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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/advanced-r
课程评论：没有评论
课程名称：高级R编程 课程概述：本课程涵盖了R编程中的高级主题，这些主题对于开发强大、稳健和可重用的数据科学工具至关重要。课程内容包括R中的函数编程、稳健的错误处理、面向对象编程、性能分析与基准测试、调试和函数设计的原则。完成本课程后，学员将能够识别和抽象常见的数据分析任务，并将其封装为用户友好的函数。由于每个数据科学环境都面临独特的数据挑战，因此总是需要开发特定于组织使命的定制软件。学员还将能够在R中定义新的数据类型，并开发特定于这些数据类型的功能，以便在数据科学任务的执行中实现更清晰的执行和团队内更强的可重用性。 课程大纲： 1. 欢迎来到高级R编程：本模块简介课程的主题及其重要性。 2. 函数：介绍R中的控制结构和函数在编程中的作用，以及编写优质函数的指导方针。 3. 函数：选择的课程内容（具体内容未列出）。 4. 函数式编程：强调函数式编程在R中的关键作用，并介绍如何处理错误和异常以编写稳健的代码。 5. 函数式编程：选择的课程内容（具体内容未列出）。 6. 调试和性能分析：探讨R中的调试工具，以识别代码中的问题，以及性能分析工具以优化代码效率。 7. 面向对象编程：讲解如何定义自定义数据类型或类，以及处理这些数据类型的函数，介绍R中三种实现面向对象编程的方法。 通过这些学习，学员将能够提升R编程技能，以应对数据科学领域中的各种需求。
Name:Welcome to Advanced R Programming
Description:This course covers advanced topics in R programming that are necessary for developing powerful, robust, and reusable data science tools. Topics covered include functional programming in R, robust error handling, object oriented programming, profiling and benchmarking, debugging, and proper design of functions. Upon completing this course you will be able to identify and abstract common data analysis tasks and to encapsulate them in user-facing functions. Because every data science environment encounters unique data challenges, there is always a need to develop custom software specific to your organization’s mission. You will also be able to define new data types in R and to develop a universe of functionality specific to those data types to enable cleaner execution of data science tasks and stronger reusability within a team.
Name:Functions
Description:This module begins with control structures in R for controlling the logical flow of an R program. We then move on to functions, their role in R programming, and some guidelines for writing good functions.
Name:Functions: Lesson Choices
Description:
Name:Functional Programming
Description:Functional programming is a key aspect of R and is one of R's differentiating factors as a data analysis language. Understanding the concepts of functional programming will help you to become a better data science software developer. In addition, we cover error and exception handling in R for writing robust code.
Name:Functional Programming: Lesson Choices
Description:
Name:Debugging and Profiling
Description:Debugging tools are useful for analyzing your code when it exhibits unexpected behavior. We go through the various debugging tools in R and how they can be used to identify problems in code. Profiling tools allow you to see where your code spends its time and to optimize your code for maximum efficiency.
Name:Object-Oriented Programming
Description:Object oriented programming allows you to define custom data types or classes and a set of functions for handling that data type in a way that you define. R has a three different methods for implementing object oriented programming and we will cover them in this section.
This course covers advanced topics in R programming that are necessary for developing powerful, robust, and reusable data science tools. Topics covered include functional programming in R, robust error handling, object oriented programming, profiling and benchmarking, debugging, and proper design of functions. Upon completing this course you will be able to identify and abstract common data analysis tasks and to encapsulate them in user-facing functions. Because every data science environment encounters unique data challenges, there is always a need to develop custom software specific to your organization’s mission. You will also be able to define new data types in R and to develop a universe of functionality specific to those data types to enable cleaner execution of data science tasks and stronger reusability within a team.
