Advance Your Skills as an R Expert

Overview

The R language is one of the top two languages you need to learn if you want build the strongest career path possible in data science. (The other is Python.) After mastering the basics of R, take your skills in data science into highly valued areas of specialty with this learning path.

Learn R in the context of the R tidyverse.
Createdata visualizations and presentations.
Developbusiness analytics skills at an advanced level in Excel.

Syllabus

Courses under this program:
Course 1: R Data Science Code Challenges
-Test your knowledge of R programming in this Code Challenges course.

Course 2: Learning the R Tidyverse
-Learn to integrate the tidyverse into your R workflow and get new tools for importing, filtering, visualizing, and modeling research and statistical data.

Course 3: Machine Learning with Data Reduction in Excel, R, and Power BI
-Explore data reduction techniques from machine learning and how to integrate your methods in Excel, R, and Power BI.

Course 4: Creating Interactive Presentations with Shiny and R
-Make the results of big data analysis more compelling and clear. Learn how to create interactive presentations and dashboards with RStudio and Shiny.

Course 5: R: Interactive Visualizations with htmlwidgets
-Learn how to rapidly create rich, interactive data visualizations with R and htmlwidgets—packages that connect R to popular JavaScript libraries like Plotly, Leaflet, and DT.

Course 6: Data Visualization in R with ggplot2
-Discover how to create informative and visually appealing data visualizations using ggplot2, the leading visualization package for R.

Courses

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2-3 hours
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Discover how to create informative and visually appealing data visualizations using ggplot2, the leading visualization package for R.
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5 hours
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Learn how to rapidly create rich, interactive data visualizations with R and htmlwidgets—packages that connect R to popular JavaScript libraries like Plotly, Leaflet, and DT.
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1-2 hours
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Make the results of big data analysis more compelling and clear. Learn how to create interactive presentations and dashboards with RStudio and Shiny.
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3-4 hours
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Learn to integrate the tidyverse into your R workflow and get new tools for importing, filtering, visualizing, and modeling research and statistical data.
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3-4 hours
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Explore data reduction techniques from machine learning and how to integrate your methods in Excel, R, and Power BI.
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Test your knowledge of R programming in this Code Challenges course.