AI driven Data Analytics and Business Intelligence #254010

Upon successful completion of this course, participants will be able to:

• Differentiate between traditional descriptive analytics and AI-driven predictive & prescriptive analytics.

• Build and interpret machine learning models for core business tasks like forecasting, classification, and customer segmentation.

• Integrate AI capabilities (e.g., NLP, anomaly detection) directly into BI dashboards and reports.

• Automate data workflows and insight generation using AI-powered data preparation and analytics platforms.

• Design an effective strategy for implementing and scaling AI-driven analytics within an organization.

• Communicate AI-generated insights effectively to stakeholders to drive actionable business outcomes.

This course is designed for data professionals, analysts, and business leaders who want to harness the power of AI in their analytics practice.

• Data Analysts & Business Intelligence Developers

• Data Scientists looking to improve their deployment into business workflows

• Business Analysts & Product Analysts

• BI Managers & Heads of Analytics

• IT Professionals involved in data warehouse and analytics infrastructure

• Financial Analysts & Marketing Analysts seeking advanced forecasting skills

Prerequisites: Foundational understanding of data analysis and BI concepts (e.g., SQL, data visualization, Excel). No prior programming or AI expertise is required, but a willingness to engage with code-friendly platforms (e.g., Python, R) is beneficial.

• Pre-assessment
• Live group instruction
• Use of real-world examples, case studies and exercises
• Interactive participation and discussion
• Power point presentation, LCD and flip chart
• Group activities and tests
• Each participant receives a binder containing a copy of the presentation
• slides and handouts
• Post-assessment

Day 1: The New Landscape of AI-Powered Analytics

• Module 1: The Evolution of BI: From Descriptive to Prescriptive

  • The limitations of traditional dashboards. Introduction to predictive and prescriptive analytics.

  • Overview of the AI-driven analytics workflow: data -> ML -> insight -> action.

• Module 2: Foundational Tools and Platforms

  • Landscape of tools: Cloud BI with AI (Power BI, Tableau, Looker), AutoML platforms, and custom code (Python/R).

  • Introduction to the core technical environment (e.g., Jupyter notebooks, cloud consoles).

• Hands-On Lab: Connecting to a data source and using an AutoML tool (e.g., Power BI AutoML or DataRobot) to generate a first predictive model.

Day 2: Building Predictive Models for Business

• Module 3: Core Machine Learning for Analysts

  • Key algorithms for business: Regression for forecasting, Classification for propensity modeling, and Clustering for segmentation.

  • How to select the right algorithm for your business question.

• Module 4: The Modeling Process

  • From data preparation to feature engineering for ML.

  • Evaluating model performance: interpreting accuracy, precision, recall, and AUC.

• Hands-On Lab: Building a customer churn prediction model from scratch using a cloud AutoML platform.

Day 3: Integrating AI into the BI Workflow

• Module 5: Advanced Analytics in Visualization Tools

  • Embedding AI insights directly into dashboards (e.g., Power BI’s AI Insights, Tableau’s Einstein Discovery).

  • Creating “what-if” parameters and interactive forecasting visuals.

• Module 6: Natural Language for Analytics

  • Natural Language Query (NLQ): Building dashboards that users can question with text.

  • Generative BI: Using AI to automatically generate narrative summaries of reports.

• Hands-On Lab: Enhancing an existing sales dashboard with predictive forecasts, anomaly detection, and a natural language Q&A feature.

Day 4: Operationalizing AI Analytics

• Module 7: MLOps for Analytics

  • Automating model retraining and deployment pipelines.

  • Monitoring models in production for performance decay and data drift.

• Module 8: Designing for Action and Impact

  • Building alert systems that trigger actions based on AI predictions (e.g., alerting a sales rep to a high-risk customer).

  • Measuring the ROI of AI-driven analytics initiatives.

• Case Study: Deconstructing a fully operational AI-driven analytics system at a Fortune 500 company.

Day 5: Strategy, Ethics, and the Future

• Module 9: Building an AI-Driven Analytics Culture

  • Change management: Getting business users to trust and act on AI insights.

  • Developing a roadmap for implementing AI-driven analytics in your organization.

• Module 10: Responsible AI in Analytics

  • Understanding and mitigating bias in automated insights.

  • Ensuring transparency and explainability in AI-driven decisions.

• Capstone Project: Participants work on a final project to design an end-to-end AI-driven analytics solution for a real-world business problem, presenting their architecture, model choice, and implementation plan.

• Conclusion: Next Steps and forging a path forward.