Learning Path: R: Powerful Data Analysis with R هذه الدورة تدرس باللغة الإنجليزية

تفاصيل الدورة

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.