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所在平台: Udemy |
课程主页: https://www.udemy.com/course/first-steps-in-data-analysis-with-r/
课程评论：没有评论
课程名称：使用R进行数据分析的第一步 课程概述：本课程旨在为已经具备统计概念理论知识的人群提供实践数据分析的学习机会。学习如何分析数据可能是一项艰巨的任务，将从书本上学到的统计知识应用于实际场景中往往充满挑战，而复杂的数据分析软件更是增添了难度。本课程将帮助你建立一个可靠的数据分析流程，打下坚实的基础，以便在未来的职业生涯中进一步提升数据分析技能。 我们将使用R语言，这是一款免费的、最先进的软件环境，适用于建模、数据处理、数据分析和数据可视化。课程将从安装R开始，循序渐进地熟悉R编程语言。你将学习如何在R中加载数据，如何使用高质量的图形进行可视化，以及如何对数据进行分析。课程中将提供我所使用的脚本，以便你能够轻松使用并适应自己的研究目标。 课程内容将包括： - 如何在R中输入数据 - 如何使用plot()函数和ggplot2包进行数据可视化 - 如何拟合、解释和评估通用线性模型，适用于多种研究设计，包括t检验、方差分析（ANOVA）、回归分析、协方差分析（ANCOVA）和多元回归场景 - 如何进行多项式回归 - 用户自定义非线性模型的介绍 - 针对非正态分布数据的广义线性模型入门（案例研究：计数数据） - 优化数据组织和“数据整理”技巧，包括数据的合并、子集化和汇总 通过本课程的学习，你将具备基础的R编程和数据分析能力，为你的未来研究打下良好的基础。
This course is aimed at those that already have a theoretical understanding of statistical concepts and want to learn the practical side of data analysis.Learning how to analyse data can be a daunting test. Applying the statistical knowledge learned from books to real-world scenarios can be challenging, and it's often made harder by seemingly complicated data analysis softwares.This course will help you to develop a reliable data analysis pipeline, creating a solid basis that will make it easy for you to further your data analysis skills throughout your career.We will use R, a free, state-of-the-art software environment for modelling, data handling, data analysis, and data visualisation.We will start from installing R and taking baby steps to become familiar with the R programming language. We will then learn how to load data in R, how to visualise them with publication-level quality graphs, and how to analyse them. I will provide you with the scripts that I use throughout the course, so that you can easily use them and adapt them to your own research objectives.We will learn R one small step at a time, starting from absolute zero:· how to enter data in R· how to visualise data using function plot() and package ggplot2· how to fit, interpret, and evaluate general linear models for a variety of study designs, including t test, ANOVA, regression, ANCOVA, and multiple regression scenarios· how to fit polynomial regression· an introduction to user-defined non-linear models· an introduction to generalised linear models for non-normally distributed data (case study: count data)· optimal data organisation and "data wrangling" - merging, subsetting, and summarising data
