Not tutorials that list functions. Each course builds the mental model of the machine first — then the API, then a hands-on project with deliberately messy data. Every concept gets an analogy, a hand-drawn diagram, runnable code, the gotchas, and a checkpoint quiz.
Start from nothing: values and types, strings, control flow, collections, functions, modules, files, exceptions, classes and testing.
Names vs objects, CPython internals, decorators, generators, descriptors, metaclasses, typing, the GIL and asyncio.
Big-O measured on real timings, every core structure drawn and implemented, and the patterns behind interview problems.
Engine-agnostic SQL taught by execution order — joins, subqueries, CTEs, window functions, modelling, indexes and EXPLAIN.
From the query down to the disk: server architecture, the clustered index, MVCC, locking, EXPLAIN, backups and replication.
ASGI and Pydantic v2 internals, dependency injection, async done right, security, testing, observability and deployment at senior depth.
One product's journey from a single server to a million users: every load balancer, cache, shard and region added only when something breaks.
Driver, executors, shuffles and Catalyst — why a job is slow and how to fix it, from your first SparkSession to production.
Everything around Spark: Unity Catalog, Auto Loader, declarative pipelines, jobs, Databricks SQL, MLflow, cost and CI/CD.
From the first request to production: the Responses API, streaming, structured outputs, tools, embeddings, retries, cost and agents.
How LLMs work, retrieval-augmented generation, agents and the Model Context Protocol, then OpenTelemetry, Langfuse and Grafana.
Every concept opens with a kitchen, a library or a sorting office — no jargon until the idea has landed.
What the interpreter, planner, storage layer or scheduler is actually doing, at senior-engineer depth.
Hand-drawn SVG that adapts to light and dark mode and stays readable on a phone.
Complete, copy-pasteable snippets. Where they could be executed (Python, DSA, SQLite, FastAPI, the AI projects), the outputs shown are real.
The mistakes that cost real teams real money, with the fix next to them.
Two to four questions per lesson with explanations, so you can prove you understood it.
| Course | Project | Runs with |
|---|---|---|
| Core Python | An expense-tracker CLI: a messy 600-row bank export cleaned, categorised and reported, as a package of small modules with 53 tests | Python 3.12+, standard library only |
| Advanced Python | A small task-queue library: decorators, descriptors, generators, asyncio, typing, pytest | Python 3.12+ |
| DSA | A route planner over 26 South Indian towns: Dijkstra/A* on your own heap, LRU cache, trie autocomplete, 15 tests | Python, no dependencies |
| SQL | 5-table retail database, 4,007 messy rows, 30 graded challenges — every solution verified by execution | SQLite (zero install), Postgres, MySQL |
| MySQL | Make a slow database fast with EXPLAIN ANALYZE, provoke a deadlock, point-in-time restore | MySQL 8 in Docker |
| FastAPI | A production-grade orders API: JWT roles, idempotency keys, ETags, live SSE status, a transactional outbox for signed webhooks, rate limits, 51 async tests | Python + SQLite (zero setup), PostgreSQL via Docker |
| System Design | Scale Lab: a URL shortener taken through six architecture stages under real load (index, click queue, cache, load balancer, shards), a live failover, and a capacity plan for a million users | Python, no services to install |
| PySpark | Retail analytics over CSV + JSON + Parquet: clean, join, window, write partitioned | Local Spark or Databricks |
| Databricks | End-to-end lakehouse: Auto Loader → declarative pipeline → jobs → dashboard | Databricks Free Edition |
| OpenAI SDK | A retail analyst assistant: SQL tool calling, structured answers, streaming, embeddings search, cost tracking, tests with a fake client | Python + OpenAI API (tests run offline) |
| AI Engineering | An observable docs copilot: RAG over this site, exposed as an MCP server, traced with OpenTelemetry → Grafana, evaluated in Langfuse | Python + Docker |
A fast single-page overview of the Spark mental model — a 20-minute refresher before an interview.
Eleven courses, each with runnable examples, quizzes and a hands-on project.
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