Follow a guided track.

Answer real business questions with data — SQL, pandas, charts that tell the story, and the analytics sense to turn numbers into decisions.

1. 1 SQL (full track)
2. 2 Python (core)
3. 3 Pandas
4. 4 Storytelling with Visualisation
5. 5 Business Analytics

Python + SQL + Spark, with the warehouse knowledge to glue them together.

1. 1 Python (Core, OOP, Engineering)
2. 2 SQL (full track)
3. 3 Pandas
4. 4 PySpark
5. 5 MLOps basics

Build, train, and deploy models — the skillset most teams actually need.

1. 1 Python
2. 2 NumPy
3. 3 Pandas
4. 4 Math for ML
5. 5 Machine Learning
6. +2 more

Ship LLM-powered products. Async Python, RAG, agents, evals.

1. 1 Python (async, Pydantic, FastAPI)
2. 2 SQL (essentials)
3. 3 Generative AI
4. 4 Agentic AI

From SQL to statistical rigor to communicating results that change decisions.

1. 1 Python
2. 2 Pandas
3. 3 Storytelling with Visualisation
4. 4 SQL
5. 5 Math for ML
6. +1 more

Build AI products from first principles — neural nets, transformers, LLMs, RAG and agents, all the way to shipping them in production.

1. 1 Python
2. 2 Machine Learning
3. 3 Deep Learning
4. 4 NLP & Transformers
5. 5 Generative AI
6. +2 more

Turn data into decisions leaders act on — unit economics, funnels, forecasting, and the story that lands.

1. 1 Business Analytics
2. 2 SQL (essentials)
3. 3 Storytelling with Visualisation
4. 4 Stats: probability & A/B testing

Docker, CI/CD, MLflow, serving, Kubernetes. The glue that runs production ML.

1. 1 Python
2. 2 MLOps
3. 3 PySpark

Math, PyTorch, transformers — for people building the next architecture.

1. 1 Math for ML
2. 2 Deep Learning
3. 3 Generative AI