Predictive Analytics Certification

Module 1: Introduction to Predictive Analytics

• Predictive analytics fundamentals
• Descriptive vs predictive analytics
• Predictive modelling
• Business applications
• Analytics lifecycle
• Prediction workflows

Module 2: Predictive Analytics with Python

• Python analytics ecosystem
• NumPy
• Pandas
• Scikit-learn
• Development environments
• Predictive workflows

Module 3: Data Collection & Understanding

• Data sources
• Structured datasets
• Target variables
• Predictor variables
• Data profiling
• Dataset assessment

Module 4: Data Cleaning & Preparation

• Missing values
• Duplicate records
• Incorrect values
• Outlier treatment
• Data transformation
• Data validation

Module 5: Exploratory Data Analysis

• Descriptive statistics
• Data distributions
• Correlation
• Pattern identification
• Relationship analysis
• Data visualization

Module 6: Statistics for Predictive Analytics

• Probability fundamentals
• Statistical distributions
• Sampling
• Variance
• Correlation
• Statistical relationships

Module 7: Feature Engineering

• Feature creation
• Feature transformation
• Categorical encoding
• Scaling
• Normalization
• Feature interactions

Module 8: Feature Selection

• Feature relevance
• Correlation-based selection
• Filter methods
• Wrapper methods
• Embedded methods
• Dimensionality considerations

Module 9: Regression Analysis

• Regression concepts
• Linear regression
• Multiple regression
• Regression assumptions
• Predictions
• Business applications

Module 10: Advanced Regression Techniques

• Polynomial regression
• Ridge regression
• Lasso regression
• Regularization
• Regression comparison
• Model selection

Module 11: Classification Fundamentals

• Classification concepts
• Logistic regression
• Binary classification
• Multiclass classification
• Probability estimates
• Decision thresholds

Module 12: Decision Trees

• Tree structure
• Splitting criteria
• Classification trees
• Regression trees
• Tree depth
• Feature importance

Module 13: Ensemble Learning

• Ensemble concepts
• Random Forest
• Bagging
• Boosting
• Gradient boosting
• Model comparison

Module 14: Advanced Predictive Algorithms

• K-Nearest Neighbors
• Support Vector Machines
• Naive Bayes
• XGBoost concepts
• Algorithm selection
• Predictive applications

Module 15: Model Evaluation

• Training and testing
• Cross-validation
• Accuracy
• Precision and recall
• F1 score
• ROC-AUC

Module 16: Regression Model Evaluation

• Mean Absolute Error
• Mean Squared Error
• Root Mean Squared Error
• R-squared
• Residual analysis
• Model comparison

Module 17: Hyperparameter Tuning

• Model parameters
• Hyperparameters
• Grid search
• Random search
• Cross-validation
• Performance optimization

Module 18: Time-Series Forecasting

• Time-series data
• Trend
• Seasonality
• Moving averages
• Forecasting methods
• Forecast evaluation

Module 19: Model Explainability

• Model interpretation
• Feature importance
• Local explanations
• Global explanations
• SHAP concepts
• Communicating predictions

Module 20: AI-Assisted Predictive Analytics

• AI-assisted data exploration
• Feature suggestions
• Automated modelling concepts
• Predictive insights
• Model interpretation assistance
• Responsible AI usage

Module 21: Deployment & Monitoring

• Model deployment concepts
• Prediction pipelines
• Batch predictions
• Real-time predictions
• Model monitoring
• Data and model drift

Module 22: Practical Predictive Analytics Project

• Business problem definition
• Data preparation
• Feature engineering
• Model development
• Model evaluation
• Prediction presentation