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所在平台: Udemy |
课程主页: https://www.udemy.com/course/programming-statistical-applications-in-r/
课程评论：没有评论
**课程名称：** R语言统计应用编程 **课程概述：** 本课程是一门R语言入门课程，旨在教授使用R语言进行数学和统计应用编程的基础知识。课程广泛采用R语言的CRAN（Comprehensive R Archive Network）上的“Introduction to Scientific Programming and Simulation using R (spuRs)”软件包。 本课程是科学编程基础课程，是Udemy上的“R Programming for Simulation and Monte-Carlo Methods”课程的有用补充和先修课程。这两个课程最初被设计为一个包含两门课程的系列（尽管它们之间共享一些练习）。合起来，这两门课程提供了关于如何使用R软件创建自己的数学和统计函数及应用的强大、独特且有用的教学内容。 “R语言统计应用编程”是一门“实践型”课程，全面教授使用R软件开发统计应用所需的 R 编程基础技能、概念和技术。课程还使用了数十个“真实世界”的科学函数示例。学生无需熟悉R，也无需具备通用编程知识即可成功完成本课程。本课程内容“自成体系”，包含所有材料、幻灯片、练习（含答案）；事实上，课程视频中出现的所有内容都包含在压缩的可下载材料文件中。 本课程是任何有兴趣提升R语言统计编程技能和知识的人的绝佳教学资源。它对于量化分析专业人士以及寻求新工作相关技能或适用于研究数据分析的技能的本科生和研究生都很有用。 **课程内容：** 课程从R控制台和RStudio应用程序的安装及使用基础教学开始，并提供创建和执行R脚本及R函数的必要指导。课程将解释基本的R数据结构，然后讲解数据输入输出以及基本的R编程技术和控制结构。课程将详细介绍创建新的R统计函数以及使用现有的R统计函数。此外，还将详细讲解Bootstrap和Jackknife重采样方法，以及用于估计推断、构建置信区间的方法和技术，以及执行N折交叉验证评估竞争性统计模型。最后，课程将演示调试和提高R程序运行效率的详细说明和示例。 **教学大纲：** 无
Programming Statistical Applications in R is an introductory course teaching the basics of programming mathematical and statistical applications using the R language. The course makes extensive use of the Introduction to Scientific Programming and Simulation using R (spuRs) package from the Comprehensive R Archive Network (CRAN). The course is a scientific-programming foundations course and is a useful complement and precursor to the more simulation-application oriented R Programming for Simulation and Monte-Carlo Methods Udemy course. The two courses were originally developed as a two-course sequence (although they do share some exercises in common). Together, both courses provide a powerful set of unique and useful instruction about how to create your own mathematical and statistical functions and applications using R software.Programming Statistical Applications in R is a "hands-on" course that comprehensively teaches fundamental R programming skills, concepts and techniques useful for developing statistical applications with R software. The course also uses dozens of "real-world" scientific function examples. It is not necessary for a student to be familiar with R, nor is it necessary to be knowledgeable about programming in general, to successfully complete this course. This course is 'self-contained' and includes all materials, slides, exercises (and solutions); in fact, everything that is seen in the course video lessons is included in zipped, downloadable materials files. The course is a great instructional resource for anyone interested in refining their skills and knowledge about statistical programming using the R language. It would be useful for practicing quantitative analysis professionals, and for undergraduate and graduate students seeking new job-related skills and/or skills applicable to the analysis of research data.The course begins with basic instruction about installing and using the R console and the RStudio application and provides necessary instruction for creating and executing R scripts and R functions. Basic R data structures are explained, followed by instruction on data input and output and on basic R programming techniques and control structures. Detailed examples of creating new statistical R functions, and of using existing statistical R functions, are presented. Boostrap and Jackknife resampling methods are explained in detail, as are methods and techniques for estimating inference and for constructing confidence intervals, as well as of performing N-fold cross validation assessments of competing statistical models. Finally, detailed instructions and examples for debugging and for making R programs run more efficiently are demonstrated.
