• Introduction to SAS and its platform
• Understanding SAS Viya architecture
• Industry use cases and applications
• Roles and responsibilities of SAS professionals
• Data types, variables, and datasets
• Control statements and functions
• Writing reusable and efficient programs
• Best practices in SAS coding
• Data cleaning and transformation
• Handling missing and inconsistent data
• Data integration from multiple sources
• Data quality and validation
• Data profiling and pattern detection
• Identifying anomalies and trends
• Visualization and insights
• Reporting and documentation
• Interactive reporting
• Business insights and storytelling
• Sharing reports with stakeholders
• Visualization best practices
• Hypothesis testing
• Regression and correlation
• ANOVA and statistical modeling
• Time-series basics
• Classification and regression models
• Model evaluation and validation
• Feature engineering and tuning
• Model optimization
• Predictive and prescriptive analytics
• Natural language processing
• Advanced modeling use cases
• Real-world AI applications







