关键信息
关于内容
This course provides a rigorous introduction to the R programming language, with a particular focus on using R for software development in a data science setting. Whether you are part of a data science team or working individually within a community of developers, this course will give you the knowledge of R needed to make useful contributions in those settings. As the first course in the Specialization, the course provides the essential foundation of R needed for the following courses. We cover basic R concepts and language fundamentals, key concepts like tidy data and related "tidyverse" tools, processing and manipulation of complex and large datasets, handling textual data, and basic data science tasks. Upon completing this course, learners will have fluency at the R console and will be able to create tidy datasets from a wide range of possible data sources.
课程大纲
- Week 1 - Basic R Language
In this module, you'll learn the basics of R, including syntax, some tidy data principles and processes, and how to read data into R. - Week 2 - Data Manipulation
During this module, you'll learn to summarize, filter, merge, and otherwise manipulate data in R, including working through the challenges of dates and times. - Week 3 - Text Processing, Regular Expression, & Physical Memory
During this module, you'll learn to use R tools and packages to deal with text and regular expressions. You'll also learn how to manage and get the most from your computer's physical memory when working in R. - Week 4 - Large Datasets
In this final module, you'll learn how to overcome the challenges of working with large datasets both in memory and out as well as how to diagnose problems and find help.
教师
Roger D. Peng, PhD
Associate Professor, Biostatistics
Bloomberg School of Public Health
Brooke Anderson
Assistant Professor, Environmental & Radiological Health Sciences
Colorado State University
内容设计师
平台
Coursera是一家数字公司，提供由位于加利福尼亚州山景城的计算机教师Andrew Ng和达芙妮科勒斯坦福大学创建的大型开放式在线课程。
Coursera与顶尖大学和组织合作，在线提供一些课程，并提供许多科目的课程，包括：物理，工程，人文，医学，生物学，社会科学，数学，商业，计算机科学，数字营销，数据科学 和其他科目。
