Master R for Data Science
Offered By: LinkedIn Learning
Course Description
Overview
Learn the most popular data-science-specific language: R! This learning path provides a strong foundation of skills and knowledge on which to build your coding resume.
- Learn how R works, from the foundational concepts on up.
- Practice using R with two of the most common tools in data science: Excel and Tableau.
- Explore the applied use of R in social network analysis.
Syllabus
Courses under this program:
Course 1: Learning R
-Learn the basics of R, the free, open-source language for data science. Discover how to use R and RStudio for beginner-level data modeling, visualization, and statistical analysis.
Course 2: Code Clinic: R
-Practice coding with R. Explore common R programming challenges, and then compare the results with other programming languages in the Code Clinic series.
Course 3: Data Wrangling in R
-Learn about the principles of tidy data and discover how to import, transform, clean, and wrangle data using the R programming language.
Course 4: R Essential Training: Wrangling and Visualizing Data
-Learn how to wrangle data and create meaningful visualizations with R, the programming language powering modern data science.
Course 5: Social Network Analysis Using R
-Examine the relationships and trends among social networks in new and exciting ways. Learn how to perform social network analysis with R.
Course 6: R Programming in Data Science: Setup and Start
-Learn how to choose and install a version of R-base R, tidyverse R, R Open, or Bioconductor R-and install useful R packages.
Course 7: Integrating Tableau and R for Data Science
-Discover how to combine Tableau and R to provide your business with the ability to see and understand your data. Learn how to integrate these platforms and when to use either one.
Course 8: R for Excel Users
-Update your data science skills by learning R. Learn how data analysis and statistics operations are run in Excel versus R and how to move data back and forth between each program.
Course 9: Machine Learning with Logistic Regression in Excel, R, and Power BI
-Learn how to perform logistic regression using R and Excel and use Power BI to integrate these methods into a scalable, sharable model.
Course 1: Learning R
-Learn the basics of R, the free, open-source language for data science. Discover how to use R and RStudio for beginner-level data modeling, visualization, and statistical analysis.
Course 2: Code Clinic: R
-Practice coding with R. Explore common R programming challenges, and then compare the results with other programming languages in the Code Clinic series.
Course 3: Data Wrangling in R
-Learn about the principles of tidy data and discover how to import, transform, clean, and wrangle data using the R programming language.
Course 4: R Essential Training: Wrangling and Visualizing Data
-Learn how to wrangle data and create meaningful visualizations with R, the programming language powering modern data science.
Course 5: Social Network Analysis Using R
-Examine the relationships and trends among social networks in new and exciting ways. Learn how to perform social network analysis with R.
Course 6: R Programming in Data Science: Setup and Start
-Learn how to choose and install a version of R-base R, tidyverse R, R Open, or Bioconductor R-and install useful R packages.
Course 7: Integrating Tableau and R for Data Science
-Discover how to combine Tableau and R to provide your business with the ability to see and understand your data. Learn how to integrate these platforms and when to use either one.
Course 8: R for Excel Users
-Update your data science skills by learning R. Learn how data analysis and statistics operations are run in Excel versus R and how to move data back and forth between each program.
Course 9: Machine Learning with Logistic Regression in Excel, R, and Power BI
-Learn how to perform logistic regression using R and Excel and use Power BI to integrate these methods into a scalable, sharable model.
Courses
-
Learn how to choose and install a version of R-base R, tidyverse R, R Open, or Bioconductor R-and install useful R packages.
R is powerful, but not intuitive. There is a strong and diverse R ecosystem, and data scientists are expected to mix and match from the different versions and packages. Before you can even begin programming, you have to choose, install, and set up R to work for you.
In this course, Mark Niemann-Ross provides a direct and efficient introduction to the many flavors of the R programming language, including base R, tidyverse R, R Open from Microsoft, and Bioconductor R. He also provides a peek at programming with R interactively and via the command line, and introduces some helpful packages for working with SQL, 3D graphics, data, and clusters in R. At the end of this short course, you will have installed a version of R along with a few core libraries and an optimized IDE.
-
Discover how to combine Tableau and R to provide your business with the ability to see and understand your data. Learn how to integrate these platforms and when to use either one.
R is known as one of the most robust statistical computing solutions out there. Tableauâa leading business intelligence platformâprovides excellent data visualization and exploration capabilities. When combined, Tableau and R offer one of the most powerful and complete data analytics solutions in the industry today, providing businesses with unparalleled abilities to see and understand their data. In this course, learn how to integrate these two platforms, as well as how to determine when each one is a better choice. Instructor Ben Sullins explains how to connect Tableau to R, and covers geocoding, running linear regression models, clustering, and more.
-
Update your data science skills by learning R. Learn how data analysis and statistics operations are run in Excel versus R and how to move data back and forth between each program.
-
Examine the relationships and trends among social networks in new and exciting ways. Learn how to perform social network analysis with R.
-
Practice coding with R. Explore common R programming challenges, and then compare the results with other programming languages in the Code Clinic series.
-
Learn about the principles of tidy data and discover how to import, transform, clean, and wrangle data using the R programming language.
-
Learn the basics of R, the free, open-source language for data science. Discover how to use R and RStudio for beginner-level data modeling, visualization, and statistical analysis.
-
Learn how to perform logistic regression using R and Excel and use Power BI to integrate these methods into a scalable, sharable model.
-
Learn how to wrangle data and create meaningful visualizations with R, the programming language powering modern data science.
Taught by
Barton Poulson, Mark Niemann-Ross, Mike Chapple, Curtis Frye, Ben Sullins, Conrad Carlberg and Helen Wall
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