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Data science & AI,
taught from scratch.

The complete path from your first line of Python to production AI — become a data analyst, data scientist, or AI engineer.

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Start with Python — free

662 lessons 650+ interview Q&A 20 courses

Jump into a course

Python 41 lessons SQL 27 lessons Machine Learning 33 lessons Deep Learning 28 lessons Generative AI 63 lessons Agentic AI 71 lessons GATE DA 122 lessons

01 Where do you want to land?

Pick the job. We’ll sequence the courses.

The right courses in the right order, taught from scratch. Three headline tracks here; 9 in all.

Best first job in data

Data Analyst

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

SQL Python Pandas Storytelling with Visualisation Business Analytics

114 lessons · 8–12 weeks See the path

The classic path

Data Scientist

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

Python Pandas Storytelling with Visualisation SQL Math for ML Machine Learning

163 lessons · 12–16 weeks See the path

The 2026 frontier

AI Engineer

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

Python Machine Learning Deep Learning NLP & Transformers Generative AI Agentic AI MLOps

308 lessons · 16–22 weeks See the path

See all 9 career paths

02 The full curriculum

20 courses, 662 lessons. All of it free.

In learning order, grouped the way the field actually fits together. Each card opens its first lesson straight away.

Foundation

Python From syntax to production AI apps — the language for everything. 41 SQL Analytics-grade SQL, warehouse dialects, and dbt. 27 Data Structures & Algorithms The algorithmic core every data & AI engineer needs — in Python. 32 Git Version control the way every team actually uses it — branch, merge, recover. 15 Command Line The terminal skills that make every other tool faster. 14

Data & analytics

NumPy The numeric foundation of the entire data stack. 14 Pandas The de-facto tool for tabular data in Python. 13 Storytelling with Visualisation Matplotlib, Seaborn — and how to turn a chart into a story that lands. 12 Business Analytics Turn data into decisions — the analytics an MBA pays for. 21 Math for ML Linear algebra, calculus, and probability with code. 37

Machine learning

Machine Learning Trees beat neural nets on most tabular problems — and other truths. 33 Time Series Forecast what comes next — ARIMA, SARIMA, VAR, Prophet, done right. 14 Recommender Systems How Netflix, Spotify & Amazon decide what you see next. 11 Deep Learning Neural networks from scratch — the core, no fluff. 28 NLP & Transformers From text features to the transformer — language modeling end to end. 44

AI & production

Generative AI LLMs in practice — RAG, evals, fine-tuning, self-hosting. 63 Agentic AI Build agents on the LLM — patterns, tools, MCP, multi-agent, production. 71 MLOps Ship models, not notebooks. 28 PySpark Big data at scale — Hadoop → Spark internals → production. 22 GATE DA Crack GATE Data Science & AI — every topic, ground-up to exam level. 122

03 Start with a classic

The lessons readers open first.

Or jump to today’s pick — Eigenvalues & eigenvectors

Python 10 min Functions Read the lesson SQL 10 min Window functions Read the lesson Machine Learning 7 min Bias–variance & learning curves Read the lesson Deep Learning 9 min Backprop by hand Read the lesson Pandas 10 min GroupBy Read the lesson Agentic AI 5 min What agentic AI means Read the lesson

GATE Data Science & AI paper

Preparing for GATE DA? The whole syllabus is here.

122 exam-grade lessons covering every section of the paper — probability, linear algebra, ML, databases, and general aptitude — with solved problems in every lesson.

Start preparing

04 Land the job

Prep & reference, when it counts.

Interview rounds, a quick definition, the syntax you forgot — the fast lane next to the courses.

650+ questions · role-filtered

Interview Q&A

Real questions across SQL, ML, Python, statistics & AI — each with a worked answer and the trap to avoid. Filter by the role you're chasing.

Start practising

Glossary

245 data & AI terms in plain English — from overfitting to RAG — each linked to a lesson.

Cheat sheets

6 dense one-pagers for Python, SQL, Pandas, NumPy, Git & scikit-learn — the syntax you actually reach for.

05 From the blog

Fresh from the desk.

Python · Aug 15

apply() is a for loop in disguise

Rewriting a Python loop as .apply() makes the code shorter and almost never makes it faster, because .apply() is the same loop with a nicer costume. Here is the ladder that does make it faster.

Agents · Aug 15

MCP went stateless — what server authors have to change

The 2026-07-28 revision deleted the initialize handshake and the session header, replaced server-initiated requests with a retry pattern, and put Roots, Sampling and Logging on a twelve-month clock.

Statistics · Aug 15

Simpson's paradox is already in your dashboard

The new checkout converts better on desktop and better on mobile, and worse overall. Both statements are arithmetically correct, and one of them is about to be presented to your leadership team.

Data Engineering · Aug 15

The small files problem eats your read throughput

A 400 GB table that takes forty minutes to scan is not a 400 GB problem. It is a 1.2-million-file problem, and every one of those files charges the same fixed toll.

From scratch, with the math

Every idea is built up from first principles — derivations traced step by step, then turned into working code you can follow line by line.

Every output is real

Code samples ship with their actual verified output — run before publishing, never guessed. What you see is what the code does.

One clear idea at a time

Short lessons in a deliberate order — each one hands off to the next, so the whole field clicks together instead of piling up.

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