Crack your Data Science interview, one concept at a time.
167 interview questions across statistics, ML, SQL, Python, and system design — paired with YouTube explanations, AI-assisted practice, and real company case studies.
Browse by Topic
Every topic includes difficulty levels, worked hints, and full explanations — direct interview-ready content.
Getting Started
Roadmap, company expectations, storytelling with data, and confidence building.
Probability
Bayes' theorem, distributions, conditional probability, and expected value problems.
Python Coding
LeetCode-style problems + pandas, NumPy, and data manipulation tasks.
Descriptive Stats
Mean, median, variance, outliers, distributions, and data summarization.
Business Case Studies
Product sense, metric design, and feature impact analysis — Swiggy, Zomato, and more.
ML Breadth
Supervised, unsupervised, regularization, feature engineering, and model selection.
Inferential Stats
A/B testing, hypothesis testing, p-values, confidence intervals, and experiment design.
ML Depth
From-scratch derivations — logistic regression, gradient descent, backprop, transformers.
SQL
Real company problems — Meta, Google, Amazon, Netflix, Shopify, LinkedIn.
Take-Home Assignments
Full Jupyter notebook exercises used in real hiring pipelines.
ML System Design
End-to-end ML system design: recommendation engines, duplicate detection, ranking.
Latest Articles
Each post pairs with a YouTube video. Open any article in Claude for AI-assisted Q&A practice.
How LLMs Work: From Tokens to AI Agents
Before you can build an AI agent, you need to understand the engine inside it. A ground-up walkthrough of LLMs — tokenization, transformers, training, and the limits that make agents necessary.
How to Actually Use LLMs in Your Daily Life
Practical ways to use ChatGPT, Claude, and Gemini — from clearing doubts and building resumes to brainstorming ML projects and automating content creation. Plus one critical warning about when not to reach for them.
Open-Source vs Paid LLMs: Which One Should You Use?
GPT, Claude, and Gemini aren't the only options. A clear breakdown of open-source vs paid models — what they are, how they differ, and a decision framework for choosing the right one for your use case.
Your First LLM API Call: OpenAI, Streaming, and System Prompts
Stop using the chat UI — connect to LLMs directly in Python. Covers OpenAI and Groq setup, streaming vs non-streaming, picking the right model for cost, and controlling behavior with system prompts.
LLM Parameters Explained: Temperature, Max Tokens, and Context Window
Three knobs that control how your LLM behaves — and how much it costs you. Learn what Temperature, Max Tokens, and Context Window actually do, with real examples and code.
Learn by watching, practice by doing.
Deep dives on ML, statistics, and data science — watch the explanation, then come back here to practice or open it in Claude for a Q&A session.
Start your DS interview prep today.
167 questions, blog posts, YouTube videos, and AI-assisted learning — all in one place.