- Github
- Machine Learning
- R Programming
- Regression Analysis
- Data Science
- Rstudio
- Data Analysis
- Debugging
- Data Manipulation
- Regular Expression (REGEX)
- Data Cleansing
- Cluster Analysis
数据科学 专项课程
在数据科学领域工作. A ten-course introduction to data science, developed and taught by leading professors.
468,380 人已注册
提供方
数据科学 专项课程约翰霍普金斯大学您将学到的内容有
Use R to clean, analyze, and visualize data.
Navigate the entire data science pipeline from data acquisition to publication.
Use GitHub to manage data science projects.
Perform regression analysis, least squares and inference using regression models.
您将获得的技能
关于此 专项课程
86,930 次近期查看
Ask the right questions, manipulate data sets, and create visualizations to communicate results.
This Specialization covers the concepts and tools you'll need throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results. In the final Capstone Project, you’ll apply the skills learned by building a data product using real-world data. At completion, students will have a portfolio demonstrating their mastery of the material.
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
You should have beginner level experience in Python. Familiarity with regression is recommended
完成课程大约需要 11 个月
建议进度：7 小时/周
英语（English）
字幕：英语（English）, 阿拉伯语（Arabic）, 法语（French）, （欧洲人讲的）葡萄牙语, 意大利语, 越南语, 韩语, 德语（German）, 俄语（Russian）, 西班牙语（Spanish）, 中文（简体）, 巴西葡萄牙语, 日语
对员工进行热门技能培训能否为您的公司带来益处？
体验 Coursera 企业版可分享的证书
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
You should have beginner level experience in Python. Familiarity with regression is recommended
完成课程大约需要 11 个月
建议进度：7 小时/周
英语（English）
字幕：英语（English）, 阿拉伯语（Arabic）, 法语（French）, （欧洲人讲的）葡萄牙语, 意大利语, 越南语, 韩语, 德语（German）, 俄语（Russian）, 西班牙语（Spanish）, 中文（简体）, 巴西葡萄牙语, 日语
对员工进行热门技能培训能否为您的公司带来益处？
体验 Coursera 企业版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 10 门课程
数据科学家的工具箱（中文版）
33,207 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）
21,730 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
7,950 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
5,989 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
退款政策是如何规定的？
我可以只注册一门课程吗？
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？
完成专项课程需要多长时间？
此专项课程中每门课程的开课频率为多久？
What background knowledge is necessary?
Do I need to take the courses in a specific order?
完成专项课程后我会获得大学学分吗？
What will I be able to do upon completing the Specialization?
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。
