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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/r-programming-environment
课程评论：没有评论
课程名称：R编程环境 概述：本课程为学习R编程语言提供了严格的介绍，特别关注在数据科学环境中使用R进行软件开发。无论您是数据科学团队的一员还是独立在开发者社区中工作，本课程将为您提供在这些环境下做出有效贡献所需的R知识。作为专项课程的第一门课，课程为后续课程提供了R语言的基本基础。我们将覆盖R语言的基本概念和语言基础，关键概念如整洁数据及相关的“tidyverse”工具，复杂和大型数据集的处理与操作，文本数据的处理，以及基本的数据科学任务。完成本课程后，学习者将能够流利使用R控制台，并能够从各种数据源创建整洁的数据集。 课程大纲： 1. 基础R语言：在本模块中，您将学习R的基础知识，包括语法、一些整洁数据原则和过程，以及如何将数据读入R。 2. 数据操作：在这一模块中，您将学习如何在R中总结、过滤、合并和操作数据，包括处理日期和时间的挑战。 3. 文本处理、正则表达式与物理内存：在本模块中，您将学习如何使用R工具和包处理文本和正则表达式，同时还将学习如何管理和充分利用计算机的物理内存。 4. 大型数据集：在最后一个模块中，您将学习如何克服处理大型数据集时的挑战，无论是在内存中还是在外部，并学习如何诊断问题和寻求帮助。 此课程为数据科学学习者奠定了坚实的R编程基础，是深入数据科学领域的关键一步。
Name:Basic R Language
Description: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.
Name:Basic R Language: Lesson Choices
Description:
Name:Data Manipulation
Description: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.
Name:Data Manipulation: Lesson Choices
Description:
Name:Text Processing, Regular Expression, & Physical Memory
Description: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.
Name:Text Processing, Regular Expression, & Physical Memory: Lesson Choices
Description:Choice 1: Get credit while using swirl | Choice 2: Get credit by providing a code from swirl
Name:Large Datasets
Description: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.
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.
