Feed Content Quality: Engagement and Insights Impact

Company:LinkedIn
Dawn AI

Welcome to Interview Master! 👋

I'm here to help you practice Feed Content Quality: Engagement and Insights Impact - a Data interview question from LinkedIn.

Problem Overview

As a Product Analyst on the LinkedIn Feed team, you are tasked with analyzing how different content types impact user engagement on the platform. Your team is focused on understanding which types of content consistently drive higher engagement scores. The ultimate goal is to leverage these insights to enhance content recommendation strategies and improve user experience on the feed.

This problem will test your SQL or Python skills in:

  • Writing and optimizing code
  • Data analysis and manipulation
  • Real-world database scenarios used in LinkedIn interviews

What You'll Learn

By solving this LinkedIn interview question, you'll gain experience with:

  • Writing efficient SQL queries or Python code for production databases
  • Understanding complex data relationships and schema design
  • Applying SQL or Python (your choice!) in a LinkedIn-style technical interview setting
  • Problem-solving techniques used by data scientists and analysts at top tech companies

Getting Started

Use the code editor on the right to:

  1. Explore the database schema and table structures
  2. Write and test your SQL or Python (your choice!) queries in a real coding environment
  3. Get instant feedback on your query results
  4. Learn from hints and detailed explanations

Ready to practice this LinkedIn Data challenge? Let's dive in! 🚀

I'm ready to work on this LinkedIn interview problem. Can you break down the requirements for me?

Dawn AI

Excellent! Let's break down "Feed Content Quality: Engagement and Insights Impact" step by step.

Problem Analysis

Company: LinkedIn
Problem Type: Data Science Interview Question
Skill Level: Professional interview preparation

Key Requirements

As a Product Analyst on the LinkedIn Feed team, you are tasked with analyzing how different content types impact user engagement on the platform. Your team is focused on understanding which types of content consistently drive higher engagement scores. The ultimate goal is to leverage these insights to enhance content recommendation strategies and improve user experience on the feed.

Approach Strategy

For this LinkedIn interview question, consider:

  1. Data Exploration: Start by examining the table schemas to understand the data relationships
  2. Query Planning: Think about which tables you'll need to JOIN and what conditions to apply
  3. Code Optimization: Consider performance implications for large datasets (important for LinkedIn scale)
  4. Edge Cases: Think about NULL values, duplicate data, and boundary conditions

Next Steps

  1. Click on the "Schema" tab in the code editor to examine the table structures
  2. Review the sample data to understand the data patterns
  3. Start with a basic SELECT statement and build complexity gradually
  4. Test your query and iterate based on the results

This type of problem is commonly asked in LinkedIn technical interviews for data analyst, data scientist, and software engineer positions. Take your time to understand the problem thoroughly before writing your solution.

Ready to start coding? 💻

Interview Master can make mistakes. If you encounter a mistake

Current Question

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Identify which content formats performed best in July 2024 by reporting their average engagement scores, but only list the formats that achieved an average score of 50 or higher.

Tables

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fct_user_engagement (engagement_id, content_id, user_id, engagement_score, engagement_date)
dim_content (content_id, content_type, publish_date)
Ready to start practicing?