Master AI-Driven Power BI Analytics to Future-Proof Your Career
You’re not behind. You’re not alone. But you can’t ignore it any longer-the analytics landscape is shifting at an unprecedented pace, and traditional Power BI skills are no longer enough. AI is redefining what’s possible, and the professionals who adapt now are securing high-impact roles, promotions, and recognition at record speed. Staying stagnant means risk. Risk of being passed over. Risk of automation replacing routine reporting work. Risk of falling behind peers who’ve already unlocked predictive insights, intelligent dashboards, and automated decision-making-using AI-enhanced Power BI. Master AI-Driven Power BI Analytics to Future-Proof Your Career isn’t just another data course. It’s your strategic transformation from static report builder to future-ready analytics leader. This structured program takes you from idea to implementation in under 6 weeks, equipping you with a portfolio-ready AI augmented Power BI project, complete with natural language queries, automated forecasting, and board-level visual storytelling. Jamie R., Senior Financial Analyst at a Fortune 500 firm, used this exact path to deliver an AI-powered sales prediction model that reduced forecast error by 41%. Her board presentation was fast-tracked into a company-wide rollout, and she received a promotion within 8 weeks of completing the course. This isn’t about learning in isolation. It’s about proving value fast, gaining visibility, and positioning yourself at the forefront of intelligent analytics. No vague theory. No outdated templates. Just high-leverage, real-world execution. Here’s how this course is structured to help you get there.Course Format & Delivery Details Learn on Your Terms – Anytime, Anywhere, Forever
This course is 100% self-paced, with full online access from day one. No rigid schedules, no mandatory sessions. Start today, progress at your speed, and repeat any section as often as you need. Most learners complete the core curriculum in 4 to 6 weeks with 60–90 minutes of focused effort per day. Immediate access means you can begin transforming your skills tonight. And because this is an on-demand program, you can revisit modules whenever new organizational challenges arise-whether it’s six months from now or five years later. Unlimited Access: Lifetime Learning with Zero Extra Cost
You receive lifetime access to all course content. This includes every future update, enhancement, and emerging technique in AI-driven Power BI analytics-at no additional fee. As Microsoft integrates new AI capabilities into Power BI, your access evolves with them. Stay perpetually relevant. No need to repurchase, re-enroll, or chase certifications every year. Your investment compounds over time. Work Where You Work – Mobile-Friendly, 24/7, Globally Accessible
Whether you’re on a laptop in the office, a tablet during your commute, or a smartphone reviewing concepts between meetings, the course platform adapts seamlessly. No downloads. No software conflicts. Just consistent, responsive access across all your devices, anywhere in the world. Instructor Support That Builds Confidence – Not Dependency
You’re not learning in isolation. Receive guided support through structured feedback pathways, scenario-based guidance, and direct access to industry-experienced instructors. This isn’t about hand-holding-it’s about accountability, clarification, and real-time problem solving when it matters most. Ask specific questions. Get actionable advice. Move forward with clarity. Stand Out with a Globally Recognized Certificate of Completion
Upon finishing the program, you will earn a verifiable Certificate of Completion issued by The Art of Service. This credential is trusted by professionals in over 120 countries and recognized by hiring managers across finance, healthcare, technology, and enterprise consulting sectors. Display it proudly on LinkedIn, in job applications, or during performance reviews. It signals not just completion-but mastery of high-demand, future-focused analytics. Simple, Transparent Pricing – No Hidden Fees, No Surprises
The listed course fee includes everything. No add-ons. No upgrade traps. No recurring charges. What you see is what you get, with full access from enrollment day forward. Secure payment processing accepts Visa, Mastercard, and PayPal-ensuring fast, trusted transactions across global regions. Zero-Risk Enrollment: Satisfied or Refunded
If you complete the first two modules and don’t feel confident that this course will transform your analytics capabilities, you’re covered by our full money-back guarantee. No questions asked. No friction. No risk. We remove the barrier between doubt and action because we know the results speak for themselves. Instant Confirmation – Seamless Onboarding
After enrollment, you’ll receive a confirmation email with instructions. Access details to the course platform will be delivered separately once your learning environment is fully activated. This ensures a stable, personalized setup for every participant. This Works For You-Even If…
- You’ve never used AI tools in your analytics workflow before
- You’re overwhelmed by technical jargon or complex documentation
- You’re unsure whether your current role values advanced analytics
- You’ve taken other courses but couldn’t apply the learning in real projects
- You don’t have a data science background
This program is designed for real professionals in real jobs. It’s not theoretical. It’s structured around immediate applicability, incremental confidence building, and visible results from day one. Finance analysts, operations leads, supply chain specialists, and BI developers-all have mastered this content and leveraged it to expand their influence. Trust isn’t earned through hype. It’s earned through consistency, clarity, and delivering on promises. That’s why every design choice, learning sequence, and support mechanism in this course exists to reduce friction and maximise your return on time and investment.
Extensive and Detailed Course Curriculum
Module 1: Foundations of AI-Driven Analytics in Power BI - Understanding the AI revolution in business intelligence
- Core differences between traditional and AI-enhanced Power BI workflows
- Identifying high-impact use cases across industries
- Setting realistic, measurable goals for AI integration
- Overview of Power BI’s built-in AI capabilities and limitations
- Mapping AI features to common business problems
- Preparing your organizational data culture for AI adoption
- Aligning AI analytics with strategic business objectives
- Creating your personal AI analytics roadmap
- Establishing success metrics for your first AI project
Module 2: Data Preparation for AI Workflows - Structuring data to maximize AI model performance
- Automated data cleansing using Power Query
- Handling missing data with intelligent imputation techniques
- Standardising and normalising datasets for consistency
- Creating time-based features for forecasting models
- Optimising data schemas for AI readiness
- Validating data quality before AI processing
- Using Power BI dataflows to streamline preparation
- Automating repetitive data prep steps
- Documenting data lineage for governance and compliance
Module 3: Natural Language Processing in Power BI - Introduction to Q&A visual and how it leverages NLP
- Configuring synonyms and phrasing rules for accuracy
- Training the Q&A engine with domain-specific language
- Creating intuitive question prompts for stakeholders
- Analysing usage patterns to refine NLP performance
- Building voice-enabled dashboards with Power BI and Azure AI
- Integrating custom language models for enterprise jargon
- Deploying multilingual Q&A experiences
- Monitoring NLP accuracy over time
- Designing user onboarding for natural language dashboards
Module 4: AI-Powered Data Modelling - Leveraging AI for automatic relationship detection
- Using Auto Date/Time with intelligent granularity
- Implementing cognitive services in data models
- Enhancing model semantics with AI tagging
- Optimising model performance with AI diagnostics
- Automated schema suggestions based on data patterns
- AI-driven field grouping and categorisation
- Using AI to detect data type inconsistencies
- Generating hierarchies from unstructured text
- Validating data model integrity with intelligent checks
Module 5: Automated Machine Learning (AutoML) Integration - Understanding Power BI’s integration with Azure AutoML
- Setting up Azure ML workspace from Power BI
- Configuring experiment settings for classification tasks
- Configuring experiment settings for regression tasks
- Interpreting model performance metrics (AUC, R², MAE)
- Selecting the best-performing model automatically
- Deploying trained models as Power BI measures
- Applying AutoML to predict customer churn
- Applying AutoML to forecast sales volumes
- Applying AutoML to classify risk levels
- Scheduling retraining cycles for model freshness
- Monitoring model drift and accuracy decay
- Integrating external AI models via APIs
- Securing model access with role-based permissions
- Exporting models for use in other enterprise systems
Module 6: Intelligent Visualisation Techniques - Using Key Influencers visual to explain data patterns
- Interpreting contribution analysis for decision-making
- Applying decomposition trees to explore data hierarchies
- Configuring dynamic drill paths using AI suggestions
- Enhancing scatter plots with clustering overlays
- Automating insight generation with Smart Narrative
- Customising AI-generated narratives for tone and clarity
- Creating multi-layered storytelling dashboards
- Using AI to recommend optimal chart types
- Personalising visual layouts based on user behaviour
- Implementing dynamic colour palettes based on sentiment
- Embedding real-time alert thresholds using anomaly detection
- Building interactive data exploration experiences
- Designing mobile-first AI dashboards
- Validating visual clarity with usability testing frameworks
Module 7: Forecasting and Predictive Analytics - Configuring built-in forecasting in line charts
- Adjusting seasonality and confidence intervals
- Validating forecast accuracy using backtesting
- Applying exponential smoothing models in Power BI
- Integrating Prophet models via Python scripts
- Building dynamic what-if scenarios with AI inputs
- Modelling demand under uncertainty
- Forecasting with multiple drivers and variables
- Automating forecast refreshes with scheduled updates
- Creating executive summary reports from forecasts
- Comparing actuals vs predicted with variance alerts
- Generating forecast commentary automatically
- Sharing forecasts securely with stakeholders
- Benchmarking forecasts against industry trends
- Refining forecasts based on user feedback
Module 8: Anomaly Detection and Alerting - Enabling anomaly detection in time series visuals
- Configuring sensitivity and historical baselines
- Interpreting anomaly scores and root cause hints
- Setting up real-time alerting via email
- Routing alerts to specific teams based on severity
- Integrating with Power Automate for automated response
- Creating incident dashboards for anomaly tracking
- Reducing false positives with contextual filters
- Archiving and auditing past anomalies
- Training AI on known business events to improve detection
- Visualising anomaly patterns over extended periods
- Using anomalies to trigger workflow automation
- Reporting on anomaly resolution effectiveness
- Scaling anomaly detection across multiple KPIs
- Implementing multi-metric anomaly correlation
Module 9: Dataflows and Azure Data Factory Integration - Creating AI-enhanced dataflows in Power BI
- Applying data deduplication using AI rules
- Standardising address and contact information automatically
- Extracting entities from unstructured text fields
- Leveraging cognitive services for language detection
- Using sentiment analysis in customer feedback columns
- Integrating with Azure Data Factory for orchestration
- Scheduling AI-powered ETL pipelines
- Monitoring dataflow execution and errors
- Version controlling dataflows for team collaboration
- Sharing dataflows across workspaces securely
- Optimising compute resources for cost efficiency
- Implementing data privacy rules in transformations
- Validating output quality before downstream use
- Building reusable transformation templates with AI logic
Module 10: Advanced DAX and AI Measures - Writing DAX formulas that incorporate AI model outputs
- Creating dynamic thresholds based on predictive scores
- Generating conditional formatting rules from AI insights
- Building measures that adapt to data context changes
- Using variables to improve AI measure readability
- Optimising calculation performance with AI guidance
- Implementing time intelligence with AI-adjusted baselines
- Creating risk-adjusted performance metrics
- Combining multiple AI signals into composite scores
- Documenting DAX logic for auditability
- Testing measures against edge cases
- Debugging AI-influenced calculations
- Sharing AI measures across reports
- Versioning and tracking changes to AI-driven logic
- Training team members on AI-enhanced DAX standards
Module 11: Power Automate and AI Workflow Automation - Connecting Power BI alerts to Power Automate flows
- Sending AI-generated insights via email summaries
- Posting critical findings to Teams channels
- Creating Jira tickets from anomaly detections
- Updating CRM records based on predictive scores
- Generating PDF reports and distributing them automatically
- Initiating approval workflows when thresholds are breached
- Archiving old reports using lifecycle policies
- Scheduling data refresh confirmation messages
- Automating onboarding sequences for new users
- Triggering retraining of AI models on data drift
- Escalating high-risk predictions to management
- Logging all automated actions for compliance
- Monitoring flow performance and error rates
- Designing resilient workflows with error handling
Module 12: Enterprise Deployment and Governance - Planning AI dashboard rollouts across departments
- Defining user access levels and data security policies
- Implementing row-level security with AI logic
- Configuring sensitivity labels for AI outputs
- Managing version control for published reports
- Establishing change management protocols
- Conducting impact assessments before deployment
- Creating user training materials for AI features
- Gathering stakeholder feedback iteratively
- Monitoring adoption and usage metrics
- Scaling AI solutions from pilot to production
- Integrating with organisational identity providers
- Auditing access and usage logs
- Aligning with GDPR and privacy regulations
- Developing a centralised BI governance framework
Module 13: Performance Optimisation and Scalability - Diagnosing slow report performance with AI tools
- Optimising data model size and compression
- Choosing between import and live connection modes
- Implementing aggregation tables for large datasets
- Leveraging Premium capacities for AI workloads
- Monitoring resource consumption in real time
- Setting up performance baselines and alerts
- Using Query Diagnostics to identify bottlenecks
- Reducing DAX calculation overhead
- Testing scalability with synthetic user loads
- Caching strategies for frequently used AI insights
- Planning for concurrency during peak usage
- Architecting for multi-region deployment
- Documenting performance optimisations
- Creating maintenance checklists for long-term health
Module 14: Building Your AI-Powered Portfolio Project - Selecting a high-visibility business problem to solve
- Defining project scope and success criteria
- Gathering and preparing relevant datasets
- Designing the data model architecture
- Integrating at least two AI features (e.g., forecasting + NLP)
- Implementing automated workflows
- Creating a board-ready executive summary
- Building interactive exploration paths
- Adding real-time alerting mechanisms
- Documenting technical decisions and rationale
- Testing with real users and iterating
- Publishing to a secure workspace
- Generating a final presentation deck
- Recording a narrative walkthrough of key insights
- Submitting for instructor review and feedback
Module 15: Certification, Career Acceleration, and Next Steps - Reviewing certification requirements and milestones
- Preparing for the Certificate of Completion assessment
- Submitting your final AI analytics project
- Receiving official verification from The Art of Service
- Sharing your credential on LinkedIn and professional networks
- Updating your resume with AI-driven Power BI expertise
- Positioning your skills in salary negotiations
- Preparing for technical interview questions on AI analytics
- Identifying internal promotion opportunities
- Transitioning into data science adjacent roles
- Joining the global alumni network
- Accessing ongoing industry updates and best practices
- Receiving invitations to exclusive professional events
- Exploring advanced specialisations in AI and ML
- Building a personal brand as an intelligent analytics leader
Module 1: Foundations of AI-Driven Analytics in Power BI - Understanding the AI revolution in business intelligence
- Core differences between traditional and AI-enhanced Power BI workflows
- Identifying high-impact use cases across industries
- Setting realistic, measurable goals for AI integration
- Overview of Power BI’s built-in AI capabilities and limitations
- Mapping AI features to common business problems
- Preparing your organizational data culture for AI adoption
- Aligning AI analytics with strategic business objectives
- Creating your personal AI analytics roadmap
- Establishing success metrics for your first AI project
Module 2: Data Preparation for AI Workflows - Structuring data to maximize AI model performance
- Automated data cleansing using Power Query
- Handling missing data with intelligent imputation techniques
- Standardising and normalising datasets for consistency
- Creating time-based features for forecasting models
- Optimising data schemas for AI readiness
- Validating data quality before AI processing
- Using Power BI dataflows to streamline preparation
- Automating repetitive data prep steps
- Documenting data lineage for governance and compliance
Module 3: Natural Language Processing in Power BI - Introduction to Q&A visual and how it leverages NLP
- Configuring synonyms and phrasing rules for accuracy
- Training the Q&A engine with domain-specific language
- Creating intuitive question prompts for stakeholders
- Analysing usage patterns to refine NLP performance
- Building voice-enabled dashboards with Power BI and Azure AI
- Integrating custom language models for enterprise jargon
- Deploying multilingual Q&A experiences
- Monitoring NLP accuracy over time
- Designing user onboarding for natural language dashboards
Module 4: AI-Powered Data Modelling - Leveraging AI for automatic relationship detection
- Using Auto Date/Time with intelligent granularity
- Implementing cognitive services in data models
- Enhancing model semantics with AI tagging
- Optimising model performance with AI diagnostics
- Automated schema suggestions based on data patterns
- AI-driven field grouping and categorisation
- Using AI to detect data type inconsistencies
- Generating hierarchies from unstructured text
- Validating data model integrity with intelligent checks
Module 5: Automated Machine Learning (AutoML) Integration - Understanding Power BI’s integration with Azure AutoML
- Setting up Azure ML workspace from Power BI
- Configuring experiment settings for classification tasks
- Configuring experiment settings for regression tasks
- Interpreting model performance metrics (AUC, R², MAE)
- Selecting the best-performing model automatically
- Deploying trained models as Power BI measures
- Applying AutoML to predict customer churn
- Applying AutoML to forecast sales volumes
- Applying AutoML to classify risk levels
- Scheduling retraining cycles for model freshness
- Monitoring model drift and accuracy decay
- Integrating external AI models via APIs
- Securing model access with role-based permissions
- Exporting models for use in other enterprise systems
Module 6: Intelligent Visualisation Techniques - Using Key Influencers visual to explain data patterns
- Interpreting contribution analysis for decision-making
- Applying decomposition trees to explore data hierarchies
- Configuring dynamic drill paths using AI suggestions
- Enhancing scatter plots with clustering overlays
- Automating insight generation with Smart Narrative
- Customising AI-generated narratives for tone and clarity
- Creating multi-layered storytelling dashboards
- Using AI to recommend optimal chart types
- Personalising visual layouts based on user behaviour
- Implementing dynamic colour palettes based on sentiment
- Embedding real-time alert thresholds using anomaly detection
- Building interactive data exploration experiences
- Designing mobile-first AI dashboards
- Validating visual clarity with usability testing frameworks
Module 7: Forecasting and Predictive Analytics - Configuring built-in forecasting in line charts
- Adjusting seasonality and confidence intervals
- Validating forecast accuracy using backtesting
- Applying exponential smoothing models in Power BI
- Integrating Prophet models via Python scripts
- Building dynamic what-if scenarios with AI inputs
- Modelling demand under uncertainty
- Forecasting with multiple drivers and variables
- Automating forecast refreshes with scheduled updates
- Creating executive summary reports from forecasts
- Comparing actuals vs predicted with variance alerts
- Generating forecast commentary automatically
- Sharing forecasts securely with stakeholders
- Benchmarking forecasts against industry trends
- Refining forecasts based on user feedback
Module 8: Anomaly Detection and Alerting - Enabling anomaly detection in time series visuals
- Configuring sensitivity and historical baselines
- Interpreting anomaly scores and root cause hints
- Setting up real-time alerting via email
- Routing alerts to specific teams based on severity
- Integrating with Power Automate for automated response
- Creating incident dashboards for anomaly tracking
- Reducing false positives with contextual filters
- Archiving and auditing past anomalies
- Training AI on known business events to improve detection
- Visualising anomaly patterns over extended periods
- Using anomalies to trigger workflow automation
- Reporting on anomaly resolution effectiveness
- Scaling anomaly detection across multiple KPIs
- Implementing multi-metric anomaly correlation
Module 9: Dataflows and Azure Data Factory Integration - Creating AI-enhanced dataflows in Power BI
- Applying data deduplication using AI rules
- Standardising address and contact information automatically
- Extracting entities from unstructured text fields
- Leveraging cognitive services for language detection
- Using sentiment analysis in customer feedback columns
- Integrating with Azure Data Factory for orchestration
- Scheduling AI-powered ETL pipelines
- Monitoring dataflow execution and errors
- Version controlling dataflows for team collaboration
- Sharing dataflows across workspaces securely
- Optimising compute resources for cost efficiency
- Implementing data privacy rules in transformations
- Validating output quality before downstream use
- Building reusable transformation templates with AI logic
Module 10: Advanced DAX and AI Measures - Writing DAX formulas that incorporate AI model outputs
- Creating dynamic thresholds based on predictive scores
- Generating conditional formatting rules from AI insights
- Building measures that adapt to data context changes
- Using variables to improve AI measure readability
- Optimising calculation performance with AI guidance
- Implementing time intelligence with AI-adjusted baselines
- Creating risk-adjusted performance metrics
- Combining multiple AI signals into composite scores
- Documenting DAX logic for auditability
- Testing measures against edge cases
- Debugging AI-influenced calculations
- Sharing AI measures across reports
- Versioning and tracking changes to AI-driven logic
- Training team members on AI-enhanced DAX standards
Module 11: Power Automate and AI Workflow Automation - Connecting Power BI alerts to Power Automate flows
- Sending AI-generated insights via email summaries
- Posting critical findings to Teams channels
- Creating Jira tickets from anomaly detections
- Updating CRM records based on predictive scores
- Generating PDF reports and distributing them automatically
- Initiating approval workflows when thresholds are breached
- Archiving old reports using lifecycle policies
- Scheduling data refresh confirmation messages
- Automating onboarding sequences for new users
- Triggering retraining of AI models on data drift
- Escalating high-risk predictions to management
- Logging all automated actions for compliance
- Monitoring flow performance and error rates
- Designing resilient workflows with error handling
Module 12: Enterprise Deployment and Governance - Planning AI dashboard rollouts across departments
- Defining user access levels and data security policies
- Implementing row-level security with AI logic
- Configuring sensitivity labels for AI outputs
- Managing version control for published reports
- Establishing change management protocols
- Conducting impact assessments before deployment
- Creating user training materials for AI features
- Gathering stakeholder feedback iteratively
- Monitoring adoption and usage metrics
- Scaling AI solutions from pilot to production
- Integrating with organisational identity providers
- Auditing access and usage logs
- Aligning with GDPR and privacy regulations
- Developing a centralised BI governance framework
Module 13: Performance Optimisation and Scalability - Diagnosing slow report performance with AI tools
- Optimising data model size and compression
- Choosing between import and live connection modes
- Implementing aggregation tables for large datasets
- Leveraging Premium capacities for AI workloads
- Monitoring resource consumption in real time
- Setting up performance baselines and alerts
- Using Query Diagnostics to identify bottlenecks
- Reducing DAX calculation overhead
- Testing scalability with synthetic user loads
- Caching strategies for frequently used AI insights
- Planning for concurrency during peak usage
- Architecting for multi-region deployment
- Documenting performance optimisations
- Creating maintenance checklists for long-term health
Module 14: Building Your AI-Powered Portfolio Project - Selecting a high-visibility business problem to solve
- Defining project scope and success criteria
- Gathering and preparing relevant datasets
- Designing the data model architecture
- Integrating at least two AI features (e.g., forecasting + NLP)
- Implementing automated workflows
- Creating a board-ready executive summary
- Building interactive exploration paths
- Adding real-time alerting mechanisms
- Documenting technical decisions and rationale
- Testing with real users and iterating
- Publishing to a secure workspace
- Generating a final presentation deck
- Recording a narrative walkthrough of key insights
- Submitting for instructor review and feedback
Module 15: Certification, Career Acceleration, and Next Steps - Reviewing certification requirements and milestones
- Preparing for the Certificate of Completion assessment
- Submitting your final AI analytics project
- Receiving official verification from The Art of Service
- Sharing your credential on LinkedIn and professional networks
- Updating your resume with AI-driven Power BI expertise
- Positioning your skills in salary negotiations
- Preparing for technical interview questions on AI analytics
- Identifying internal promotion opportunities
- Transitioning into data science adjacent roles
- Joining the global alumni network
- Accessing ongoing industry updates and best practices
- Receiving invitations to exclusive professional events
- Exploring advanced specialisations in AI and ML
- Building a personal brand as an intelligent analytics leader
- Structuring data to maximize AI model performance
- Automated data cleansing using Power Query
- Handling missing data with intelligent imputation techniques
- Standardising and normalising datasets for consistency
- Creating time-based features for forecasting models
- Optimising data schemas for AI readiness
- Validating data quality before AI processing
- Using Power BI dataflows to streamline preparation
- Automating repetitive data prep steps
- Documenting data lineage for governance and compliance
Module 3: Natural Language Processing in Power BI - Introduction to Q&A visual and how it leverages NLP
- Configuring synonyms and phrasing rules for accuracy
- Training the Q&A engine with domain-specific language
- Creating intuitive question prompts for stakeholders
- Analysing usage patterns to refine NLP performance
- Building voice-enabled dashboards with Power BI and Azure AI
- Integrating custom language models for enterprise jargon
- Deploying multilingual Q&A experiences
- Monitoring NLP accuracy over time
- Designing user onboarding for natural language dashboards
Module 4: AI-Powered Data Modelling - Leveraging AI for automatic relationship detection
- Using Auto Date/Time with intelligent granularity
- Implementing cognitive services in data models
- Enhancing model semantics with AI tagging
- Optimising model performance with AI diagnostics
- Automated schema suggestions based on data patterns
- AI-driven field grouping and categorisation
- Using AI to detect data type inconsistencies
- Generating hierarchies from unstructured text
- Validating data model integrity with intelligent checks
Module 5: Automated Machine Learning (AutoML) Integration - Understanding Power BI’s integration with Azure AutoML
- Setting up Azure ML workspace from Power BI
- Configuring experiment settings for classification tasks
- Configuring experiment settings for regression tasks
- Interpreting model performance metrics (AUC, R², MAE)
- Selecting the best-performing model automatically
- Deploying trained models as Power BI measures
- Applying AutoML to predict customer churn
- Applying AutoML to forecast sales volumes
- Applying AutoML to classify risk levels
- Scheduling retraining cycles for model freshness
- Monitoring model drift and accuracy decay
- Integrating external AI models via APIs
- Securing model access with role-based permissions
- Exporting models for use in other enterprise systems
Module 6: Intelligent Visualisation Techniques - Using Key Influencers visual to explain data patterns
- Interpreting contribution analysis for decision-making
- Applying decomposition trees to explore data hierarchies
- Configuring dynamic drill paths using AI suggestions
- Enhancing scatter plots with clustering overlays
- Automating insight generation with Smart Narrative
- Customising AI-generated narratives for tone and clarity
- Creating multi-layered storytelling dashboards
- Using AI to recommend optimal chart types
- Personalising visual layouts based on user behaviour
- Implementing dynamic colour palettes based on sentiment
- Embedding real-time alert thresholds using anomaly detection
- Building interactive data exploration experiences
- Designing mobile-first AI dashboards
- Validating visual clarity with usability testing frameworks
Module 7: Forecasting and Predictive Analytics - Configuring built-in forecasting in line charts
- Adjusting seasonality and confidence intervals
- Validating forecast accuracy using backtesting
- Applying exponential smoothing models in Power BI
- Integrating Prophet models via Python scripts
- Building dynamic what-if scenarios with AI inputs
- Modelling demand under uncertainty
- Forecasting with multiple drivers and variables
- Automating forecast refreshes with scheduled updates
- Creating executive summary reports from forecasts
- Comparing actuals vs predicted with variance alerts
- Generating forecast commentary automatically
- Sharing forecasts securely with stakeholders
- Benchmarking forecasts against industry trends
- Refining forecasts based on user feedback
Module 8: Anomaly Detection and Alerting - Enabling anomaly detection in time series visuals
- Configuring sensitivity and historical baselines
- Interpreting anomaly scores and root cause hints
- Setting up real-time alerting via email
- Routing alerts to specific teams based on severity
- Integrating with Power Automate for automated response
- Creating incident dashboards for anomaly tracking
- Reducing false positives with contextual filters
- Archiving and auditing past anomalies
- Training AI on known business events to improve detection
- Visualising anomaly patterns over extended periods
- Using anomalies to trigger workflow automation
- Reporting on anomaly resolution effectiveness
- Scaling anomaly detection across multiple KPIs
- Implementing multi-metric anomaly correlation
Module 9: Dataflows and Azure Data Factory Integration - Creating AI-enhanced dataflows in Power BI
- Applying data deduplication using AI rules
- Standardising address and contact information automatically
- Extracting entities from unstructured text fields
- Leveraging cognitive services for language detection
- Using sentiment analysis in customer feedback columns
- Integrating with Azure Data Factory for orchestration
- Scheduling AI-powered ETL pipelines
- Monitoring dataflow execution and errors
- Version controlling dataflows for team collaboration
- Sharing dataflows across workspaces securely
- Optimising compute resources for cost efficiency
- Implementing data privacy rules in transformations
- Validating output quality before downstream use
- Building reusable transformation templates with AI logic
Module 10: Advanced DAX and AI Measures - Writing DAX formulas that incorporate AI model outputs
- Creating dynamic thresholds based on predictive scores
- Generating conditional formatting rules from AI insights
- Building measures that adapt to data context changes
- Using variables to improve AI measure readability
- Optimising calculation performance with AI guidance
- Implementing time intelligence with AI-adjusted baselines
- Creating risk-adjusted performance metrics
- Combining multiple AI signals into composite scores
- Documenting DAX logic for auditability
- Testing measures against edge cases
- Debugging AI-influenced calculations
- Sharing AI measures across reports
- Versioning and tracking changes to AI-driven logic
- Training team members on AI-enhanced DAX standards
Module 11: Power Automate and AI Workflow Automation - Connecting Power BI alerts to Power Automate flows
- Sending AI-generated insights via email summaries
- Posting critical findings to Teams channels
- Creating Jira tickets from anomaly detections
- Updating CRM records based on predictive scores
- Generating PDF reports and distributing them automatically
- Initiating approval workflows when thresholds are breached
- Archiving old reports using lifecycle policies
- Scheduling data refresh confirmation messages
- Automating onboarding sequences for new users
- Triggering retraining of AI models on data drift
- Escalating high-risk predictions to management
- Logging all automated actions for compliance
- Monitoring flow performance and error rates
- Designing resilient workflows with error handling
Module 12: Enterprise Deployment and Governance - Planning AI dashboard rollouts across departments
- Defining user access levels and data security policies
- Implementing row-level security with AI logic
- Configuring sensitivity labels for AI outputs
- Managing version control for published reports
- Establishing change management protocols
- Conducting impact assessments before deployment
- Creating user training materials for AI features
- Gathering stakeholder feedback iteratively
- Monitoring adoption and usage metrics
- Scaling AI solutions from pilot to production
- Integrating with organisational identity providers
- Auditing access and usage logs
- Aligning with GDPR and privacy regulations
- Developing a centralised BI governance framework
Module 13: Performance Optimisation and Scalability - Diagnosing slow report performance with AI tools
- Optimising data model size and compression
- Choosing between import and live connection modes
- Implementing aggregation tables for large datasets
- Leveraging Premium capacities for AI workloads
- Monitoring resource consumption in real time
- Setting up performance baselines and alerts
- Using Query Diagnostics to identify bottlenecks
- Reducing DAX calculation overhead
- Testing scalability with synthetic user loads
- Caching strategies for frequently used AI insights
- Planning for concurrency during peak usage
- Architecting for multi-region deployment
- Documenting performance optimisations
- Creating maintenance checklists for long-term health
Module 14: Building Your AI-Powered Portfolio Project - Selecting a high-visibility business problem to solve
- Defining project scope and success criteria
- Gathering and preparing relevant datasets
- Designing the data model architecture
- Integrating at least two AI features (e.g., forecasting + NLP)
- Implementing automated workflows
- Creating a board-ready executive summary
- Building interactive exploration paths
- Adding real-time alerting mechanisms
- Documenting technical decisions and rationale
- Testing with real users and iterating
- Publishing to a secure workspace
- Generating a final presentation deck
- Recording a narrative walkthrough of key insights
- Submitting for instructor review and feedback
Module 15: Certification, Career Acceleration, and Next Steps - Reviewing certification requirements and milestones
- Preparing for the Certificate of Completion assessment
- Submitting your final AI analytics project
- Receiving official verification from The Art of Service
- Sharing your credential on LinkedIn and professional networks
- Updating your resume with AI-driven Power BI expertise
- Positioning your skills in salary negotiations
- Preparing for technical interview questions on AI analytics
- Identifying internal promotion opportunities
- Transitioning into data science adjacent roles
- Joining the global alumni network
- Accessing ongoing industry updates and best practices
- Receiving invitations to exclusive professional events
- Exploring advanced specialisations in AI and ML
- Building a personal brand as an intelligent analytics leader
- Leveraging AI for automatic relationship detection
- Using Auto Date/Time with intelligent granularity
- Implementing cognitive services in data models
- Enhancing model semantics with AI tagging
- Optimising model performance with AI diagnostics
- Automated schema suggestions based on data patterns
- AI-driven field grouping and categorisation
- Using AI to detect data type inconsistencies
- Generating hierarchies from unstructured text
- Validating data model integrity with intelligent checks
Module 5: Automated Machine Learning (AutoML) Integration - Understanding Power BI’s integration with Azure AutoML
- Setting up Azure ML workspace from Power BI
- Configuring experiment settings for classification tasks
- Configuring experiment settings for regression tasks
- Interpreting model performance metrics (AUC, R², MAE)
- Selecting the best-performing model automatically
- Deploying trained models as Power BI measures
- Applying AutoML to predict customer churn
- Applying AutoML to forecast sales volumes
- Applying AutoML to classify risk levels
- Scheduling retraining cycles for model freshness
- Monitoring model drift and accuracy decay
- Integrating external AI models via APIs
- Securing model access with role-based permissions
- Exporting models for use in other enterprise systems
Module 6: Intelligent Visualisation Techniques - Using Key Influencers visual to explain data patterns
- Interpreting contribution analysis for decision-making
- Applying decomposition trees to explore data hierarchies
- Configuring dynamic drill paths using AI suggestions
- Enhancing scatter plots with clustering overlays
- Automating insight generation with Smart Narrative
- Customising AI-generated narratives for tone and clarity
- Creating multi-layered storytelling dashboards
- Using AI to recommend optimal chart types
- Personalising visual layouts based on user behaviour
- Implementing dynamic colour palettes based on sentiment
- Embedding real-time alert thresholds using anomaly detection
- Building interactive data exploration experiences
- Designing mobile-first AI dashboards
- Validating visual clarity with usability testing frameworks
Module 7: Forecasting and Predictive Analytics - Configuring built-in forecasting in line charts
- Adjusting seasonality and confidence intervals
- Validating forecast accuracy using backtesting
- Applying exponential smoothing models in Power BI
- Integrating Prophet models via Python scripts
- Building dynamic what-if scenarios with AI inputs
- Modelling demand under uncertainty
- Forecasting with multiple drivers and variables
- Automating forecast refreshes with scheduled updates
- Creating executive summary reports from forecasts
- Comparing actuals vs predicted with variance alerts
- Generating forecast commentary automatically
- Sharing forecasts securely with stakeholders
- Benchmarking forecasts against industry trends
- Refining forecasts based on user feedback
Module 8: Anomaly Detection and Alerting - Enabling anomaly detection in time series visuals
- Configuring sensitivity and historical baselines
- Interpreting anomaly scores and root cause hints
- Setting up real-time alerting via email
- Routing alerts to specific teams based on severity
- Integrating with Power Automate for automated response
- Creating incident dashboards for anomaly tracking
- Reducing false positives with contextual filters
- Archiving and auditing past anomalies
- Training AI on known business events to improve detection
- Visualising anomaly patterns over extended periods
- Using anomalies to trigger workflow automation
- Reporting on anomaly resolution effectiveness
- Scaling anomaly detection across multiple KPIs
- Implementing multi-metric anomaly correlation
Module 9: Dataflows and Azure Data Factory Integration - Creating AI-enhanced dataflows in Power BI
- Applying data deduplication using AI rules
- Standardising address and contact information automatically
- Extracting entities from unstructured text fields
- Leveraging cognitive services for language detection
- Using sentiment analysis in customer feedback columns
- Integrating with Azure Data Factory for orchestration
- Scheduling AI-powered ETL pipelines
- Monitoring dataflow execution and errors
- Version controlling dataflows for team collaboration
- Sharing dataflows across workspaces securely
- Optimising compute resources for cost efficiency
- Implementing data privacy rules in transformations
- Validating output quality before downstream use
- Building reusable transformation templates with AI logic
Module 10: Advanced DAX and AI Measures - Writing DAX formulas that incorporate AI model outputs
- Creating dynamic thresholds based on predictive scores
- Generating conditional formatting rules from AI insights
- Building measures that adapt to data context changes
- Using variables to improve AI measure readability
- Optimising calculation performance with AI guidance
- Implementing time intelligence with AI-adjusted baselines
- Creating risk-adjusted performance metrics
- Combining multiple AI signals into composite scores
- Documenting DAX logic for auditability
- Testing measures against edge cases
- Debugging AI-influenced calculations
- Sharing AI measures across reports
- Versioning and tracking changes to AI-driven logic
- Training team members on AI-enhanced DAX standards
Module 11: Power Automate and AI Workflow Automation - Connecting Power BI alerts to Power Automate flows
- Sending AI-generated insights via email summaries
- Posting critical findings to Teams channels
- Creating Jira tickets from anomaly detections
- Updating CRM records based on predictive scores
- Generating PDF reports and distributing them automatically
- Initiating approval workflows when thresholds are breached
- Archiving old reports using lifecycle policies
- Scheduling data refresh confirmation messages
- Automating onboarding sequences for new users
- Triggering retraining of AI models on data drift
- Escalating high-risk predictions to management
- Logging all automated actions for compliance
- Monitoring flow performance and error rates
- Designing resilient workflows with error handling
Module 12: Enterprise Deployment and Governance - Planning AI dashboard rollouts across departments
- Defining user access levels and data security policies
- Implementing row-level security with AI logic
- Configuring sensitivity labels for AI outputs
- Managing version control for published reports
- Establishing change management protocols
- Conducting impact assessments before deployment
- Creating user training materials for AI features
- Gathering stakeholder feedback iteratively
- Monitoring adoption and usage metrics
- Scaling AI solutions from pilot to production
- Integrating with organisational identity providers
- Auditing access and usage logs
- Aligning with GDPR and privacy regulations
- Developing a centralised BI governance framework
Module 13: Performance Optimisation and Scalability - Diagnosing slow report performance with AI tools
- Optimising data model size and compression
- Choosing between import and live connection modes
- Implementing aggregation tables for large datasets
- Leveraging Premium capacities for AI workloads
- Monitoring resource consumption in real time
- Setting up performance baselines and alerts
- Using Query Diagnostics to identify bottlenecks
- Reducing DAX calculation overhead
- Testing scalability with synthetic user loads
- Caching strategies for frequently used AI insights
- Planning for concurrency during peak usage
- Architecting for multi-region deployment
- Documenting performance optimisations
- Creating maintenance checklists for long-term health
Module 14: Building Your AI-Powered Portfolio Project - Selecting a high-visibility business problem to solve
- Defining project scope and success criteria
- Gathering and preparing relevant datasets
- Designing the data model architecture
- Integrating at least two AI features (e.g., forecasting + NLP)
- Implementing automated workflows
- Creating a board-ready executive summary
- Building interactive exploration paths
- Adding real-time alerting mechanisms
- Documenting technical decisions and rationale
- Testing with real users and iterating
- Publishing to a secure workspace
- Generating a final presentation deck
- Recording a narrative walkthrough of key insights
- Submitting for instructor review and feedback
Module 15: Certification, Career Acceleration, and Next Steps - Reviewing certification requirements and milestones
- Preparing for the Certificate of Completion assessment
- Submitting your final AI analytics project
- Receiving official verification from The Art of Service
- Sharing your credential on LinkedIn and professional networks
- Updating your resume with AI-driven Power BI expertise
- Positioning your skills in salary negotiations
- Preparing for technical interview questions on AI analytics
- Identifying internal promotion opportunities
- Transitioning into data science adjacent roles
- Joining the global alumni network
- Accessing ongoing industry updates and best practices
- Receiving invitations to exclusive professional events
- Exploring advanced specialisations in AI and ML
- Building a personal brand as an intelligent analytics leader
- Using Key Influencers visual to explain data patterns
- Interpreting contribution analysis for decision-making
- Applying decomposition trees to explore data hierarchies
- Configuring dynamic drill paths using AI suggestions
- Enhancing scatter plots with clustering overlays
- Automating insight generation with Smart Narrative
- Customising AI-generated narratives for tone and clarity
- Creating multi-layered storytelling dashboards
- Using AI to recommend optimal chart types
- Personalising visual layouts based on user behaviour
- Implementing dynamic colour palettes based on sentiment
- Embedding real-time alert thresholds using anomaly detection
- Building interactive data exploration experiences
- Designing mobile-first AI dashboards
- Validating visual clarity with usability testing frameworks
Module 7: Forecasting and Predictive Analytics - Configuring built-in forecasting in line charts
- Adjusting seasonality and confidence intervals
- Validating forecast accuracy using backtesting
- Applying exponential smoothing models in Power BI
- Integrating Prophet models via Python scripts
- Building dynamic what-if scenarios with AI inputs
- Modelling demand under uncertainty
- Forecasting with multiple drivers and variables
- Automating forecast refreshes with scheduled updates
- Creating executive summary reports from forecasts
- Comparing actuals vs predicted with variance alerts
- Generating forecast commentary automatically
- Sharing forecasts securely with stakeholders
- Benchmarking forecasts against industry trends
- Refining forecasts based on user feedback
Module 8: Anomaly Detection and Alerting - Enabling anomaly detection in time series visuals
- Configuring sensitivity and historical baselines
- Interpreting anomaly scores and root cause hints
- Setting up real-time alerting via email
- Routing alerts to specific teams based on severity
- Integrating with Power Automate for automated response
- Creating incident dashboards for anomaly tracking
- Reducing false positives with contextual filters
- Archiving and auditing past anomalies
- Training AI on known business events to improve detection
- Visualising anomaly patterns over extended periods
- Using anomalies to trigger workflow automation
- Reporting on anomaly resolution effectiveness
- Scaling anomaly detection across multiple KPIs
- Implementing multi-metric anomaly correlation
Module 9: Dataflows and Azure Data Factory Integration - Creating AI-enhanced dataflows in Power BI
- Applying data deduplication using AI rules
- Standardising address and contact information automatically
- Extracting entities from unstructured text fields
- Leveraging cognitive services for language detection
- Using sentiment analysis in customer feedback columns
- Integrating with Azure Data Factory for orchestration
- Scheduling AI-powered ETL pipelines
- Monitoring dataflow execution and errors
- Version controlling dataflows for team collaboration
- Sharing dataflows across workspaces securely
- Optimising compute resources for cost efficiency
- Implementing data privacy rules in transformations
- Validating output quality before downstream use
- Building reusable transformation templates with AI logic
Module 10: Advanced DAX and AI Measures - Writing DAX formulas that incorporate AI model outputs
- Creating dynamic thresholds based on predictive scores
- Generating conditional formatting rules from AI insights
- Building measures that adapt to data context changes
- Using variables to improve AI measure readability
- Optimising calculation performance with AI guidance
- Implementing time intelligence with AI-adjusted baselines
- Creating risk-adjusted performance metrics
- Combining multiple AI signals into composite scores
- Documenting DAX logic for auditability
- Testing measures against edge cases
- Debugging AI-influenced calculations
- Sharing AI measures across reports
- Versioning and tracking changes to AI-driven logic
- Training team members on AI-enhanced DAX standards
Module 11: Power Automate and AI Workflow Automation - Connecting Power BI alerts to Power Automate flows
- Sending AI-generated insights via email summaries
- Posting critical findings to Teams channels
- Creating Jira tickets from anomaly detections
- Updating CRM records based on predictive scores
- Generating PDF reports and distributing them automatically
- Initiating approval workflows when thresholds are breached
- Archiving old reports using lifecycle policies
- Scheduling data refresh confirmation messages
- Automating onboarding sequences for new users
- Triggering retraining of AI models on data drift
- Escalating high-risk predictions to management
- Logging all automated actions for compliance
- Monitoring flow performance and error rates
- Designing resilient workflows with error handling
Module 12: Enterprise Deployment and Governance - Planning AI dashboard rollouts across departments
- Defining user access levels and data security policies
- Implementing row-level security with AI logic
- Configuring sensitivity labels for AI outputs
- Managing version control for published reports
- Establishing change management protocols
- Conducting impact assessments before deployment
- Creating user training materials for AI features
- Gathering stakeholder feedback iteratively
- Monitoring adoption and usage metrics
- Scaling AI solutions from pilot to production
- Integrating with organisational identity providers
- Auditing access and usage logs
- Aligning with GDPR and privacy regulations
- Developing a centralised BI governance framework
Module 13: Performance Optimisation and Scalability - Diagnosing slow report performance with AI tools
- Optimising data model size and compression
- Choosing between import and live connection modes
- Implementing aggregation tables for large datasets
- Leveraging Premium capacities for AI workloads
- Monitoring resource consumption in real time
- Setting up performance baselines and alerts
- Using Query Diagnostics to identify bottlenecks
- Reducing DAX calculation overhead
- Testing scalability with synthetic user loads
- Caching strategies for frequently used AI insights
- Planning for concurrency during peak usage
- Architecting for multi-region deployment
- Documenting performance optimisations
- Creating maintenance checklists for long-term health
Module 14: Building Your AI-Powered Portfolio Project - Selecting a high-visibility business problem to solve
- Defining project scope and success criteria
- Gathering and preparing relevant datasets
- Designing the data model architecture
- Integrating at least two AI features (e.g., forecasting + NLP)
- Implementing automated workflows
- Creating a board-ready executive summary
- Building interactive exploration paths
- Adding real-time alerting mechanisms
- Documenting technical decisions and rationale
- Testing with real users and iterating
- Publishing to a secure workspace
- Generating a final presentation deck
- Recording a narrative walkthrough of key insights
- Submitting for instructor review and feedback
Module 15: Certification, Career Acceleration, and Next Steps - Reviewing certification requirements and milestones
- Preparing for the Certificate of Completion assessment
- Submitting your final AI analytics project
- Receiving official verification from The Art of Service
- Sharing your credential on LinkedIn and professional networks
- Updating your resume with AI-driven Power BI expertise
- Positioning your skills in salary negotiations
- Preparing for technical interview questions on AI analytics
- Identifying internal promotion opportunities
- Transitioning into data science adjacent roles
- Joining the global alumni network
- Accessing ongoing industry updates and best practices
- Receiving invitations to exclusive professional events
- Exploring advanced specialisations in AI and ML
- Building a personal brand as an intelligent analytics leader
- Enabling anomaly detection in time series visuals
- Configuring sensitivity and historical baselines
- Interpreting anomaly scores and root cause hints
- Setting up real-time alerting via email
- Routing alerts to specific teams based on severity
- Integrating with Power Automate for automated response
- Creating incident dashboards for anomaly tracking
- Reducing false positives with contextual filters
- Archiving and auditing past anomalies
- Training AI on known business events to improve detection
- Visualising anomaly patterns over extended periods
- Using anomalies to trigger workflow automation
- Reporting on anomaly resolution effectiveness
- Scaling anomaly detection across multiple KPIs
- Implementing multi-metric anomaly correlation
Module 9: Dataflows and Azure Data Factory Integration - Creating AI-enhanced dataflows in Power BI
- Applying data deduplication using AI rules
- Standardising address and contact information automatically
- Extracting entities from unstructured text fields
- Leveraging cognitive services for language detection
- Using sentiment analysis in customer feedback columns
- Integrating with Azure Data Factory for orchestration
- Scheduling AI-powered ETL pipelines
- Monitoring dataflow execution and errors
- Version controlling dataflows for team collaboration
- Sharing dataflows across workspaces securely
- Optimising compute resources for cost efficiency
- Implementing data privacy rules in transformations
- Validating output quality before downstream use
- Building reusable transformation templates with AI logic
Module 10: Advanced DAX and AI Measures - Writing DAX formulas that incorporate AI model outputs
- Creating dynamic thresholds based on predictive scores
- Generating conditional formatting rules from AI insights
- Building measures that adapt to data context changes
- Using variables to improve AI measure readability
- Optimising calculation performance with AI guidance
- Implementing time intelligence with AI-adjusted baselines
- Creating risk-adjusted performance metrics
- Combining multiple AI signals into composite scores
- Documenting DAX logic for auditability
- Testing measures against edge cases
- Debugging AI-influenced calculations
- Sharing AI measures across reports
- Versioning and tracking changes to AI-driven logic
- Training team members on AI-enhanced DAX standards
Module 11: Power Automate and AI Workflow Automation - Connecting Power BI alerts to Power Automate flows
- Sending AI-generated insights via email summaries
- Posting critical findings to Teams channels
- Creating Jira tickets from anomaly detections
- Updating CRM records based on predictive scores
- Generating PDF reports and distributing them automatically
- Initiating approval workflows when thresholds are breached
- Archiving old reports using lifecycle policies
- Scheduling data refresh confirmation messages
- Automating onboarding sequences for new users
- Triggering retraining of AI models on data drift
- Escalating high-risk predictions to management
- Logging all automated actions for compliance
- Monitoring flow performance and error rates
- Designing resilient workflows with error handling
Module 12: Enterprise Deployment and Governance - Planning AI dashboard rollouts across departments
- Defining user access levels and data security policies
- Implementing row-level security with AI logic
- Configuring sensitivity labels for AI outputs
- Managing version control for published reports
- Establishing change management protocols
- Conducting impact assessments before deployment
- Creating user training materials for AI features
- Gathering stakeholder feedback iteratively
- Monitoring adoption and usage metrics
- Scaling AI solutions from pilot to production
- Integrating with organisational identity providers
- Auditing access and usage logs
- Aligning with GDPR and privacy regulations
- Developing a centralised BI governance framework
Module 13: Performance Optimisation and Scalability - Diagnosing slow report performance with AI tools
- Optimising data model size and compression
- Choosing between import and live connection modes
- Implementing aggregation tables for large datasets
- Leveraging Premium capacities for AI workloads
- Monitoring resource consumption in real time
- Setting up performance baselines and alerts
- Using Query Diagnostics to identify bottlenecks
- Reducing DAX calculation overhead
- Testing scalability with synthetic user loads
- Caching strategies for frequently used AI insights
- Planning for concurrency during peak usage
- Architecting for multi-region deployment
- Documenting performance optimisations
- Creating maintenance checklists for long-term health
Module 14: Building Your AI-Powered Portfolio Project - Selecting a high-visibility business problem to solve
- Defining project scope and success criteria
- Gathering and preparing relevant datasets
- Designing the data model architecture
- Integrating at least two AI features (e.g., forecasting + NLP)
- Implementing automated workflows
- Creating a board-ready executive summary
- Building interactive exploration paths
- Adding real-time alerting mechanisms
- Documenting technical decisions and rationale
- Testing with real users and iterating
- Publishing to a secure workspace
- Generating a final presentation deck
- Recording a narrative walkthrough of key insights
- Submitting for instructor review and feedback
Module 15: Certification, Career Acceleration, and Next Steps - Reviewing certification requirements and milestones
- Preparing for the Certificate of Completion assessment
- Submitting your final AI analytics project
- Receiving official verification from The Art of Service
- Sharing your credential on LinkedIn and professional networks
- Updating your resume with AI-driven Power BI expertise
- Positioning your skills in salary negotiations
- Preparing for technical interview questions on AI analytics
- Identifying internal promotion opportunities
- Transitioning into data science adjacent roles
- Joining the global alumni network
- Accessing ongoing industry updates and best practices
- Receiving invitations to exclusive professional events
- Exploring advanced specialisations in AI and ML
- Building a personal brand as an intelligent analytics leader
- Writing DAX formulas that incorporate AI model outputs
- Creating dynamic thresholds based on predictive scores
- Generating conditional formatting rules from AI insights
- Building measures that adapt to data context changes
- Using variables to improve AI measure readability
- Optimising calculation performance with AI guidance
- Implementing time intelligence with AI-adjusted baselines
- Creating risk-adjusted performance metrics
- Combining multiple AI signals into composite scores
- Documenting DAX logic for auditability
- Testing measures against edge cases
- Debugging AI-influenced calculations
- Sharing AI measures across reports
- Versioning and tracking changes to AI-driven logic
- Training team members on AI-enhanced DAX standards
Module 11: Power Automate and AI Workflow Automation - Connecting Power BI alerts to Power Automate flows
- Sending AI-generated insights via email summaries
- Posting critical findings to Teams channels
- Creating Jira tickets from anomaly detections
- Updating CRM records based on predictive scores
- Generating PDF reports and distributing them automatically
- Initiating approval workflows when thresholds are breached
- Archiving old reports using lifecycle policies
- Scheduling data refresh confirmation messages
- Automating onboarding sequences for new users
- Triggering retraining of AI models on data drift
- Escalating high-risk predictions to management
- Logging all automated actions for compliance
- Monitoring flow performance and error rates
- Designing resilient workflows with error handling
Module 12: Enterprise Deployment and Governance - Planning AI dashboard rollouts across departments
- Defining user access levels and data security policies
- Implementing row-level security with AI logic
- Configuring sensitivity labels for AI outputs
- Managing version control for published reports
- Establishing change management protocols
- Conducting impact assessments before deployment
- Creating user training materials for AI features
- Gathering stakeholder feedback iteratively
- Monitoring adoption and usage metrics
- Scaling AI solutions from pilot to production
- Integrating with organisational identity providers
- Auditing access and usage logs
- Aligning with GDPR and privacy regulations
- Developing a centralised BI governance framework
Module 13: Performance Optimisation and Scalability - Diagnosing slow report performance with AI tools
- Optimising data model size and compression
- Choosing between import and live connection modes
- Implementing aggregation tables for large datasets
- Leveraging Premium capacities for AI workloads
- Monitoring resource consumption in real time
- Setting up performance baselines and alerts
- Using Query Diagnostics to identify bottlenecks
- Reducing DAX calculation overhead
- Testing scalability with synthetic user loads
- Caching strategies for frequently used AI insights
- Planning for concurrency during peak usage
- Architecting for multi-region deployment
- Documenting performance optimisations
- Creating maintenance checklists for long-term health
Module 14: Building Your AI-Powered Portfolio Project - Selecting a high-visibility business problem to solve
- Defining project scope and success criteria
- Gathering and preparing relevant datasets
- Designing the data model architecture
- Integrating at least two AI features (e.g., forecasting + NLP)
- Implementing automated workflows
- Creating a board-ready executive summary
- Building interactive exploration paths
- Adding real-time alerting mechanisms
- Documenting technical decisions and rationale
- Testing with real users and iterating
- Publishing to a secure workspace
- Generating a final presentation deck
- Recording a narrative walkthrough of key insights
- Submitting for instructor review and feedback
Module 15: Certification, Career Acceleration, and Next Steps - Reviewing certification requirements and milestones
- Preparing for the Certificate of Completion assessment
- Submitting your final AI analytics project
- Receiving official verification from The Art of Service
- Sharing your credential on LinkedIn and professional networks
- Updating your resume with AI-driven Power BI expertise
- Positioning your skills in salary negotiations
- Preparing for technical interview questions on AI analytics
- Identifying internal promotion opportunities
- Transitioning into data science adjacent roles
- Joining the global alumni network
- Accessing ongoing industry updates and best practices
- Receiving invitations to exclusive professional events
- Exploring advanced specialisations in AI and ML
- Building a personal brand as an intelligent analytics leader
- Planning AI dashboard rollouts across departments
- Defining user access levels and data security policies
- Implementing row-level security with AI logic
- Configuring sensitivity labels for AI outputs
- Managing version control for published reports
- Establishing change management protocols
- Conducting impact assessments before deployment
- Creating user training materials for AI features
- Gathering stakeholder feedback iteratively
- Monitoring adoption and usage metrics
- Scaling AI solutions from pilot to production
- Integrating with organisational identity providers
- Auditing access and usage logs
- Aligning with GDPR and privacy regulations
- Developing a centralised BI governance framework
Module 13: Performance Optimisation and Scalability - Diagnosing slow report performance with AI tools
- Optimising data model size and compression
- Choosing between import and live connection modes
- Implementing aggregation tables for large datasets
- Leveraging Premium capacities for AI workloads
- Monitoring resource consumption in real time
- Setting up performance baselines and alerts
- Using Query Diagnostics to identify bottlenecks
- Reducing DAX calculation overhead
- Testing scalability with synthetic user loads
- Caching strategies for frequently used AI insights
- Planning for concurrency during peak usage
- Architecting for multi-region deployment
- Documenting performance optimisations
- Creating maintenance checklists for long-term health
Module 14: Building Your AI-Powered Portfolio Project - Selecting a high-visibility business problem to solve
- Defining project scope and success criteria
- Gathering and preparing relevant datasets
- Designing the data model architecture
- Integrating at least two AI features (e.g., forecasting + NLP)
- Implementing automated workflows
- Creating a board-ready executive summary
- Building interactive exploration paths
- Adding real-time alerting mechanisms
- Documenting technical decisions and rationale
- Testing with real users and iterating
- Publishing to a secure workspace
- Generating a final presentation deck
- Recording a narrative walkthrough of key insights
- Submitting for instructor review and feedback
Module 15: Certification, Career Acceleration, and Next Steps - Reviewing certification requirements and milestones
- Preparing for the Certificate of Completion assessment
- Submitting your final AI analytics project
- Receiving official verification from The Art of Service
- Sharing your credential on LinkedIn and professional networks
- Updating your resume with AI-driven Power BI expertise
- Positioning your skills in salary negotiations
- Preparing for technical interview questions on AI analytics
- Identifying internal promotion opportunities
- Transitioning into data science adjacent roles
- Joining the global alumni network
- Accessing ongoing industry updates and best practices
- Receiving invitations to exclusive professional events
- Exploring advanced specialisations in AI and ML
- Building a personal brand as an intelligent analytics leader
- Selecting a high-visibility business problem to solve
- Defining project scope and success criteria
- Gathering and preparing relevant datasets
- Designing the data model architecture
- Integrating at least two AI features (e.g., forecasting + NLP)
- Implementing automated workflows
- Creating a board-ready executive summary
- Building interactive exploration paths
- Adding real-time alerting mechanisms
- Documenting technical decisions and rationale
- Testing with real users and iterating
- Publishing to a secure workspace
- Generating a final presentation deck
- Recording a narrative walkthrough of key insights
- Submitting for instructor review and feedback