A structured interview preparation repository for Data Scientist, Product Data Scientist, Analytics, Applied Scientist, and ML-adjacent roles.
This repository brings together SQL practice, Python practice for data-focused problems, case study frameworks, experimentation notes, probability/statistics guides, and interview preparation templates in one place.
The goal is to build a practical and reusable preparation system for data science interviews.
datascience-interview-prep/
│
├── README.md
│
├── coding-practice/
│ ├── sqlzoo-solutions.md
│ ├── leetcode-sql-solutions.md
│ ├── leetcode-python-solutions.md
│ └── codingbat-python-solutions.md
│
├── cheat-sheets/
│ ├── sql-cheat-sheet.md
│ ├── python-cheat-sheet.md
│ ├── pandas-cheat-sheet.md
│ └── statistics-cheat-sheet.md
│
├── case-study-templates/
│ ├── 1-decide-should-we-launch-or-not.md
│ ├── 2-measure-success.md
│ ├── 3-diagnose-why-did-this-metric-change.md
│ └── 4-improve-how-would-you-improve-this-product-feature-metric.md
│
├── experimentation/
│ └── experimentation-study-guide.md
│
├── probability-statistics/
│ └── probability-distributions-study-guide.md
│
├── machine-learning/
│ └── ml-interview-notes.md
│
└── resources/
└── useful-links.md
Completed SQLZoo solutions for building SQL fundamentals through structured practice.
LeetCode SQL solutions for data science and analytics interview preparation.
LeetCode Python solutions focused on dataset-style, pandas-style, and data manipulation problems rather than traditional data structures and algorithms.
CodingBat Python solutions for programming warm-up, basic logic, and Python fluency.
A practical SQL reference for data science and analytics interviews.
A quick Python reference for coding, data manipulation, and interview preparation.
A practical pandas reference for data manipulation and dataset-style interview questions.
A concise reference for common statistics concepts used in data science interviews.
These templates are designed for Product Data Scientist, Analytics, Growth, Experimentation, and Business/Product Sense interviews.
Framework for launch decision case studies where experiment results, business impact, and metric tradeoffs need to be evaluated.
Framework for defining success metrics, guardrail metrics, diagnostic metrics, and product measurement strategy.
Framework for debugging metric movements, identifying root causes, and structuring analytical investigations.
Framework for product improvement, metric growth, opportunity sizing, and prioritization case studies.
A study guide for A/B testing, experiment design, metric interpretation, and launch decision interviews.
A practical guide to probability distributions and related concepts commonly used in data science interviews.
A practical reference for common machine learning concepts, model evaluation, feature engineering, and applied ML interview questions.
Future additions may include practice from the following platforms and resources:
- DataLemur SQL
- StrataScratch SQL
- HackerRank SQL
- HackerRank Python
- Mode Analytics SQL Tutorial
- Interview Query
- Kaggle Learn
- Blind 75
- NeetCode 150
- CodeSignal
- Product Data Science Question Bank
- Experimentation Question Bank
- Metrics and Product Sense Question Bank
- Machine Learning Question Bank
The goal of this repository is to create a focused and reusable interview preparation system across:
- SQL
- Python for data science
- Data manipulation
- Product analytics
- Experimentation
- Probability and statistics
- Machine learning
- Data science case studies
- Metric design
- Root-cause analysis
- Launch decision-making
This repository is a work in progress and will continue to evolve as I practice more problems and refine my interview frameworks.