• What is statistical modelling
• Role of statistical models in decision making
• Types of statistical models
• Predictive vs inferential modelling
• Applications across industries
• Statistical modelling workflow
• Role of statistical models in decision making
• Types of statistical models
• Predictive vs inferential modelling
• Applications across industries
• Statistical modelling workflow
• Types of data and variables
• Measurement scales
• Descriptive statistics
• Probability concepts
• Random variables
• Basic statistical assumptions
• Measurement scales
• Descriptive statistics
• Probability concepts
• Random variables
• Basic statistical assumptions
• Data understanding techniques
• Handling missing values
• Outlier detection
• Data transformation
• Feature creation basics
• Preparing data for modelling
• Handling missing values
• Outlier detection
• Data transformation
• Feature creation basics
• Preparing data for modelling
• Discrete distributions
• Continuous distributions
• Normal distribution properties
• Binomial and Poisson distributions
• Distribution selection
• Real-world examples
• Continuous distributions
• Normal distribution properties
• Binomial and Poisson distributions
• Distribution selection
• Real-world examples
• Sampling methods
• Sampling bias
• Point estimation
• Interval estimation
• Confidence intervals
• Estimation accuracy
• Sampling bias
• Point estimation
• Interval estimation
• Confidence intervals
• Estimation accuracy
• Hypothesis formulation
• Null and alternative hypotheses
• Type I and Type II errors
• t-tests and z-tests
• Chi-square tests
• Interpreting test results
• Null and alternative hypotheses
• Type I and Type II errors
• t-tests and z-tests
• Chi-square tests
• Interpreting test results
• Regression concepts
• Simple linear regression model
• Parameter estimation
• Model assumptions
• Interpretation of coefficients
• Model diagnostics
• Multiple regression concepts
• Variable selection methods
• Multicollinearity
• Interaction effects
• Model evaluation metrics
• Regression diagnostics
• Variable selection methods
• Multicollinearity
• Interaction effects
• Model evaluation metrics
• Regression diagnostics
• GLM framework
• Link functions
• Logistic regression
• Poisson regression
• Model interpretation
• Use cases of GLM
• Link functions
• Logistic regression
• Poisson regression
• Model interpretation
• Use cases of GLM
• Overfitting and underfitting
• Training and testing data
• Cross-validation techniques
• Model comparison metrics
• Bias-variance tradeoff
• Model robustness
• Training and testing data
• Cross-validation techniques
• Model comparison metrics
• Bias-variance tradeoff
• Model robustness
• Time series components
• Stationarity concepts
• Autocorrelation
• AR, MA, ARIMA models
• Forecasting techniques
• Time series evaluation
• Stationarity concepts
• Autocorrelation
• AR, MA, ARIMA models
• Forecasting techniques
• Time series evaluation
• Principal Component Analysis (PCA)
• Factor analysis
• Cluster analysis
• Discriminant analysis
• Dimension reduction
• Interpretation of multivariate models
• Factor analysis
• Cluster analysis
• Discriminant analysis
• Dimension reduction
• Interpretation of multivariate models
• Non-linear regression
• Polynomial regression
• Spline models
• Survival analysis basics
• Count data models
• Advanced modelling use cases
• Polynomial regression
• Spline models
• Survival analysis basics
• Count data models
• Advanced modelling use cases
• Residual analysis
• Goodness-of-fit measures
• Information criteria (AIC, BIC)
• Model stability checks
• Sensitivity analysis
• Diagnostic plotting
• Goodness-of-fit measures
• Information criteria (AIC, BIC)
• Model stability checks
• Sensitivity analysis
• Diagnostic plotting
