• Types of data and measurement scales
• Descriptive vs inferential statistics
• Industry use cases and applications
• Roles and responsibilities in analytics
• Experimental and observational studies
• Survey design
• Data quality and validation
• Ethical considerations
• Variability and dispersion
• Data visualization
• Distribution analysis
• Summary reporting
• Random variables
• Normal and binomial distributions
• Law of large numbers
• Real-world applications
• Type I and Type II errors
• p-values and confidence intervals
• One-sample and two-sample tests
• Interpretation and reporting
• Linear and multiple regression
• Model assumptions
• Diagnostics and validation
• Predictive modeling
• Factorial designs
• Randomized experiments
• Design optimization
• Interpretation







