📊 Master Product Data Analytics

Your comprehensive guide to data science interview preparation, analytical skills, and product analytics mastery

Statistics & Probability SQL & Data Analysis Python Product Sense
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Statistics & Probability

Master statistical concepts, probability theory, hypothesis testing, and experimental design fundamentals.

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SQL & Data Manipulation

Learn SQL for data processing and analysis, including advanced techniques, window functions, and optimization.

Browse SQL

Python for Data Analysis

Dive into Python with a focus on Pandas, NumPy, data visualization, and statistical analysis libraries.

Start Python

Analytical Reasoning

Develop skills to tackle product-related data questions, define metrics, and design experiments effectively.

Explore

Analytical Execution

Master the execution of complex analytical problems with real-world case studies and frameworks.

Practice

Behavioral Interview

Prepare for behavioral questions with the STAR method and real examples from tech interviews.

Prepare

Meta Specificity

Gain insights into Meta's interview process, culture, and specific requirements for data science roles.

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Resources & Practice

Explore a curated list of resources, practice problems, and continuous learning materials.

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100+
Practice Problems
8
Core Topics
50+
SQL Examples
∞
Learning Potential

Ready to Ace Your Data Science Interview?

Join thousands of aspiring data scientists who use this comprehensive guide to prepare for analytical interviews at top tech companies.

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📚 What You’ll Learn

This handbook is designed as a living knowledge base that provides comprehensive preparation for data science analytical interviews:

🎯 Comprehensive Coverage

  • Foundational Knowledge: Statistics, probability, SQL, and Python fundamentals
  • Interview Frameworks: Structured approaches to solving analytical problems
  • Product Sense: How to think about metrics, experiments, and product decisions
  • Real-World Practice: Case studies and examples from actual interviews

💻 Hands-On Learning

Every section includes:

  • Concept Explanations: Clear, practical descriptions of key topics
  • Code Examples: Working SQL and Python code you can practice with
  • Practice Problems: Questions to test your understanding
  • Tips & Tricks: Insider knowledge from industry professionals

🚀 Getting Started

  1. Begin with the Introduction to understand the interview process
  2. Review Foundational Knowledge for core concepts
  3. Practice with SQL Examples to sharpen your skills
  4. Work through Case Studies to apply your learning
  5. Prepare for Behavioral Questions with the STAR method

📈 Repository Stats

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🤝 Contributing

This handbook is a collaborative effort, and contributions are welcome! If you have suggestions, find errors, or want to add more content, please feel free to open an issue or submit a pull request.

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