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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/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 课程概述：R编程语言专为数据分析而设计，是解决数据问题与获得所需答案之间的关键。该课程从一个问题开始，逐步引导您通过数据过程来解决它。您将首先学习准备（或整理）数据以进行分析的重要技术，然后通过探索性数据分析，帮助您总结数据并识别变量之间的相关关系，从而获得洞察力。一旦数据准备就绪，您将学习如何开发模型并评估和调整其性能。通过遵循此过程，您可以确保数据分析符合设定的标准，并对结果充满信心。 您将通过分析航空公司出发与到达数据以预测航班延误，积累实战经验。使用航空报告承运人及时绩效数据集，您将练习读取数据文件，预处理数据，创建模型，改进模型并评估它们，以最终选择最佳模型。 观看视频，完成实验室作业，并为您的作品集增添内容。祝您好运！ 注意：本课程的先决条件是基础的R编程技能，例如，确保您已完成IBM的“数据科学入门与R编程”课程。 课程大纲： 1. 数据分析导论：理解你需要解决的问题以及数据的特性是数据分析的关键，这里R编程语言为进行强有力的数据分析提供了所需工具。 2. 数据整理：数据整理或预处理是准确和完整分析数据的必要第一步，将原始数据转化为易于分类或映射的格式，建立它们之间的可预测关系。 3. 探索性数据分析：探索性数据分析（EDA）通过总结数据的主要特征，帮助您更好地理解数据集，揭示不同变量之间的关系，并提取与所要解决的问题相关的重要变量。 4. 在R中开发模型：在识别解决问题并预处理数据集，进行一些初步探索性数据分析后，接下来是开发模型并评估假设的强度。 5. 模型评估：一旦全面理解数据并进行预处理，您将开发模型，现在需要评估模型在现实世界中的表现和优化效果。 6. 项目：综合应用课程所学知识，进行实际项目练习。
Part: 1
Title:Introduction to Data Analysis with R
Description:All data analysis starts with a problem that you need to solve and understanding your data and the types of questions you can answer about it are key aspects of this. The R programming language provides you with all the tools you need to conduct powerful data analysis, providing the conduit between your data and the real-world problems you want to solve.
Part: 2
Title:Data Wrangling
Description:Data wrangling, or data pre-processing, is an essential first step to achieving accurate and complete analysis of your data. This process transforms your raw data into a format that can be easily categorized or mapped to other data, creating predictable relationships between them, and making it easier to build the models you need to answer questions about your data.
Part: 3
Title:Exploratory Data Analysis
Description:Exploratory data analysis, or EDA, is an approach to analyzing data that summarizes its main characteristics and helps you gain a better understanding of the dataset, uncover relationships between different variables, and extract important variables for the problem you are trying to solve.
Part: 4
Title: Model Development in R
Description:You have identified the problem that you’re trying to solve and have pre-processed the dataset you’ll use in your analysis, and you have conducted some exploratory data analysis to answer some of your initial questions. Now, it’s time to develop your model and assess the strength of your assumptions.
Part: 5
Title:Model Evaluation
Description:You have a firm understanding of your data and have pre-processed it to ensure the best possible outcomes. And you have conducted exploratory data analysis and developed your model. Everything looks good so far, but how can you be certain your model works in the real world and performs optimally?
Part: 6
Title:Project
Description:
The R programming language is purpose-built for data analysis. R is the key that opens the door between the problems that you want to solve with data and the answers you need to meet your objectives. This course starts with a question and then walks you through the process of answering it through data. You will first learn important techniques for preparing (or wrangling) your data for analysis. You will then learn how to gain a better understanding of your data through exploratory data analysis, helping you to summarize your data and identify relevant relationships between variables that can lead to insights. Once your data is ready to analyze, you will learn how to develop your model and evaluate and tune its performance. By following this process, you can be sure that your data analysis performs to the standards that you have set, and you can have confidence in the results. You will build hands-on experience by playing the role of a data analyst who is analyzing airline departure and arrival data to predict flight delays. Using an Airline Reporting Carrier On-Time Performance Dataset, you will practice reading data files, preprocessing data, creating models, improving models, and evaluating them to ultimately choose the best model. Watch the videos, work through the labs, and add to your portfolio. Good luck! Note: The pre-requisite for this course is basic R programming skills. For example, ensure that you have completed a course like Introduction to R Programming for Data Science from IBM.
