Data Science

Interview-focused preparation for data science roles. Each topic is ordered lecture first, then exercises from easy → hard, and the track itself moves from foundations to applied ML and product sense.

Learning path

1. Interview soft skills

• Behavioral interview — STAR stories, culture fit, practice randomizer

2. Probability

• Probability — Bayes, counting, distributions, Markov chains
• Easy problems
• Medium problems
• Hard problems

3. Statistics

• Statistics — LLN, CLT, hypothesis tests, CIs, MLE
• Easy problems
• Medium problems
• Hard problems

4. SQL & data systems

• SQL — joins, CTEs, windows
• Schema Diagram Lab — ER diagrams and foreign keys
• Databases and Systems — keys, CAP/ACID, scale, NoSQL
• SQL Easy
• SQL Medium

5. Coding interviews

• Approach & Big-O
• Data structures
• Algorithms
• Sorting algorithms
• Coding Easy
• Coding Medium
• Coding Hard

6. Machine learning

• The Math Behind ML
• Linear Regression
• Classification Fundamentals
• Classification Models
• Random Forests & Boosting
• Dimensionality Reduction
• Clustering
• Neural Networks
• Reinforcement Learning
• End-to-End ML Workflow
• ML Easy
• ML Medium
• ML Hard

7. Product sense

• Product Sense — metrics, diagnosis, funnels, A/B
• Product sense problems

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Work top → bottom in the sidebar. Within a topic, finish the lecture before jumping into the problem sets.