Explore the convergence of AI, business intelligence, and machine learning for predictive analytics in this advanced program. Participants will learn to apply machine learning algorithms for prediction and classification, integrate AI capabilities into Power BI, and automate analytics workflows. The curriculum covers regression, classification, clustering, and time series forecasting using tools accessible to business professionals (Excel, Power BI, and no‑code/low‑code platforms). Through hands‑on projects, attendees will build predictive models and embed them into interactive dashboards. This program is designed for data analysts, BI professionals, and managers who want to harness AI for forward‑looking insights.
Objectives
- Understand key machine learning concepts (supervised vs. unsupervised learning, overfitting)
- Build regression models to predict continuous outcomes (sales, demand)
- Apply classification algorithms to predict categories (churn, fraud)
- Use clustering for customer segmentation and pattern discovery
- Perform time series forecasting with exponential smoothing and ARIMA
- Integrate Python or R models with Power BI for deployment
- Leverage AI features in Power BI (Q&A, decomposition trees, key influencers)
- Automate data preparation and model retraining
- Communicate predictive insights to business stakeholders
Target Audience
- Data and business analysts
- Business intelligence professionals
- Financial and marketing analysts
- Managers who want to understand predictive analytics
- IT professionals supporting analytics
- Consultants in data science and BI
- Anyone seeking to add predictive analytics to their skillset
Methodology
- Interactive lectures on ML concepts
- Hands‑on modeling exercises using Excel, Power BI, and/or Python
- Case study applications of predictive analytics
- Dashboard integration workshops
- Peer review of predictive models
- Discussion of ethical considerations in AI/ML
- Action planning for applying predictive analytics in participants' work
