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所在平台: Udemy |
课程主页: https://www.udemy.com/course/nonprofit-data-analysis-using-r/
课程评论：没有评论
课程名称：R数据分析：终极初学者指南 概述：这是为没有编码经验的初学者设计的R课程，基于最新的在线学习理论研究和我个人在许多在线课程中的经验。我创建这门课程是希望能有一门我在学习R时所需的课程。我们将一起编写代码，专注于那些占工作80%的20%的代码。在每个章节结束时，您将面临一个“坚持练习”挑战，以不同的数据集应用您刚刚学到的知识（基于《Make It Stick》一书的原则）。 这门课程与其他初学者R课程有几个显著的不同之处： - 项目驱动的学习与真实场景：所有课程都基于数据从业者常见的问题。 - 内容聚焦：课程大纲和讲座基于数据从业者的日常工作流程，而不是从底层向上学习R编程。 - 当前（并持续更新）的代码：我每天使用R，确保您学习到最有效的方式来完成最常见的重要任务。 - 保持真实：在视频中，我不会剪掉错误，让您从中学习。R是我的第一门编程语言，我曾因错误过多、学习时间过长及网上课程缺少重要步骤而放弃两次。我努力在操作时解释我们所做的事情，并给您机会用不同但相关的数据集自己尝试。 在本课程中，您将学习到： - 从不同来源（文件、数据库）加载数据 - 使用tidyverse包构建适合分析的数据结构 - 快速探索和可视化数据趋势 - 进行特征工程以进行更深入的分析 - 分析调查数据 - 为您的数据选择合适的可视化方式 - 创建专业可视化图表 - 使用RMarkdown创建和自动化报告
This is the R course for beginners with no coding experience. It is based on the latest research in online learning theory and my personal experience with dozens of online courses. I created this course as the course I wish I would have had when I first started learning R. We will code together and focus on the 20% of code responsible for 80% of the work. At the end of sections, you will have a 'Make It Stick' challenge to apply what you have just learned with a different dataset (based on principles in the book 'Make It Stick'). This course is different from other beginner courses in R in a couple significant ways:Project-based learning with real-world scenarios: All lessons are based on common questions facing data practioners. Content focus: The course outline and lectures are based on everyday workflows of data practioners rather than a bottom-up approach to R programming. Practically, this means we won't spend much time learning about R and core principles of programming; we will immediately start with how you will use it. Current (& continually updated) code: I work in R everyday and make sure you are learning the best and most efficient ways to accomplish the most common and important tasks. For example, the rowwise function in the dplyr package enables you to perform calculations across columns by rows. A single line of code can now accomplish what was previously far more challenging. Keeping it real: I keep the video rolling when I make an error. You can learn a lot from mistakes. R was my first programming language and I quit twice because of too many errors, too much time to learn it, and frustration with online courses that left out important steps or assumed knowledge that simply wasn't there. I try really hard to explain what we're doing while we're doing it and then giving you an opportunity to do it on your own with a different (but related) dataset. In this course, you will learn to:Load data from different sources (files, databases)Structure data for analysis using the tidyverse packagesQuickly explore and visualize data trendsConduct feature engineering for deeper analysisAnalyze survey dataSelect the right visualization for your dataCreate professional visualizationsCreate and automate reports using RMarkdown
