COVID-19 Data Visualization Dashboard
Dynamic COVID-19 data dashboard built with Google Looker Studio and Python
- View Dashboard: Link to Dashboard
- View GitHub Repository: Link to Repository
- View Medium Article:Link to Article
Tools & Technologies
Technologies: Python · pandas · Google Looker Studio · Google Sheets · CDC COVID Dataset · Jupyter Notebook
Concepts: Data Visualization · Dashboard Design · Data Pipeline · Real-time Analytics
Overview
This project presents a dynamic COVID-19 data dashboard built with Google Looker Studio, powered by a CDC dataset processed in Python. The goal is to enable interactive visualization of COVID-19 trends by integrating real-world data into a streamlined and easily updateable pipeline.
Objectives
- Retrieve and clean raw COVID-19 data from CDC’s open API.
- Prepare time-series data with proper formatting, missing value handling, and sorting.
- Connect the cleaned dataset to Looker Studio via Google Sheets to support real-time visual exploration.
Methodology
Data Source: U.S. CDC open dataset containing daily case, death, and hospitalization metrics.
Data Cleaning in Python:
- Parsed dates and sorted records chronologically.
- Handled missing values and ensured consistent column formatting.
- Exported the processed data to Google Sheets for visualization.
Dashboard Design:
- Built an interactive dashboard in Looker Studio.
- Included key indicators such as daily cases, cumulative deaths, and moving averages.
- Implemented filters for state selection, time range, and metric comparison.
Results
The resulting dashboard enables users to explore U.S. COVID-19 statistics in a clear and flexible format, powered by a lightweight yet automated Python-to-Google Sheets pipeline.