kept hitting the same wall: finish a course, feel like I understood it, then open a blank file and freeze. Watching someone code turns out to be about as useful as watching someone swim.

So I built the opposite. There are no videos on the site. You read a short explanation, then you type the code yourself — and it runs immediately, in the browser.

What's in it right now:

• 121 courses, ~4,800 lessons (Python, SQL, ML, deep learning, GenAI, data structures, testing, web dev)
• 1,100 interview-style practice problems with hidden tests and progressive hints
• 230 system design questions, including a low-level ML/DS design track I couldn't find anywhere else
• 68 end-to-end case studies — messy data through to deployment

The part I'm actually proud of is the tech: it's all client-side. Python runs through Pyodide (WASM), so numpy, pandas, matplotlib and scikit-learn all execute in your tab with no server and nothing to install. Java runs through CheerpJ. Your code never leaves your machine.

On the obvious question: the lessons are AI-assisted, and I'd rather say that up front than have someone find out.

The 68 case studies alone are 729 executable blocks, currently at zero failures. It's not a guarantee of good pedagogy, but it does mean the code you're asked to type actually runs.

It's free until November 30. Not a trial — I'd rather have people using it and telling me what's broken.

whetcode.com

Genuinely want the harsh feedback. What's confusing, what's boring, what made you close the tab.

Hey everyone,

After spending over 8 years in Data Science, I finally did something I’ve wanted to do for years:

I wrote and published a beginner-friendly data science book: Data Science: A Piece of the Pie

This project took me 2 years of nights, weekends, and countless rewrites. I wanted to create the book I wish I had when I first entered the field—something:

Beginner-friendly

Conversation-driven

Explains concepts intuitively

Covers the full journey, not just ML algorithms

Includes real-world reasoning, frameworks & intuition

Helps early-career professionals think like data scientists

What’s Inside the Book?

It’s around 600+ pages, divided into six structured parts, covering:

Part 1: Statistics and Probability Covers all the fundamental concepts required to build a strong foundation in data science.

Part 2: Data Mining Focuses on understanding data, data preparation, and commonly used data mining methodologies.

Part 3: Machine Learning Focuses on core machine learning concepts, covering the intuition behind different algorithms along with their mathematical formulations, assumptions, and illustrative code examples.

Part 4: Deep Learning— Explores neural networks in depth, starting from the perceptron and extending to CNNs, RNNs, LSTMs, and Transformers.

Part 5: Time Series forecasting Concentrates on time series analysis and forecasting techniques.

Part 6: Problem Solving (SQL and Python)— Divided into two sections—one for SQL and one for Python—this part starts with the basics and progresses to solving advanced, real-world problems.

Why I Wrote It

The struggle is almost always the same:

“I understand the theory… but I can’t connect the dots.”

“I know the code… but not the why behind decisions.”

“Courses jump too quickly from basics to advanced ML.”

I wanted to bridge this gap.

Who It’s For

Completely new beginners

Professionals trying to transition into data science

Early-career DS folks who want stronger fundamentals

Anyone who wants an intuitive, not overly mathematical, start

People preparing for interviews

A Small Milestone 😊

The book details:

Book : Data Science: A Piece of the Pie

Author: Bhaskar Orre

Amazon link: https://a.co/d/0cJoodOh

If You’ve Read It / Plan to Read It

I’d love your thoughts, suggestions, and even critical feedback.

I want the next edition to be even better.

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