Data Analysis with R Programming by Google via Coursera: Fee, Duration, How to Apply

Quick Facts

particular	details
Medium of instructions English	Mode of learning Self study	Mode of Delivery Video and Text Based

Course overview

Data Analysis with R Programming is Google Data Analytics Certificate's seventh certification course, which provides applicants the necessary skills they need to get an introductory level data analysts’ job. The applicant would learn the programming language known as R in this course. The candidate can learn how to use RStudio and the environment for working with R throughout the course.

This course also covers R-unique software programmes and techniques, such as R kits. Candidates will learn how R enables them to clean up, coordinate, interpret, view, and report information in fresh and more efficient ways. Current Google data analysis providers will continue to offer practical instructions and services to registered applicants for basic data analyst roles.

Data Analysis with R Programming certification will be awarded after the successful submission of the quizzes. The candidate will adapt the new programming called ‘R’ and implement its use to solve the respective problems and get efficient solutions.

Course and certificate fees

The fees for the course Data Analysis with R Programming is -

Head	Amount in INR
1 month	Rs. 1,175
3 month	Rs. 2,351
6 month	Rs. 3,527

certificate availability

Yes

certificate providing authority

Coursera

Eligibility criteria

Education

The candidate needs to have basic knowledge of high-school-level mathematics to enrol in Data Analysis with R Programming course by Coursera.

Work experience

No work experience needed for Data Analysis with R Programming certification course.

Certification qualifying details

Candidates need to submit the regular quizzes on time, that’s the only criteria to get Data Analysis with R Programming course certification.

What you will learn

Data science knowledge R programming Knowledge of data visualization

Students will master the following skills after completing the Data Analysis with R Programming programme -

Candidates will study the advantages of using the R language for programming
Candidates can find out how to add R to their study with RStudio.
The key topics associated with programming in R are discussed.
The contents and elements of R packages and the Tidyverse package will be examined by the candidate.
Candidates will understand the use of data frames in R.
Candidates can find the options for visualisation generation in R.
For programming R documentation, the candidates will hear about R Markdown.
The applicant shall explain the R programming language and the programming environment in the entire Data Analysis with R Programming certification syllabus.

The syllabus

Module 1: Programming and data analytics

Videos

Introduction to the exciting world of programming
Fun with R
Carrie: Getting started with R
Programming languages
Introduction to R
Intro to RStudio

Readings

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

Quizzes

Optional Hands-On Activity: Downloading and installing R
Optional Hands-On Activity: R Console
Test your knowledge on 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
Module 1 challenge

Plugin

Refresher: Your data analytics certificate roadmap

Module 2: Programming using RStudio

Videos

Programming using RStudio
Programming fundamentals
Operators and calculations
The gift that keeps on giving
Welcome to the tidyverse
More on the tidyverse
Working with pipes
Connor: Coding tips

Readings

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

Quizzes

Module 2 challenge
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

Plugin

Basic Concepts of R

Module 3: Working with data in R

Videos

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

Readings

More about tibbles
Data-import basics
File-naming conventions
More on R operators
Optional: Manually create a data frame
Wide to long with tidyr
Working with biased data
Glossary: Terms and definitions

Quizzes

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
Test your knowledge on cleaning data
Hands-On Activity: Changing your data
Test your knowledge on R functions
Module 3 challenge

Assignment

Hands-on Activity: Create your own data frame

Plugin

Clean, organize, and transform data with R

Module 4: More about visualizations, aesthetics, and annotations

Videos

Visualizations in R
Visualization basics in R and tidyverse
Getting started with ggplot()
Joseph: Career path to people analytics
Enhancing visualizations in R
Doing more with ggplot
Aesthetics and facets
Annotation layer
Saving your visualizations

Readings

Common problems when visualizing in R
Aesthetic attributes
Smoothing
Filtering and plots
Adding annotations in R
Saving images without ggsave()
Glossary: Terms and definitions

Quizzes

Hands-On Activity: Visualizing data with ggplot
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
Module 4 challenge

Plugin

Elements of ggplot

Module 5: Documentation and reports

Videos

Documentation and reports
Overview of R Markdown
Using R Markdown in RStudio
Structure of markdown documents
Meg: Programming is empowering
Even more document elements
Code chunks
Exporting documentation

Readings

R Markdown resources
Optional: Jupyter notebooks
Output formats in R Markdown
Glossary: Terms and definitions
Coming up next...

Quizzes

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
Module 5 challenge

Admission details

The procedure for enrolling on Data Analysis with R Programming online course is -

Step 1: Go to the course webpage.

Step 2: Choose the "enrol for free" option; if candidates are not registered, then make an account with one of the validated social accounts. Then they must sign in with a registered account.

Step 3: Full details about the time and fees of the course will appear on the page, go along with it, and if the applicants are okay and want to confirm it, tap the "start a free trial" tab.

Step 4: The applicant can go to the billing tab to pay the course fees.

Step 5: Registration is complete for the Data Analysis with R Programming programme.

Scholarship Details

Candidates who cannot afford the Data Analysis with R Programming course can apply for the scholarship. The procedure requires at least 15 days to check and verify applications for offering scholarships.

Evaluation process

Candidates need to submit the regular quizzes on time; that’s the only criteria to get Data Analysis with R Programming course certification.

How it helps

The Data Analysis with R Programming course helps candidates learn the R vocabulary. The applicant will learn how to interact with RStudio and its environment. This course also includes R-single applications and technology programmes, including R kits. Candidates can learn how R allows them to effectively smooth out, coordinate, interpret, and report content. After satisfactory submission of the quizzes assigned weekly, the Data Analysis with R Programming certification will be awarded to the candidates.

FAQs

What is the reimbursement policy if candidates want to disenroll from the Data Analysis with R Programming online course?

If candidates subscribe, they will get a 7-day free trial that allows them to cancel without any charge. Coursera does not have a policy for refunds afterwards, but candidates can cancel their membership anytime.

What are the curriculum methods and platforms taught?

The applicants learn how to use applications and frameworks for analysis, such as Excel and spreadsheets, SQL, presentation software, RStudio, Tableau, and Kaggle.