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📘 About This Course
AI-Powered Data Analytics & Business Intelligence is a complete, hands-on path from raw business data to executive decisions: 72 lessons of about 10 minutes each (roughly 12 hours), organised in ten modules and finished by an end-to-end capstone.
Everything happens in a real laboratory: an Ubuntu analytics VM running PostgreSQL, Adminer, Python and JupyterLab, Metabase and Apache Superset, plus a Windows client for Power BI Desktop and Tableau. A fictitious company dataset is used throughout, so every number you see can be recomputed and checked.
What you will build, step by step
- A reproducible Ubuntu analytics lab with Docker, PostgreSQL, Python and an LLM API connection.
- Solid SQL - joins without double counting, CASE and NULL rules, CTEs, window functions and period-over-period analysis - and a safe text-to-SQL assistant.
- Python and Pandas analysis, validated Matplotlib charts, data-quality profiling and a reproducible cleaning pipeline.
- A star-schema warehouse in PostgreSQL with independently validated revenue, margin, growth and customer KPIs.
- Executive dashboards in Power BI (Power Query, DAX, drill-through), Tableau, Metabase and Apache Superset.
- AI workflows that stay grounded: anomaly explanations, executive summaries and recommendations whose every figure traces back to verified metrics, with secrets, PII and prompt injection under control.
Every lesson shows a successful demonstration and a controlled failure - symptom, diagnosis, correction, retest - so you learn to debug, not just to follow along. Each lesson comes with its slide deck and the exact code run on screen.
What You'll Learn
Build a reproducible Ubuntu analytics lab with Docker, PostgreSQL, JupyterLab and an LLM API
Write trustworthy SQL: joins without double counting, CTEs, window functions and period-over-period analysis
Clean, model and validate data in Python and PostgreSQL, from profiling to a star-schema warehouse with tested KPIs
Design executive dashboards in Power BI, Tableau, Metabase and Apache Superset with filters, drill paths and governed metrics
Use LLMs safely for text-to-SQL, anomaly explanations and executive summaries grounded in verified metrics
Deliver a capstone project from raw data to a multi-platform dashboard, an AI assistant and an executive report
🛠️ Requirements
- Basic computer literacy and comfort with spreadsheets; no prior SQL or Python experience is required
- A computer able to run an Ubuntu VM (local or cloud) and, for the Power BI and Tableau modules, a Windows 10/11 client
- Access to an OpenAI-compatible LLM API for the AI-assisted lessons
👤 Who This Course is For
- Aspiring data analysts and BI developers who want a complete, practical path from SQL to dashboards
- Business, finance and operations professionals who want to answer their own questions with data and AI
- Analysts who already use Excel or one BI tool and want to add SQL, Python, open-source BI and safe AI workflows
53618 Students applied
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John Doe
Morbi nec nisi ante. Quisque lacus ligula, iaculis in elit et, interdum semper quam. Fusce in interdum tortor. Ut sollicitudin lectus dolor eget imperdiet libero pulvinar sit amet
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John Doe
Morbi nec nisi ante. Quisque lacus ligula, iaculis in elit et, interdum semper quam. Fusce in interdum tortor. Ut sollicitudin lectus dolor eget imperdiet libero pulvinar sit amet
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John Doe
Morbi nec nisi ante. Quisque lacus ligula, iaculis in elit et, interdum semper quam. Fusce in interdum tortor. Ut sollicitudin lectus dolor eget imperdiet libero pulvinar sit amet
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John Doe
Morbi nec nisi ante. Quisque lacus ligula, iaculis in elit et, interdum semper quam. Fusce in interdum tortor. Ut sollicitudin lectus dolor eget imperdiet libero pulvinar sit amet
Morbi nec nisi ante. Quisque lacus ligula, iaculis in elit et, interdum semper quam. Fusce in interdum tortor. Ut sollicitudin lectus dolor eget imperdiet libero pulvinar sit amet