datarekha
01 Interactive · open source · 2026

Learn data science the way
you actually use it.

From Python syntax to production AI — every lesson runs in your browser, every concept has a quiz, every path leads to a job.

Start with Python Open playground
198
lessons
12
sections
6
learning paths
02 By role

Learning paths

Curated routes through the sections, ordered for a specific job.

All paths

Data Engineer

10–14 weeks · Intermediate

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
Start path

ML Engineer

12–18 weeks · Intermediate → Advanced

Build, train, and deploy models — the role 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
Start path

AI / LLM App Builder

8–12 weeks · Intermediate

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
Start path

Data Scientist

12–16 weeks · Beginner → Intermediate

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

  1. 1 Python
  2. 2 Pandas
  3. 3 Visualization
  4. 4 SQL
  5. 5 Math for ML
  6. +1 more
Start path

MLOps / Platform

10–14 weeks · Intermediate → Advanced

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

  1. 1 Python
  2. 2 MLOps
  3. 3 PySpark
Start path

Research-leaning

16+ weeks · Advanced

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

  1. 1 Math for ML
  2. 2 Deep Learning
  3. 3 Generative AI
Start path
03 By subject

All sections

Jump into any subject. Every lesson has runnable code, a quiz, and a cheatsheet.

Python

From syntax to production AI apps — the language for everything.

40 lessons · 6 chapters

NumPy

The numeric foundation of the entire data stack.

14 lessons · 3 chapters

Pandas

The de-facto tool for tabular data in Python.

13 lessons · 3 chapters

Visualization

Matplotlib, Seaborn — and how to make a plot worth showing.

8 lessons · 2 chapters

SQL

Analytics-grade SQL, warehouse dialects, and dbt.

18 lessons · 4 chapters

PySpark

Big data at scale — Hadoop → Spark internals → production.

18 lessons · 4 chapters

Math for ML

Linear algebra, calculus, and probability with code.

16 lessons · 3 chapters

Machine Learning

Trees beat neural nets on most tabular problems — and other truths.

14 lessons · 5 chapters

Deep Learning

PyTorch-first, transformer-heavy, no fluff.

13 lessons · 3 chapters

MLOps

Ship models, not notebooks.

10 lessons · 3 chapters

Generative AI

LLMs in practice — RAG, evals, fine-tuning, self-hosting.

14 lessons · 4 chapters

Agentic AI

LangChain, LangGraph, MS Agent Framework, Google ADK.

20 lessons · 5 chapters
04 What's inside

Built like the docs you wish your job had.

Runs in your browser

Python via Pyodide, SQL via SQLite-WASM. No install, no signup — just click Run.

Industry-focused

Every lesson answers "where would I see this in a real job?" No niche academic detours.

Quizzes & certificates

Auto-graded MCQs after every lesson, a practice library, and path certificates when you finish a track.

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