Finale

Namaste Power BI

Episode 47

IPL Project: Predictive Analytics Intro

⏱ 45:00

Episode Summary

Finale Episode 5 — adding predictive intelligence. Built-in forecasting (exponential smoothing), Python/R script visuals for custom analytics, and Azure ML model integration via Dataflows.

This is Episode 47 of the free Namaste Power BI course on DevWithData — covering ipl project: predictive analytics intro. Watch the video above, then pass the MCQ checkpoint to unlock the next episode and update your Data Readiness Index score.

Key Takeaways

→Built-in Forecast uses exponential smoothing in line charts
→Python/R visuals run scripts and render matplotlib/ggplot output
→Azure ML models are invoked via Power Query in Dataflows
→Forecasting ≠ Prediction — different tools, different purposes

⚠ Common Interview Pitfalls

•Relying on Python visuals in production (requires local runtime)
•Confusing built-in forecasting with actual ML prediction
•Not understanding that Python visuals don't render fully in Power BI Service without gateway config

Official Reference

Deepen your understanding with official Microsoft documentation.

PL-300 Official Training Course

Microsoft's official instructor-led training for the Power BI Data Analyst certification.

https://learn.microsoft.com/en-us/training/courses/pl-300t00

Power BI Documentation

Complete reference for Power BI Desktop, Power BI Service, and Power BI Embedded.

https://learn.microsoft.com/en-us/power-bi/

Content on DevWithData is original and inspired by official Microsoft resources. The links above point directly to Microsoft Learn — always the authoritative source.

← Ep 46: IPL Project: Live Deployment

Ep 48: Enterprise Performance Engineering →

Episode Checkpoint

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