Power BI, AI & Predictive Analytics Training Course

• Descriptive, diagnostic, predictive and prescriptive analytics
• Predictive decision frameworks
• Power BI analytical architecture
• Data readiness for predictive use cases
• Business problem formulation
• AI and analytics governance
• Predictive use-case workshop

Practical session: Predictive use-case design workshop. Select a real business problem suitable for predictive analytics, define the outcome variable, identify available data, establish success criteria and map the decision process the model will support.

• Data quality for predictive analysis
• Feature and variable preparation
• Handling missing and inconsistent observations
• Time-series preparation
• Data profiling and analytical exploration
• Combining internal and external datasets
• Predictive data preparation laboratory

Practical session: Predictive data preparation lab. Profile a messy time-series dataset, handle missing values and inconsistent records, engineer analytical variables and produce a documented modelling-ready dataset.

• Analytical model architecture
• Advanced DAX patterns
• Time intelligence and dynamic measures
• Segmentation and ranking
• Virtual tables and advanced calculations
• Analytical measures for predictive workflows
• DAX challenge

Practical session: Advanced analytical model lab. Build a semantic model supporting predictive analysis, create analytical DAX measures and develop exploratory views that expose trends, segments, drivers and relationships.

• Time-series concepts
• Trend and seasonality analysis
• Rolling forecasts
• Forecast accuracy and validation
• Forecast visualisation in Power BI
• Business interpretation of forecasts
• Forecasting laboratory

Practical session: Forecasting laboratory. Develop a time-series forecasting workflow, compare historical patterns with forecast outputs, test forecast accuracy and build a Power BI view that clearly communicates expected ranges and limitations.

• Regression and classification concepts
• Model selection and validation
• Training and testing concepts
• Feature importance and interpretation
• Model performance measures
• Avoiding overfitting and misleading outputs
• Predictive modelling case

Practical session: Predictive modelling case. Work through a regression or classification use case, prepare training and validation data, interpret model performance and identify variables that materially influence the outcome.

• AI-assisted analytical workflows
• Natural-language analytical experiences
• Automated insight generation
• AI-supported exploration and summarisation
• Human oversight and validation
• Prompting and analytical question design
• AI analytics laboratory

Practical session: AI-assisted analytics lab. Use AI-supported analytical workflows to generate questions, explore patterns and summarise findings, then independently validate outputs and document where human review is required.

• Anomaly detection concepts
• Outlier identification
• Thresholds and dynamic alerts
• Early-warning indicators
• Risk and opportunity signals
• Exception dashboards
• Anomaly detection workshop

Practical session: Anomaly detection workshop. Build an early-warning view from operational or financial data, identify abnormal patterns, establish thresholds and produce an exception dashboard for management follow-up.

• What-if parameters
• Sensitivity analysis
• Scenario trees
• Stress and contingency scenarios
• Decision variables and assumptions
• Comparing strategic alternatives
• Scenario modelling laboratory

Practical session: Scenario simulation lab. Create multiple what-if scenarios, change key assumptions, analyse sensitivity and build a decision model showing the implications of alternative actions.

• Designing forward-looking dashboards
• Combining historical and predicted measures
• Communicating uncertainty
• Executive narratives for predictive findings
• Decision triggers and recommended actions
• Responsible interpretation of AI outputs
• Executive dashboard review

Practical session: Predictive executive dashboard studio. Combine historical performance, forecasts, anomalies and scenario outputs into a forward-looking dashboard and write an executive narrative explaining uncertainty and recommended actions.

• Define a predictive business problem
• Prepare and model the data
• Develop forecasting or predictive analysis
• Build an interactive Power BI solution
• Validate and interpret outputs
• Present management implications and limitations
• Capstone assessment