• What is predictive modelling
• Predictive vs descriptive analytics
• Business use cases of predictive models
• Predictive modelling lifecycle
• Role of SAS in predictive analytics
• Overview of modelling techniques
• Predictive vs descriptive analytics
• Business use cases of predictive models
• Predictive modelling lifecycle
• Role of SAS in predictive analytics
• Overview of modelling techniques
• Review of basic statistics
• Probability distributions
• Descriptive vs inferential statistics
• Correlation and covariance
• Hypothesis testing basics
• Statistical assumptions in modelling
• Probability distributions
• Descriptive vs inferential statistics
• Correlation and covariance
• Hypothesis testing basics
• Statistical assumptions in modelling
• Data understanding and exploration
• Handling missing values
• Outlier detection and treatment
• Data transformation techniques
• Variable creation and feature engineering
• Data partitioning (train, validate, test)
• Handling missing values
• Outlier detection and treatment
• Data transformation techniques
• Variable creation and feature engineering
• Data partitioning (train, validate, test)
• EDA concepts
• Summary statistics
• Frequency analysis
• Data visualization basics
• Identifying data patterns
• Preparing data insights
• Summary statistics
• Frequency analysis
• Data visualization basics
• Identifying data patterns
• Preparing data insights
• Simple linear regression
• Multiple linear regression
• Model assumptions
• Interpretation of regression output
• Variable selection techniques
• Model diagnostics
• Multiple linear regression
• Model assumptions
• Interpretation of regression output
• Variable selection techniques
• Model diagnostics
• Logistic regression concepts
• Binary and categorical targets
• Odds ratios interpretation
• Model fitting using SAS
• Goodness-of-fit measures
• Model validation techniques
• Binary and categorical targets
• Odds ratios interpretation
• Model fitting using SAS
• Goodness-of-fit measures
• Model validation techniques
• Overfitting and underfitting
• Train and test data concepts
• Cross-validation basics
• Model comparison techniques
• Accuracy and error metrics
• Selecting best model
• Train and test data concepts
• Cross-validation basics
• Model comparison techniques
• Accuracy and error metrics
• Selecting best model
• Decision tree fundamentals
• Splitting criteria
• Tree pruning techniques
• Rule-based modelling
• Interpretation of tree models
• Business applications
• Ensemble modelling overview
• Bagging concepts
• Boosting concepts
• Random forest basics
• Combining multiple models
• Performance improvement strategies
• Bagging concepts
• Boosting concepts
• Random forest basics
• Combining multiple models
• Performance improvement strategies
• Clustering concepts
• K-means clustering
• Hierarchical clustering
• Cluster validation
• Customer segmentation use cases
• Supporting predictive decisions
• K-means clustering
• Hierarchical clustering
• Cluster validation
• Customer segmentation use cases
• Supporting predictive decisions
• Time series data concepts
• Trend and seasonality
• Moving averages
• Exponential smoothing
• Forecast accuracy measures
• Business forecasting examples
• Trend and seasonality
• Moving averages
• Exponential smoothing
• Forecast accuracy measures
• Business forecasting examples
• Confusion matrix
• Sensitivity and specificity
• ROC curve and AUC
• Lift and gain charts
• KS statistic
• Model stability checks
• Sensitivity and specificity
• ROC curve and AUC
• Lift and gain charts
• KS statistic
• Model stability checks
