Career Services User Behavior Analytics Dashboard

This project demonstrates a complete, end-to-end data analytics pipeline. It transforms raw data into a clean, structured format and visualizes it on a live, interactive dashboard. The solution provides a clear view into user behavior and engagement, enabling career services departments to make data-driven decisions.

Core Technologies

This project was built using a modern data stack, showcasing my expertise in the following technologies:

• dbt (data build tool): Used to transform raw user data into clean, structured datasets (fact_user_activity and dim_users). This demonstrates proficiency in data modeling and SQL-based transformations.
• Python: The primary language used for the backend of the application, data processing, and orchestrating the dashboard logic.
• Streamlit: A powerful Python framework for building and sharing interactive data applications. The dashboard allows for real-time visualization of key performance indicators.
• MySQL: Serves as the data warehouse where all transformed data is stored, making it accessible for analysis and visualization.
• Git & GitHub: Used for version control and collaborative development, ensuring the project is organized and professionally managed.

Key Visualizations & Insights

The dashboard provides a clear view into user activity and platform health, including:

• Daily Active Users (DAU): A line chart that tracks user engagement over time.

• User Acquisition by Country: A bar chart visualizing the geographic distribution of new users.

• Feature Adoption Rates: A bar chart that measures how many users have engaged with key features such as resume uploads, job applications, and course enrollment.

  

  

  

Project Structure

The project is organized to follow industry-standard best practices for analytics engineering:

• my_dbt_project/
  • app.py: The Python application that serves the interactive Streamlit dashboard.
  • dbt_project.yml: The dbt project configuration file.
  • models/: Contains all dbt SQL models for data transformation.
  • requirements.txt: Lists all Python dependencies required to run the dashboard.
  • .env: A local file for storing sensitive credentials (not pushed to GitHub).
  • .gitignore: Prevents sensitive files and build artifacts from being committed.

How to Run This Project

To get this project running on your local machine, follow these steps:

1. Set Up MySQL: Ensure you have a running MySQL server.
2. Clone the Repository:

   git clone [https://github.com/akeDataAnalyst/career-services-dashboard.git](https://github.com/akeDataAnalyst/career-services-dashboard.git)
   cd career-services-dashboard

3. Set Up the Python Environment:

   python -m venv venv
   venv\Scripts\activate  # On macOS/Linux, use: source venv/bin/activate
   pip install -r requirements.txt

4. Configure Credentials: Create a file named .env in the root directory and add your database credentials.

   DB_HOST=localhost
   DB_USER=your_username
   DB_PASSWORD=your_password
   DB_DATABASE=your_database_name
   

5. Run dbt Models: Execute the dbt models to transform the data and load it into your database.

   dbt run

6. Run the Dashboard: Start the Streamlit application.

   streamlit run app.py

   The dashboard will be available in your browser at http://localhost:8501.