• Overview of the data science lifecycle
• Introduction to R and its ecosystem
• Installing R and RStudio
• Understanding data science workflows
• Industry applications and use cases
• Introduction to R and its ecosystem
• Installing R and RStudio
• Understanding data science workflows
• Industry applications and use cases
• Data types, variables, and operators
• Control structures and functions
• Loops and conditional logic
• Writing reusable scripts
• Coding best practices
• Control structures and functions
• Loops and conditional logic
• Writing reusable scripts
• Coding best practices
• Introduction to the Tidyverse
• Data cleaning and transformation
• Using dplyr and tidyr
• Handling missing values
• Efficient data wrangling
• Data cleaning and transformation
• Using dplyr and tidyr
• Handling missing values
• Efficient data wrangling
• Data exploration techniques
• Summary statistics and insights
• Identifying trends and patterns
• Detecting anomalies and outliers
• Data storytelling fundamentals
• Summary statistics and insights
• Identifying trends and patterns
• Detecting anomalies and outliers
• Data storytelling fundamentals
• Creating charts and visualizations
• Customizing plots and themes
• Interactive visualizations
• Business insights and reporting
• Visualization best practices
• Customizing plots and themes
• Interactive visualizations
• Business insights and reporting
• Visualization best practices
• Hypothesis testing
• Regression and correlation
• Time-series fundamentals
• Statistical modeling
• Regression and correlation
• Time-series fundamentals
• Statistical modeling
• Supervised and unsupervised learning
• Classification and regression
• Model evaluation and tuning
• Feature engineering
• Model optimization
• Classification and regression
• Model evaluation and tuning
• Feature engineering
• Model optimization
• Ensemble learning techniques
• Clustering and dimensionality reduction
• Time-series forecasting
• Advanced predictive models
• Real-world ML applications
• Clustering and dimensionality reduction
• Time-series forecasting
• Advanced predictive models
• Real-world ML applications
• Introduction to Shiny
• Building interactive dashboards
• UI and server components
• Deploying Shiny apps
• Business use cases
• Building interactive dashboards
• UI and server components
• Deploying Shiny apps
• Business use cases
• Model deployment and automation
• Workflow orchestration
• Version control and collaboration
• Monitoring and performance tracking
• Continuous model improvement
• Workflow orchestration
• Version control and collaboration
• Monitoring and performance tracking
• Continuous model improvement
