- Introduction to the exciting world of programming
- Fun with R
- Carrie: Getting started with R
- Programming languages
- Introduction to R
- Intro to RStudio
Beginner
Online
5 Weeks
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.
The highlights
- 100 % complete online course
- Certification course
- Specialised in Google data analytics
- Beginner level
- Approximately 36 hours a week is required
- Programme imparted by Coursera
Program offerings
- Video lectures
- Certification
- Readings
- Tests
- Quizzes
Course and certificate fees
Data Analysis with R Programming Fee Structure
Particulars | Fee Amount in INR |
Data Analysis with R Programming - Audit course | Free |
Data Analysis with R Programming - 1 month | Rs.1,148 /- |
Data Analysis with R Programming - 3 months | Rs.2,296 /- |
Data Analysis with R Programming - 6 months | Rs.3,444/- |
certificate availability
certificate providing authority
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
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.
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.
The syllabus
Week 1: Programming and data analytics
Videos
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
Practice Exercises
- Weekly challenge 1
- 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
Week 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
Practice Exercises
- Weekly challenge 2
- 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
Week 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
Practice Exercises
- 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
- Weekly Challenge 3
Week 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
Practice Exercises
- 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
- Weekly challenge 4
Week 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...
Practice Exercises
- 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
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
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.
The applicants learn how to use applications and frameworks for analysis, such as Excel and spreadsheets, SQL, presentation software, RStudio, Tableau, and Kaggle.
It is a beginner-level course.
The applicant must have a high-school mathematical understanding to apply for the course
Candidates need to submit their assignments at the stipulated time to get the certificate.
Google itself offers the course.
Candidates who cannot afford to get trained in this course offered by Coursera can get financial assistance.
Students can select the platform they want to use: Google sheets or Microsoft excel.
Approximately 36 hours are required to complete the course.
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