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Data Visualization with R

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Total Duration
12 Hours

Bring your data into focus. Develop the invaluable skills you need to analyze and display data using R—essential when communicating insights and discoveries to non-technical stakeholders. In this track, you'll learn how to create and modify plots using ggplot2, optimizing the aesthetics and geoms to help you build beautiful and accurate visualizations. From here you’ll progress your ggplot2 skills and learn how to calculate statistics and use facets and coordinates. You'll also learn some best …

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Courses in this Learning Path
1
Introduction to Data Visualization with ggplot2
DataCamp Course via DataCamp
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Duration : 4 hours
Price :₹1,093
Level :Beginner
Learn Type :Certification
Introduction to Data Visualization with ggplot2

Data scientists need to be able create meaningful and beautiful data visualizations. This tutorial explains the basics of R data visualization as well as the grammar of graphics plotting concepts within the ggplot2 software. ggplot2 has become the most widely used tool to create professional, flexible plots in R. This course will concentrate on the three main layers of a plot: Geometries, Data, …

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2
Intermediate Data Visualization with ggplot2
DataCamp Course via DataCamp
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Duration : 4 hours
Price :₹1,093
Level :Intermediate
Learn Type :Certification
Intermediate Data Visualization with ggplot2

The ggplot2 course builds on the knowledge from the introductory course, and creates explanation plots. Statistics will automatically be calculated and you'll learn how Coordinates and Facets can help with communication. We will discuss best practices to help you understand data visualization using ggplot2. By the end of this course, you will be able create a custom plotting tool that allows for …

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3
Visualization Best Practices in R
DataCamp Course via DataCamp
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Duration : 4 hours
Price :₹1,093
Level :Intermediate
Learn Type :Certification
Visualization Best Practices in R

This course will help you improve your data visualization skills beyond the basic level and make them an important part of your data science toolkit. Two different datasets will be used to illustrate different types of data (proportions, point-data, single distributions and multiple distributions). We will also discuss the advantages and disadvantages of common visualizations. We'll also be …

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