Data Analytics using R
Use R for statistical analysis, data visualization, reporting, and analytics, perfect for analysts in finance, marketing, and research.
Modules
- Setup R & RStudio
- Vectors, Lists, Dataframes
- Tidyverse Introduction
- Descriptive Stats
- T-tests & ANOVA
- Correlation & Regression
- ggplot2 Grammar
- Custom Visuals
- Shiny App Basics
- dplyr: filter, mutate, group_by
- tidyr: pivot & nest
- data.table
- RMarkdown
- Shiny Deployment
- Batch Reporting
- Finance Case Study
- Marketing Analytics
- Customer Segmentation
Industry Insights
80%
Industry Relevance
High
Market Demand
6 LPA+
Avg. Salary
Ready to start learning?
Your Learning Roadmap
Follow this path to mastery. Our AI guide leads the way.
R Basics & Dataframes
10 hrsLearn R syntax, data structures, and tidyverse essentials
Statistical Analysis
12 hrsMaster hypothesis testing, ANOVA, and regression analysis
Data Visualization
10 hrsBuild interactive plots and dashboards with ggplot2 & Shiny
Data Manipulation
10 hrsClean and transform datasets using dplyr, tidyr, and data.table
Reporting & Automation
8 hrsAutomate reporting with RMarkdown and Shiny deployment
Capstone Projects
10 hrsWork on finance, marketing, and customer analytics case studies
Why choose Data Analytics using R?
- R is one of the most powerful statistical and analytical tools available.
- Used in academia, research, finance, and analytics-heavy roles.
- Comprehensive packages like dplyr, ggplot2, Shiny boost productivity.
- Gain practical experience analyzing real datasets and automating reports.