次近期查看
Ask the right questions, manipulate data sets, and create visualizations to communicate results.
This Specialization covers foundational data science tools and techniques, including getting, cleaning, and exploring data, programming in R, and conducting reproducible research. Learners who complete this specialization will be prepared to take the Data Science: Statistics and Machine Learning specialization, in which they build a data product using real-world data.
The five courses in this specialization are the very same courses that make up the first half of the Data Science Specialization. This specialization is presented for learners who want to start and complete the foundational part of the curriculum first, before moving onto the more advanced topics in Data Science: Statistics and Machine Learning.
应用的学习项目
In taking the Data Science: Foundations using R Specialization, learners will complete a project at the ending of each course in this specialization. Projects include, installing tools, programming in R, cleaning data, performing analyses, as well as peer review assignments.
学生职业成果
39%
完成此 专项课程 后开始了新的职业。
15%
加薪或升职。
学生职业成果
39%
完成此 专项课程 后开始了新的职业。
15%
加薪或升职。
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
无需相关领域的预备知识无需相关经验。
完成时间大约为4 个月
建议 6 小时/周
英语（English）
字幕：英语（English）, 韩语, 阿拉伯语（Arabic）, 法语（French）, 中文（简体）, 巴西葡萄牙语, 越南语, 西班牙语（Spanish）, 日语, 俄语（Russian）
专项课程的运作方式
加入课程
Coursera 专项课程是帮助您掌握一门技能的一系列课程。若要开始学习，请直接注册专项课程，或预览专项课程并选择您要首先开始学习的课程。当您订阅专项课程的部分课程时，您将自动订阅整个专项课程。您可以只完成一门课程，您可以随时暂停学习或结束订阅。访问您的学生面板，跟踪您的课程注册情况和进度。
实践项目
每个专项课程都包括实践项目。您需要成功完成这个（些）项目才能完成专项课程并获得证书。如果专项课程中包括单独的实践项目课程，则需要在开始之前完成其他所有课程。
获得证书
在结束每门课程并完成实践项目之后，您会获得一个证书，您可以向您的潜在雇主展示该证书并在您的职业社交网络中分享。
此专项课程包含 5 门课程
数据科学家的工具箱（中文版）
22,483 个评分
In this course you will get an introduction to the main tools and ideas in the data scientist's toolbox. The course gives an overview of the data, questions, and tools that data analysts and data scientists work with. There are two components to this course. The first is a conceptual introduction to the ideas behind turning data into actionable knowledge. The second is a practical introduction to the tools that will be used in the program like version control, markdown, git, GitHub, R, and RStudio.
R 语言程序设计（中文版）
16,177 个评分
In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. Topics in statistical data analysis will provide working examples.
获取和整理数据
6,765 个评分
Before you can work with data you have to get some. This course will cover the basic ways that data can be obtained. The course will cover obtaining data from the web, from APIs, from databases and from colleagues in various formats. It will also cover the basics of data cleaning and how to make data “tidy”. Tidy data dramatically speed downstream data analysis tasks. The course will also cover the components of a complete data set including raw data, processing instructions, codebooks, and processed data. The course will cover the basics needed for collecting, cleaning, and sharing data.
探索性数据分析
5,150 个评分
This course covers the essential exploratory techniques for summarizing data. These techniques are typically applied before formal modeling commences and can help inform the development of more complex statistical models. Exploratory techniques are also important for eliminating or sharpening potential hypotheses about the world that can be addressed by the data. We will cover in detail the plotting systems in R as well as some of the basic principles of constructing data graphics. We will also cover some of the common multivariate statistical techniques used to visualize high-dimensional data.
提供方
约翰霍普金斯大学
The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.
审阅
4.6
来自DATA SCIENCE: FOUNDATIONS USING R的热门评论
由 WH 提供Feb 2, 2016
"R Programming" forces you to dive in deep.\n\nThese skills serve as a strong basis for the rest of the data science specialization.\n\nMaterial is in depth, but presented clearly. Highly recommended!
由 AK 提供Jul 13, 2016
It's a very introductory course and in a sense I don't feel like I learnt something useful, except the part that shows how to install all the tools that are needed for the rest of the Specialization.
由 GO 提供Jul 25, 2016
Consistent yet very basic course. I would only recommend this course if you are willing to complete the whole Data Science specialization or if you have troubles with the basic functioning of GitHub.
由 WN 提供Aug 19, 2018
A very short (could be completed in a day) course to get you started on the rest of the Specialization. You will learn very basics of installing R and playing around with GitHub. That's it really.
由 AS 提供Aug 26, 2017
A very easy course, can be completed in 3-4 days at max. Having done R programming course first, this was a breeze taking a couple days. Good for people starting off with the specialization.
由 MP 提供Jan 12, 2016
The course is primarily aimed to get you primed and set-up for the other courses in the Data Science Specialization. This is a fairly lightweight course and can be completed pretty quickly.
由 VI 提供Dec 14, 2016
The Data Scientist's Toolbox is a good introduction into Data Science specialization and gives a glimpse of what can be expected from the other modules. Looking forward to other modules.
由 YM 提供Oct 19, 2016
A gentle start to the 10-course data science specialization. Would not recommend taking the course just by itself. If you're planning on the specialization, it's a great way to easy in.
常见问题
退款政策是如何规定的？
我可以只注册一门课程吗？
可以！点击您感兴趣的课程卡开始注册即可。注册并完成课程后，您可以获得可共享的证书，或者您也可以旁听该课程免费查看课程资料。如果您订阅的课程是某专项课程的一部分，系统会自动为您订阅完整的专项课程。访问您的学生面板，跟踪您的进度。
有助学金吗？
我可以免费学习课程吗？
此课程是 100% 在线学习吗？是否需要现场参加课程？
此课程完全在线学习，无需到教室现场上课。您可以通过网络或移动设备随时随地访问课程视频、阅读材料和作业。
完成专项课程需要多长时间？
Time to completion can vary based on your schedule, but most learners are able to complete the Specialization in 3-6 months.
What background knowledge is necessary?
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).
Do I need to take the courses in a specific order?
Begin by taking The Data Scientist's Toolbox and Introduction to R Programming, in order. The other courses may be taken in any order, and in parallel if desired.
完成专项课程后我会获得大学学分吗？
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.
What will I be able to do upon completing the Specialization?
You’ll have a foundational understanding of the field and be prepared to continue studying data science.
Can I sign up for the course without paying or applying for financial aid?
Yes, you can access the course for free via www.coursera.org/jhu. This will allow you to explore the course, watch lectures, and participate in discussions for free. To be eligible to earn a certificate, you must either pay for enrollment or qualify for financial aid.
还有其他问题吗？请访问 学生帮助中心。
