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所在平台: Udemy |
课程主页: https://www.udemy.com/course/learning-path-r-powerful-data-analysis-with-r/
课程评论：没有评论
课程名称：学习路径：R：强大的数据分析 课程概述：随着每天产生的数据量不断增加，市场对能够分析数据并做出决策的专业人士的需求也在上升。R语言是数据分析师广泛使用的热门工具之一。本学习路径提供了完整的数据分析学习过程，将帮助学员掌握数据分析的各个方面。 课程开始时，学员将学习如何从网络下载压缩数据的基本导入技巧，并了解CRAN的工作原理及其重要性。接下来，学员将学习如何创建静态图表，理解如何在Google Maps和OpenStreetMap等互动网页平台上绘制空间数据。同时，课程还将介绍集群分析、时间序列分析、关联挖掘、主成分分析（PCA）、缺失数据处理、情感分析以及使用R和QGIS进行空间数据分析和高级数据可视化（借助R的ggplot2库）。 在学完各个主题后，学员将能够将所学知识应用于分析来自不同行业的实际数据集。通过本学习路径，学员将全面掌握如何在现实世界数据上进行有效的数据分析。 课程讲师包括： - Fabio Veronesi博士，他在Cranfield大学获得数字土壤制图的博士学位，并在ETH Zurich担任博士后，专注于环境研究和空间统计技术的应用。 - Bharatendra Rai博士，是UMass Dartmouth商学院的商业统计与运营管理教授，教授与大数据分析、商业分析及数据挖掘等相关的课程。 参与本课程，您将获得扎实的R语言数据分析技能，为您的职业发展奠定坚实的基础。
There's an increasing number of data being produced every day. This has led to the demand for skilled professionals who can analyze these data and make decisions. R is one of the popular tools which is widely used by data analysts for performing data analysis on real-world data. This Learning Path is the complete learning process to play with data. You will start with the most basic importing techniques for downloading compressed data from the Web. You will get introduced to how CRAN works and will demonstrate why viewers should use them. Next, you will learn to create static plots. Then, you will understand how to plot spatial data on interactive web platforms such as Google Maps and OpenStreetMap. You will learn advanced data analysis concepts such as cluster analysis, time-series analysis, association mining, PCA, handling missing data, sentiment analysis, spatial data analysis with R and QGIS, and advanced data visualization with R's ggplot2 library. Finally, you will implement the various topics learned so far to analyze real-world datasets from various industry sectors. By the end of this Learning Path, you will learn how to perform data analysis on real-world data. For this course, we have combined the best works of these esteemed authors: Fabio Veronesi Fabio Veronesi obtained a Ph.D. in digital soil mapping from Cranfield University and then moved to ETH Zurich, where he has been working for the past three years as a postdoc. In his career, Dr. Veronesi worked at several topics related to environmental research: digital soil mapping, cartography and shaded relief, renewable energy and transmission line siting. During this time Dr. Veronesi specialized in the application of spatial statistical techniques to environmental data. Dr. Bharatendra Rai Dr. Bharatendra Rai is Professor of Business Statistics and Operations Management in the Charlton College of Business at UMass Dartmouth. He teaches courses on topics such as Analyzing Big Data, Business Analytics and Data Mining, Twitter and Text Analytics, Applied Decision Techniques, Operations Management, and Data Science for Business.
