📚 R Programming – Course Content

1. Introduction to R & Data Analytics

• What is R and its applications
• R vs other programming languages
• Installing R & RStudio
• Understanding R environment & workspace

2. R Basics & Syntax

• Variables & data types
• Operators & expressions
• Input & output functions
• Basic R commands

3. Data Structures in R

• Vectors
• Lists
• Matrices
• Arrays
• Data frames

4. Control Structures

• Conditional statements (if, else, else if)
• Looping statements (for, while, repeat)
• Apply family functions (apply, lapply, sapply)

5. Functions in R

• Built-in functions
• User-defined functions
• Function arguments & return values
• Anonymous functions

6. Data Import & Export

• Reading CSV, Excel & text files
• Importing data from databases (intro)
• Exporting results & reports

7. Data Manipulation & Cleaning

• Handling missing values
• Data filtering & sorting
• Data transformation
• Using dplyr package

8. Data Visualization

• Base R plots
• Advanced visualization using ggplot2
• Bar charts, line charts, histograms
• Box plots & scatter plots

9. Statistical Analysis Using R

• Descriptive statistics
• Probability distributions
• Hypothesis testing
• Correlation & regression analysis

10. Time Series Analysis (Intro)

• Time series concepts
• Trend & seasonality
• Forecasting basics

11. Introduction to Machine Learning with R

• Supervised vs unsupervised learning
• Linear regression
• Classification basics
• Clustering basics

12. R Packages & Ecosystem

• Installing & managing packages
• Popular packages overview
• CRAN & package documentation

13. Reporting & Dashboards

• Generating reports using R
• Exporting plots & analysis
• Basic R Markdown introduction

14. Mini Projects & Practical Work

• Sales data analysis
• Customer segmentation
• Statistical analysis project
• End-to-end analytics mini project