Data ScienceMachine LearningGithubR ProgrammingExploratory Data AnalysisRstudioData AnalysisDebuggingData ManipulationRegular Expression (REGEX)Data CleansingCluster Analysis
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.
Set up R, R-Studio, Github and other useful tools
您将获得的技能
关于此 专项课程
52,398 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
38%
完成此 专项课程 后开始了新的职业。
15%
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
无需相关领域的预备知识无需相关经验。
完成时间大约为5 个月
建议 8 小时/周
英语（English）
字幕：英语（English）, 法语（French）, 巴西葡萄牙语, 韩语, 俄语（Russian）, 西班牙语（Spanish）, 阿拉伯语（Arabic）, 中文（简体）, 意大利语, 巴西葡萄牙语, 越南语, 德语（German）, 土耳其语（Turkish）, 日语, 中文
学生职业成果
38%
完成此 专项课程 后开始了新的职业。
15%
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
无需相关领域的预备知识无需相关经验。
完成时间大约为5 个月
建议 8 小时/周
英语（English）
字幕：英语（English）, 法语（French）, 巴西葡萄牙语, 韩语, 俄语（Russian）, 西班牙语（Spanish）, 阿拉伯语（Arabic）, 中文（简体）, 意大利语, 巴西葡萄牙语, 越南语, 德语（German）, 土耳其语（Turkish）, 日语, 中文
此专项课程包含 5 门课程
数据科学家的工具箱（中文版）
29,981 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）
19,925 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
7,605 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
5,744 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.
常见问题
完成专项课程后我会获得大学学分吗？
Can I just enroll in a single course?
我可以只注册一门课程吗？
Can I take the course for free?
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？
完成专项课程需要多长时间？
Do I need to take the courses in a specific order?
Will I earn university credit for completing the Specialization?
完成专项课程后我会获得大学学分吗？
Can I sign up for the course without paying or applying for financial aid?
还有其他问题吗？请访问 学生帮助中心。
