Master Product Data Analytics

Your Guide To Data Analytics Mastery

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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
    • 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.)
📊

Statistics and Probability

Master statistical concepts and probability theory.

💻

SQL & Data Manipulation

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

🐍

Python for Data Analysis

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

🤔

Analytical Reasoning/Product Sense

Develop skills to tackle product-related data questions.

📈

Analytical Execution

Master the execution of complex analytical problems.

🤝

Behavioral Interview

Prepare for behavioral questions with the STAR method.

🏢

Meta Specificity

Gain insights into Meta's interview process and culture.

📚

Resources and Practice

Explore a curated list of resources for continuous learning.

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