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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/introducton-r-programming-data-science
课程评论：没有评论
课程名称：数据科学基础中的R语言简介 概述：在数据科学领域，您必然会接触到R语言及其在数据分析中的作用。本课程介绍R语言的基本概念，如数据类型、操作技巧，以及如何实现基本的编程任务。您将开始了解常见的数据结构、编程基础以及如何运用R语言对数据进行操作。 本课程强调实践学习。您将使用RStudio编写简单程序，在数据框或矩阵中操作数据，并利用Watson Studio和Jupyter笔记本作为数据分析师完成最终项目，以获取和分析数据驱动的洞察。 无须具备R语言或编程的基础知识。 课程大纲： 1. R基础：介绍R语言及其常见数据类型，以及如何操作这些数据类型。了解R解释器的角色，掌握RStudio和Jupyter Notebook这两个现代集成开发环境的使用。 2. 常见数据结构：学习R语言支持的各种数据结构，包括向量、因子、列表、数组、矩阵和数据框。了解这些数据结构的用途及其如何存储数据，为选择合适的数据结构打下基础。 3. R编程基础：掌握控制程序执行流程的编程特性，定义特定任务的函数，以及处理常见数据类型（如字符串和日期）。学习如何在R中实现这些基本的编程任务。 4. 数据处理：了解如何从不同来源和格式（如文本、Excel、.JSON、.XML文件）读取数据到R工作环境中，并使用这些数据进行分析。 5. 最终项目：综合应用所学知识，完成数据分析的实践项目。 通过此课程，您将提升对R语言的理解，并为将来在数据科学领域的进一步学习与应用奠定基础。
Name:R Basics
Description:Regardless of the programming language you use, all share some commonalities. For example, you’ll likely need to perform basic operations on different data types, like applying mathematical equations to numeric data. You’ll also need an environment in which to write your code, anbbd most modern integrated development environments (or IDEs) provide features that make writing code easier, like syntax checking, color coding, and integrated help. This module introduces you to the R language, its common data types, and techniques for manipulating them. You’ll also learn about the role of the R interpreter and how it transforms code into executable objects. Finally, you’ll be introduced to two of the most common IDEs for R development: RStudio and Jupyter Notebook.
Name:Common Data Structures
Description:The R language supports many types of data structures that you can use to organize and store values in your code, including vectors, factors, lists, arrays, matrices, and data frames. Each data structure type serves a specific purpose and can contain specific kinds of data. So, it’s important to understand the differences between them so you can make the right choice based on your scenario. In this module, you’ll learn about the types of data you can store in each data structure and how to add, remove, or manipulate its contents.
Name:R Programming Fundamentals
Description:As with most programming languages, R supports coding features that you can use to control the flow of program execution, define functions that can perform specific tasks, work with common data types, like strings and dates, and make your code more robust by intercepting likely errors and handling them before they interrupt the execution of your code. In this module, you’ll learn how to implement these fundamental programming tasks in R.
Name:Working with Data
Description:Data is everywhere! The data you need to analyze may come from a traditional database, but it may also come from a variety of different sources and systems, and it may come to you in one or more formats. For example, your data might be in text, Excel, .JSON, or .XML files. Or it may not be stored in a file at all, but instead lives on the pages of a website. How will you take all these different file formats and load them into your R working environment? This module provides you with the tools you need to read data from some common file formats and sources into data objects that you can then use and combine with other data objects in your data analysis.
Name:Final Project
Description:
When working in the data science field you will definitely become acquainted with the R language and the role it plays in data analysis. This course introduces you to the basics of the R language such as data types, techniques for manipulation, and how to implement fundamental programming tasks. You will begin the process of understanding common data structures, programming fundamentals and how to manipulate data all with the help of the R programming language. The emphasis in this course is hands-on and practical learning . You will write a simple program using RStudio, manipulate data in a data frame or matrix, and complete a final project as a data analyst using Watson Studio and Jupyter notebooks to acquire and analyze data-driven insights. No prior knowledge of R, or programming is required.
