Data Analytics with R
R is the go-to language for statistical analytics, used in pharma, finance, and academia.
Why Learn R Language for Data Analytics?
R is unmatched for statistical analysis — it has thousands of packages for everything from regression to time series to bioinformatics. If you work in pharma, actuarial, research, or quantitative finance, R is often the required skill. It also produces the most beautiful statistical visualizations.
Who uses R Language
What You'll Learn
Core topics covered when learning R Language for data analytics:
Real-World Use Cases for R Language
What data analysts actually use R Language for on the job:
Real-World Example
In practice
Performing survival analysis on clinical trial data — using Kaplan-Meier curves and Cox regression to understand treatment effectiveness, generating publication-ready charts in ggplot2 for a research paper.
Learning Path for R Language
A structured path to go from zero to job-ready with R Language:
Get familiar with R Language's interface and core concepts. Focus on: R basics & vectors and Data frames & tidyverse. Do not skip the fundamentals — they come up in every interview.
Apply what you have learned to a real dataset. Build a project using R Language that demonstrates Statistical modeling and Data visualization with ggplot2. Upload it to GitHub or share it publicly.
Deepen advanced topics: dplyr for data manipulation and R Markdown reports. Add a second portfolio project. Start applying to junior data analyst roles. R Language proficiency combined with SQL is enough to land your first role.
Prerequisites
What you need before starting to learn R Language for data analytics:
Free Resources to Start Learning R Language
High-quality free resources to begin your R Language analytics journey:
Frequently Asked Questions
How to do data analytics with R Language?+
Start with the basics: R basics & vectors, Data frames & tidyverse, ggplot2 visualization. R is unmatched for statistical analysis — it has thousands of packages for everything from regression to time series to bioinformatics. If you work in pharma, actuarial, research, or quantitative finance, R is often the required skill. It also produces the most beautiful statistical visualizations. With 6–8 weeks of focused practice, you can go from beginner to job-ready.
Is R Language enough for data analytics?+
R Language covers a wide range of analytics use cases including Statistical modeling, Data visualization with ggplot2, Survey analysis. For most analyst roles, pairing R Language with SQL and a BI tool (Power BI or Tableau) gives you a complete skill set. R is unmatched for statistical analysis — it has thousands of packages for everything from regression to time series to bioinformatics. If you work in pharma, actuarial, research, or quantitative finance, R is often the required skill. It also produces the most beautiful statistical visualizations.
How long to learn R Language for data analytics?+
You can learn the fundamentals of R Language in 6–8 weeks with 1–2 hours of daily practice. Difficulty: Intermediate. Prerequisites: Basic statistics knowledge helpful.
Other Data Analytics Tools
Learn R Language for Data Analytics — Structured & Job-Focused
The SkillsetMaster Data Analytics course covers R Language alongside SQL, Power BI, Python, and real-world projects — everything you need to land your first data analyst role.
Over 2,000 students have used this course to transition into data analytics. Includes live mentorship, project feedback, and placement support.
₹4,999 68% OFF