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.
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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
You should have beginner level experience in Python. Familiarity with regression is recommended
You should have beginner level experience in Python. Familiarity with regression is recommended
此专项课程包含 10 门课程
数据科学家的工具箱(中文版)
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)
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
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
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.
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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?
此专项课程中每门课程的开课频率为多久?
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。