Operation-Analytics-and-Investigating-Metric-Spike

Project Description 📊

Welcome to my data analysis project focused on various aspects of user engagement and data analysis. In this project, we delve into a dataset containing information related to user activities, job reviews, email events, and more. Here's a quick overview of the tasks I tackled and the insights I gained:

Jobs Reviewed Over Time 🕒

• Objective: Calculate the number of jobs reviewed per hour for each day in November 2020.
• Task: I wrote an SQL query to aggregate and display the job reviews per hour, offering a detailed view of user activity throughout November 2020.

Throughput Analysis 📈

• Objective: Calculate the 7-day rolling average of throughput (number of events per second).
• Task: I crafted an SQL query to calculate the 7-day rolling average of throughput, providing a more stable metric for assessing system performance. In the accompanying explanation, I shared my preference for this metric and explained why.

Language Share Analysis 🌐

• Objective: Calculate the percentage share of each language in the last 30 days.
• Task: I designed an SQL query to determine the percentage share of each language in the dataset over the last 30 days, offering insights into language popularity.

Duplicate Rows Detection 📋

• Objective: Identify duplicate rows in the data.
• Task: My SQL query pinpointed duplicate rows in the job data, which helps ensure data integrity.

Case Study 2: Investigating Metric Spike 🚀

In this case study, I worked with three tables: users, events, and email_events.

Weekly User Engagement 📅

• Objective: Measure the activeness of users on a weekly basis.
• Task: I provided an SQL query to calculate and visualize user engagement on a weekly basis.

User Growth Analysis 📈

• Objective: Analyze the growth of users over time for a product.
• Task: I wrote an SQL query to evaluate user growth trends for the product, offering valuable insights into its expansion.

Weekly Retention Analysis 📊

• Objective: Analyze the retention of users on a weekly basis after signing up for a product.
• Task: My SQL query quantified the weekly retention of users, allowing a closer look at their long-term engagement.

Weekly Engagement Per Device 📱

• Objective: Measure the activeness of users on a weekly basis per device.
• Task: I created an SQL query to calculate user engagement based on the device they used, facilitating insights into device-specific trends.

Email Engagement Analysis 📧

• Objective: Analyze how users are engaging with the email service.
• Task: I formulated an SQL query to provide metrics for email engagement, offering a detailed view of user interactions with this service.

Each task was completed with the aim of extracting valuable insights from the data, and the results and explanations are available in the respective sections above.

Explore the GitHub repository to access the SQL queries, insights, and findings from this data analysis project. Your feedback and contributions are most welcome! 👍🏼📈🔍