The data science revolution has produced reams of new data from a wide variety of new sources. These new datasets are being used to answer new questions in way never before conceived. Visualization remains one of the most powerful ways draw conclusions from data, but the influx of new data types requires the development of new visualization techniques and building blocks. This course provides you with the skills for creating those new visualization building blocks. We focus on the ggplot2 framework and describe how to use and extend the system to suit the specific needs of your organization or team. Upon completing this course, learners will be able to build the tools needed to visualize a wide variety of data types and will have the fundamentals needed to address new data types as they come about.
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The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.
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Meilleurs avis pour BUILDING DATA VISUALIZATION TOOLS
Great course - learned a lot. Excellent instruction One downside.. Peer review can be a blocking issue to moving forward. If no one is around to peer review, you wait, and pay while you wait.
It is a good course. The only downside is that if you are a beginner level R programmer and want to upskill, you will need to do an extensive search to complete this course.
very useful, especially the final practical exam. not 5 score because I think more time should have been spent in more modern interactive charts
It is a very good course, but feels a bit more hands-off than the other 3 preceding courses in the Mastering Software Development in R certificate.
À propos du Spécialisation Mastering Software Development in R
R is a programming language and a free software environment for statistical computing and graphics, widely used by data analysts, data scientists and statisticians. This Specialization covers R software development for building data science tools. As the field of data science evolves, it has become clear that software development skills are essential for producing and scaling useful data science results and products.
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