The systems behindmodern AI software.

From your first triple to production-grade SHACL shapes — a hands-on path through the four pillars of the Semantic Web.

Model your domain. Defend its boundaries. Compose services that stay shaped like the business — even when the business changes.

Think in patterns, not joins. Learn the property-graph model Neo4j uses, write Cypher that reads like an ASCII drawing of your data, and ship it through indexes, query plans and the official drivers.

Learn to tell apart structured, semi-structured and unstructured data; pick the right model; install quality, lineage and governance; and ship the result on lake / warehouse / lakehouse / mesh — without losing sight of the people and policies.

Learn what an ontology actually is, why RDFS isn't enough, how OWL and Description Logic let reasoners do real work, when SHACL beats OWL, and how to ship a versioned, FAIR-published ontology your downstream consumers can trust.

Walk the entire DMBOK Wheel — Governance, Architecture, Modeling, Storage, Security, Integration, Documents, MDM, Warehousing, Metadata, Quality — with strong analogies and operational deep dives. Finish with a maturity assessment you can run on your own platform Monday morning.

You learned how to build ontologies in Ontology Engineering. This track introduces the ones the world already agreed on — FIBO, SNOMED CT, IDMP, OSDU, GS1, the OBO Foundry, schema.org and ISO 15926: how each is structured, governed, licensed, and aligned.

Stop polling, start streaming. Learn the partitioned commit log that powers event-driven systems at scale — and the operational muscle you need to actually run one in production.

Stop losing experiments to notebook chaos. Learn the MLflow workflow that grown-up ML teams use: track everything, package your code, version your models, and serve them through a registry that you (and audit) can trust.

ML models that ship are 5% modelling and 95% systems. Learn the patterns, hand-offs and feedback loops that take a model from a lucky notebook to a service your business can rely on — without drowning in vendor speak.

Most 'data' careers drown in tools. This track gives you the mental models — batch vs streaming, OLTP vs OLAP, row vs columnar, push vs pull orchestration — then layers tools (Spark, Airflow, dbt, Iceberg, Flink) on top so you can swap vendors without losing your footing.

You learned the vendor-neutral fundamentals in Data Engineering. Now meet the platforms that implement them. This track decodes how Snowflake, Databricks and BigQuery each separate storage from compute, how they bill, where they shine, and how to pick — and migrate between — them without religion.

Every team writes its own SQL for 'revenue' and ships five different numbers. This track teaches the layer that fixes it: metric trees, dbt SL, Cube, LookML, and the bridge to ontology-driven analytics — one definition for BI, apps, reverse-ETL and LLM agents.