Data Analysis with R Programming
4.7 /5- Offered byCoursera
Data Analysis with R Programming at Coursera Overview
Duration | 37 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Beginner |
Official Website | Explore Free Course  |
Credential | Certificate |
Data Analysis with R Programming at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Course 7 of 8 in the Google Data Analytics
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Beginner Level No prior experience with spreadsheets or data analytics is required. All you need is high-school level math and a curiosity about how things work.
- Approx. 37 hours to complete
- English Subtitles: English
Data Analysis with R Programming at Coursera Course details
- This course is the seventh course in the Google Data Analytics Certificate. These courses will equip you with the skills needed to apply to introductory-level data analyst jobs. In this course, you?ll learn about the programming language known as R. You?ll find out how to use RStudio, the environment that allows you to work with R. This course will also cover the software applications and tools that are unique to R, such as R packages. You?ll discover how R lets you clean, organize, analyze, visualize, and report data in new and more powerful ways. Current Google data analysts will continue to instruct and provide you with hands-on ways to accomplish common data analyst tasks with the best tools and resources.
- Learners who complete this certificate program will be equipped to apply for introductory-level jobs as data analysts. No previous experience is necessary.
- By the end of this course, you will:
- - Examine the benefits of using the R programming language.
- - Discover how to use RStudio to apply R to your analysis.
- - Explore the fundamental concepts associated with programming in R.
- - Explore the contents and components of R packages including the Tidyverse package.
- - Gain an understanding of dataframes and their use in R.
- - Discover the options for generating visualizations in R.
- - Learn about R Markdown for documenting R programming.
Data Analysis with R Programming at Coursera Curriculum
Programming and data analytics
Welcome to the course
Fun with R
Carrie: Getting started with R
Programming languages
Introduction to R
Intro to RStudio
Course syllabus
The R-versus-Python debate
Learning Log: Get ready to explore R
Ways to learn about programming
From spreadsheets to SQL to R
When to use RStudio
Connecting with other analysts in the R community
Glossary: Terms and definitions
Test your knowledge on programming languages
Self-Reflection: Ask a question
Optional Hands-On Activity: Downloading and installing R
Optional Hands-On Activity: R Console
Test your knowledge on R programming languages
Hands-On Activity: Cloud access to RStudio
Optional Hands-On Activity: Get started in RStudio Desktop
Test your knowledge on programming with RStudio
Weekly challenge 1
Programming using RStudio
Programming using RStudio
Programming fundamentals
Connor: Coding tips
Operators and calculations
The gift that keeps on giving
Welcome to the tidyverse
Optional: More on the tidyverse
Working with pipes
Vectors and lists in R
Dates and times in R
Other common data structures
Logical operators and conditional statements
Guide: Keeping your code readable
Available R packages
R resources for more help
Glossary: Terms and definitions
Test your knowledge on programming concepts
Hands-On Activity: R sandbox
Test your knowledge on coding in R
Hands-On Activity: Installing and loading tidyverse
Test your knowledge on R packages
Test your knowledge on the tidyverse
Weekly challenge 2
Working with data in R
Data in R
R data frames
Working with data frames
Cleaning up with the basics
Organize your data
Transforming data
Same data, different outcome
The bias function
More about tibbles
Data import basics
File-naming conventions
R operators
Follow along with the ?Transforming Data? video
Wide to long with tidyr
Working with biased data
Glossary: Terms and definitions
Hands-On Activity: Create your own data frame
Hands-On Activity: Importing and working with data
Test your knowledge on R data frames
Hands-On Activity: Cleaning data in R
Hands-On Activity: Changing your data
Test your knowledge on cleaning data
Self-Reflection: Data cleaning in R
Test your knowledge on R functions
Weekly challenge 3
More about visualizations, aesthetics, and annotations
Visualizations in R
Visualization basics in R and tidyverse
Getting started with ggplot()
Joseph: Career path
Enhancing visualizations in R
Doing more with ggplot
Aesthetics and facets
Annotation layer
Saving your visualizations
Common problems when visualizing in R
Aesthetic attributes
Smoothing
Filtering and plots
Drawing arrows and shapes in R
Saving images without ggsave()
Glossary: Terms and definitions
Hands-On Activity: Visualizing data with ggplot2
Hands-On Activity: Using ggplot
Test your knowledge on data visualizations in R
Hands-On Activity: Aesthetics and visualizations
Hands-On Activity: Filters and plots
Test your knowledge on aesthetics in analysis
Hands-On Activity: Annotating and saving visualizations
Test your knowledge on annotating and saving visualizations
Weekly challenge 4
Documentation and reports
Documentation and reports
Overview of R Markdown
Meg: Programming is empowering
Using R Markdown in RStudio
Structure of markdown documents
Even more document elements
Code chunks
Exporting documentation
R Markdown resources
Optional: Jupyter notebooks
Output formats in R Markdown
Glossary: Terms and definitions
Coming up next...
Hands-On Activity: Your R Markdown notebook
Test your knowledge about documentation and reports
Test your knowledge about creating R Markdown documents
Hands-On Activity: Adding code chunks to R Markdown notebooks
Hands-On Activity: Exporting your R Markdown notebook
Hands-On Activity: Using R Markdown templates
Test your knowledge on code chunks
Weekly challenge 5
Course challenge
Data Analysis with R Programming at Coursera Admission Process
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