If you are looking for that one course that includes everything about data visualization with R, this is it. Let’s get on this data visualization journey together.
This course is a blend of text, videos, code examples, and assessments, which together makes your learning journey all the more exciting and truly rewarding. It includes sections that form a sequential flow of concepts covering a focused learning path presented in a modular manner. This helps you learn a range of topics at your own speed and also move towards your goal of learning data visualization with R.
The R language is a powerful open source functional programming language. R is becoming the go-to tool for data scientists and analysts. Its growing popularity is due to its open source nature and extensive development community. R is increasingly being used by experienced data science professionals instead of Python and it will remain the top choice for data scientists in 2017. Large companies continue to use R for their data science needs and this course will make you ready for when these opportunities come your way.
This course has been prepared using extensive research and curation skills. Each section adds to the skills learned and helps us to achieve mastery of data visualization. Every section is modular and can be used as a standalone resource. This course covers different visualization techniques in R and assorted R graphs, plots, maps, and reports. It is a practical and interactive way to learn about R graphics, all of which are discussed in an easy-to-grasp manner. This course has been designed to include topics on every possible data visualization requirement from a data scientist and it does so in a step-by-step and practical manner.
We will start by focusing on “ggplot2” and show you how to create advanced figures for data exploration. Then, we will move on to customizing the plots and then cover interactive plots. We will then cover time series plots, heat maps, dendograms. Following that, we will look at maps and how to make them interactive. We will then turn our attention to building an interactive report using the “ggvis” package and publishing reports and plots using Shiny. Finally, we will cover data in higher dimensions which will complete our extensive tour of the data visualization capabilities possible using R.
This course has been authored by some of the best in their fields:
Dr. Fabio Veronesi
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 has specialized in the application of spatial statistical techniques to environmental data.
Atmajitsinh Gohil
Atmajitsinh Gohil works as a senior consultant at a consultancy firm in New York City. He writes about data manipulation, data exploration, visualization, and basic R plotting functions on his blog. He has a master's degree in financial economics from the State University of New York (SUNY), Buffalo.
Yu-Wei, Chiu (David Chiu)
Yu-Wei, Chiu (David Chiu) is the founder of LargitData, a start-up company that mainly focuses on providing Big Data and machine learning products. In addition to being a start-up entrepreneur and data scientist, he specializes in using Spark and Hadoop to process big data and apply data mining techniques for data analysis. Yu-Wei is also a professional lecturer and has delivered lectures on big data and machine learning in R and Python, and given tech talks at a variety of conferences.
