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This repository contains SQL queries and analysis scripts for operational analytics and investigating metric spikes. The project focuses on tasks such as throughput analysis, language share analysis, duplicate row detection, and investigating user engagement metrics.

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Instagram User Analytics Project using SQL by Raj Rathod

Description

Welcome to the Instagram User Analytics project! As a data analyst collaborating with Instagram's product team, your role is crucial in deciphering user interactions and engagement with the app to provide actionable insights. These insights will empower various teams within Instagram to make informed decisions, leading to the app's growth and enhancement of user experience.

SQL Tasks

A) Marketing Analysis:

  1. Loyal User Reward: Identify the five oldest users on Instagram.
  2. Inactive User Engagement: Identify users who have never posted a single photo on Instagram.
  3. Contest Winner Declaration: Determine the winner of a contest based on the most likes on a single photo.
  4. Hashtag Research: Identify the top five most commonly used hashtags on the platform.
  5. Ad Campaign Launch: Determine the best day of the week to launch ads.

B) Investor Metrics:

  1. User Engagement: Calculate the average number of posts per user and assess overall user engagement.
  2. Bots & Fake Accounts: Identify potential bots by detecting users who have liked every single photo on the site.

How to Proceed

  1. Database Creation: Use the provided commands to create the necessary database for the project.
  2. Perform Analysis: Utilize SQL and MySQL Workbench to execute queries and extract insights from the data.
  3. Report Preparation: Compile your findings into a report in PDF or PowerPoint format, including SQL queries and outputs.

Project Details

  • Tech-Stack Used: MySQL Workbench was utilized for its robust SQL querying capabilities and ease of use.
  • Insights: The analysis uncovered key insights into user behavior and engagement, guiding strategic decisions for Instagram's growth.
  • Results: The project successfully provided actionable insights for various stakeholders, contributing to the app's evolution and user experience enhancement.

Drive Links

Feedback and Collaboration

Your feedback is invaluable! Feel free to connect via LinkedIn or email for any inquiries, collaborations, or feedback on the project.


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This repository contains SQL queries and analysis scripts for operational analytics and investigating metric spikes. The project focuses on tasks such as throughput analysis, language share analysis, duplicate row detection, and investigating user engagement metrics.

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