Operational Analytics involves analyzing a company's end-to-end operations to identify areas for improvement. As a Lead Data Analyst, your goal is to derive meaningful insights from data, investigate metric spikes, and support decision-making for various teams like operations, support, and marketing.
In this project, you will work with SQL to analyze real-world datasets, answer key business questions, and provide valuable data-driven insights.
You will be working with the job_data table, which contains:
- job_id: Unique identifier of jobs
- actor_id: Unique identifier of the actor
- event: The type of event (decision/skip/transfer)
- language: Language of the content
- time_spent: Time spent reviewing the job (in seconds)
- org: Organization of the actor
- ds: Date (YYYY/MM/DD, stored as text)
✅ Jobs Reviewed Over Time: Calculate jobs reviewed per hour for each day in November 2020.
✅ Throughput Analysis: Compute the 7-day rolling average of throughput (events per second) & compare it with daily metrics.
✅ Language Share Analysis: Determine the percentage share of each language in the last 30 days.
✅ Duplicate Rows Detection: Identify duplicate rows in the dataset.
You will analyze data from three tables:
- users: User account details
- events: User activity logs (e.g., logins, messages, searches)
- email_events: Email-related interactions
✅ Weekly User Engagement: Measure user activeness on a weekly basis.
✅ User Growth Analysis: Analyze user growth trends over time.
✅ Weekly Retention Analysis: Calculate user retention based on sign-up cohorts.
✅ Weekly Engagement Per Device: Track weekly engagement per device type.
✅ Email Engagement Analysis: Evaluate how users interact with emails.
Each SQL query should be accompanied by insights and interpretations of the results obtained.
🔹 SQL (Advanced Queries)
🔹 MySQL Workbench
🔹 Data Analytics & Business Insights
For any questions or discussions, feel free to reach out or open an issue. Happy analyzing! 🎯