Master Product Data Analytics

Contents

• I. Introduction
• 1. Welcome and Purpose of this Handbook
• 2. What to Expect: The Meta Data Science Role
• 3. Navigating the Meta Interview Process
• 4. How to Use This Handbook
• II. Foundational Knowledge & Skills
• 1. Statistics & Probability
• 1.1 Descriptive Statistics
• 1.2 Probability
• 1.3 Probability Distributions
• 1.4 Hypothesis Testing
• 1.5 Regression Analysis
• 1.6 Experimental Design
• 1.7 Bayesian Methods
• 2. SQL & Data Manipulation
• 2.1 Core SQL Syntax
• 2.2 Advanced SQL Techniques
• 2.3 Query Optimization
• 2.4 Data Cleaning with SQL
• 3. Programming (Python/R) for Data Analysis
• 3.1 Python Fundamentals for Data Science
• 3.2 Data Manipulation with Pandas
• 3.3 Numerical Computing with NumPy
• 3.4 Data Visualization
• 3.5 (Optional) Statistical Modeling Libraries
• III. Interview Preparation
• 1. Technical Skills Interview
• 1.1 SQL Deep Dive
• 1.2 Python/R for Data Manipulation
• 1.3 Mock Interview Practice
• 2. Analytical Execution Interview
• 2.1 Framework for Approaching Case Studies
• 2.2 Hypothesis Generation and Testing
• 2.3 Quantitative Analysis Techniques
• 2.4 Goal Setting and KPIs
• 2.5 Trade-off Analysis
• 2.6 Dealing with Ambiguity and Changing Requirements
• 2.7 Case Study Examples
• 2.8 Mock Interview Practice
• 3. Analytical Reasoning/Product Sense Interview
• 3.1 Developing Strong Product Sense
• 3.2 A Framework for Answering Product Sense Questions
• 3.3 Defining and Evaluating Metrics
• 3.4 Experimentation in Social Networks
• 3.5 Identifying and Mitigating Biases
• 3.6 Communicating Data-Driven Product Decisions
• 3.7 Example Product Sense Questions and Answers
• 3.8 Mock Interview Practice
• 3.9 Common Pitfalls to Avoid
• 4. Behavioral Interview
• 4.1 The STAR Method
• 4.2 Common Behavioral Interview Questions
• 4.3 Meta-Specific Behavioral Questions
• 4.4 Sample STAR Responses
• 4.5 Mock Interview Practice
• IV. Meta Specificity
• 1. Deep Dive into Meta's Interview Process
• 2. Meta's Data Science Culture
• 3. Internal Tools and Technologies
• 4. Product Deep Dives
• V. Resources and Practice
• 1. SQL Practice Platforms
• 2. Python/R Resources
• 3. Statistical Learning Resources
• 4. Product Sense Development
• 5. Mock Interview Platforms
• 6. Community Forums and Groups
• 7. A/B Testing and Experimentation
• 8. Business Analytics and Case Studies
• 9. YouTube and Social Media Channels
• 10. Company Blogs and Case Studies
• VI. Conclusion
• 1. Recap of Key Takeaways
• 2. Encouragement and Motivation
• 3. Final Tips for Success
• Appendix
• Glossary of Terms
• Cheatsheets (SQL, Pandas, etc.)