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Analyze Oculus App Engagement with SQL Queries

MetaData Scientist · Onsite
MediumData ScientistOnsiteData Manipulation (SQL/Python)

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This question evaluates proficiency in SQL-based data manipulation and aggregation, time-window filtering, percentage calculations, and the application of basic statistical hypothesis testing for comparing user engagement across categories.

AppUsage

+---------+--------+-----------+--------------+------------+ | user_id | app_id | category | minutes_spent| usage_date | +---------+--------+-----------+--------------+------------+ | 101 | 12 | Social | 45 | 2023-04-15 | | 102 | 17 | Game | 30 | 2023-04-16 | | 101 | 17 | Game | 20 | 2023-04-17 | | 103 | 22 | Fitness | 25 | 2023-04-17 | | 104 | 12 | Social | 60 | 2023-04-18 | +---------+--------+-----------+--------------+------------+

Scenario

Analyzing Oculus app engagement over the last 30 days

Question

Write SQL to find the most used app (total minutes) in the past 30 days. Write SQL to compute the percentage of time spent in each app category in the past 30 days. How would you test the hypothesis that users of the "Social" category are more regularly engaged than "Game" users?

Hints

Use date filters, aggregates, window functions; propose a statistical test.