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所在平台: Udemy |
课程主页: https://www.udemy.com/course/exploratory-analysis-in-r-data-analysis/
课程评论：没有评论
课程名称：《理解新数据 - R语言探索性分析》 本课程面向R语言和数据分析新手，旨在帮助学员解决在处理新数据集时遇到的挑战，包括数据整理、工具选择以及初步评估数据价值。 课程内容分为三个主要部分： 1. **统计基础概念回顾：** 尽管不是课程的重点，但会涵盖一些关键的统计概念。 2. **初始数据分析（IDA）：** 学习清理和整理数据，使其适用于后续分析，并检查数据在统计学上的合理性。本部分介绍的工具和方法可以帮助您判断数据是否被妥善收集以及是否值得深入分析。 3. **探索性数据分析（EDA）：** 学习运用各种技术，判断数据是否能够回答您的分析问题，即数据是否包含有价值的信息。这将避免您在没有潜力的项目中浪费时间和精力，并帮助确认项目和数据集的价值。 本课程将帮助您熟练运用R语言工具，弥合数据收集与确认性数据分析（CDA）之间的差距，是您数据分析之旅的良好开端。
Are you new to R and data analysis?Do you ever struggle starting an analysis with a new dataset?Do you have problems getting the data into shape and selecting the right tools to work with?Have you ever wondered if a dataset had the information you were interested in and if it was worth the effort?If some of these questions occurred to you, then this program might be a good start to set you up on your data analysis journey. Actually, these were the question I had in mind when I designed the curriculum of this course. As you can see below, the curriculum is divided into three main sections. Although this course doesn't have a focus on the basic concepts of statistics, some of the most important concepts are covered in the first section of the course.The two other sections have their focus on the initial and the exploratory data analysis phases respectively. Initial data analysis (or IDA for short) is where we clean and shape the data into a form suitable for the planned methods. This is also where we make sure the data makes sense from a statistical point of view. In the IDA section I present tools and methods that will help you figure out if the data was collected properly and if it is worthy of being analyzed.On the other hand, the exploratory data analysis (EDA) section offers techniques to find out if the data can answer your analytical questions, or in other words, if the data has a relevant story to tell. This will spare you from investing time and effort into a project that will not deliver the results you hoped for. In an ideal case the results of EDA may confirm that the planned analysis is worth it and that there are insights to be gained from that dataset and project.If you are interested in statistical methods and R tools that help you bridge the gap between data collection and the confirmatory data analysis (CDA), then this program is for you. Take a look at the curriculum and give this course a try!
