Introduction to R, RStudio, and the R ecosystem

Data types, vectors, matrices, factors, lists, and data frames

Control structures: if-else conditions, loops (for, while), and vectorized operations

Writing custom functions and applying functional programming (lapply, sapply, apply)

Importing and exporting data (CSV, Excel, SQL, JSON, and web scraping basics)

Data manipulation with dplyr: select, filter, mutate, summarize, arrange, and group operations

Reshaping datasets using tidyr: pivot longer, pivot wider, and handling missing data

Working with dates, times (lubridate), and text data processing (stringr)

Data joining techniques: inner, left, right, and full joins on relational datasets

Optimization techniques for handling large datasets in R (data.table)

Base R graphics vs. ggplot2 visualization library

Creating scatter plots, bar charts, box plots, histograms, and heatmaps

Customizing themes, color palettes, labels, legends, and multi-panel plots (facets)

Interactive visualizations using plotly and leaflet (maps)

Automated reporting with R Markdown: generating HTML, PDF, and Word reports

Descriptive statistics, summary statistics, and probability distributions

Hypothesis testing: t-tests, ANOVA, Chi-Square tests, and correlation analysis

Regression analysis: Linear, Multiple, and Logistic regression modeling

Supervised Machine Learning: Decision Trees, Random Forests, and K-Nearest Neighbors (KNN)

Unsupervised Learning: K-Means clustering and Principal Component Analysis (PCA)