Learn R Programming for data analysis, statistical modeling, visualization, and machine learning to derive insights from complex datasets.
- Introduction to R Programming and R Environment
- Installing R and RStudio
- Variables, Data Types, and Operators
- Working with Vectors, Matrices, Arrays, and Lists
- Data Frames and Data Manipulation
- Control Statements (if, else, loops)
- Functions and Packages in R
- Data Import and Export (CSV, Excel, Database Files)
- Data Cleaning and Data Transformation
- Exploratory Data Analysis (EDA)
- Statistical Analysis and Hypothesis Testing
- Data Visualization using Base R and ggplot2
- Working with dplyr and tidyverse Packages
- Handling Missing Data and Outliers
- Data Aggregation and Reporting
- Regression Analysis and Predictive Modeling
- Introduction to Machine Learning with R
- Time Series Analysis Basics
- Dashboard and Reporting with R Markdown
- Database Connectivity in R
- Real-world Data Analysis Projects
- Best Practices for Writing Efficient R Code
- Case Studies and Industry Use Cases
- Final Capstone Project and Presentation
Talk to our team about your goals, schedule and the right batch for you.
