Project Overview

In today’s data-driven world, analyzing numbers is only half the job — the real impact comes from communicating insights visually. This project demonstrates how to build a fully interactive data visualization dashboard using Python, Streamlit, and Plotly.

The dashboard allows users to filter, explore, and interact with real datasets (e.g., stock prices, COVID-19 trends, or sports performance). This not only highlights technical ability but also storytelling skills — a must-have for any data scientist or analyst.

✅ Recruiter Signal: “This candidate can turn complex datasets into insights that decision-makers understand.”

🛠️ Tech Stack

• Python – core language for data manipulation
• Pandas – handling and transforming datasets
• Streamlit – building the interactive web dashboard
• Plotly – creating interactive and responsive data visualizations
• Optional APIs – e.g., yfinance for stock market data or open COVID-19 datasets

💡 Features

• 📊 Interactive charts (line, bar, area plots)
• 🔍 Dynamic filters for data exploration
• ⚡ Real-time updates using APIs (stock or COVID data)
• 🎨 User-friendly UI with modern visuals
• 📈 Storytelling-focused design for decision-makers

📂 Project Structure

interactive_data_viz_dashboard/
│── app.py               # Main Streamlit app
│── requirements.txt      # Dependencies
│── data/                 # (Optional) Local datasets
│── README.md             # GitHub documentation

🚀 How to Run Locally

1. Clone the repo:

    

   git clone https://github.com/KingsleyOdume/interactive_data_viz_dashboard.git
   cd interactive_data_viz_dashboard
   

2. Install dependencies:

    

   pip install -r requirements.txt 

3. Run the dashboard:

    

   streamlit run app.py 

📌 Why This Project Matters

• Demonstrates technical skills in Python, data wrangling, and visualization.
• Highlights communication skills — turning raw data into interactive stories.
• Provides real-world impact by enabling decision-makers to explore insights themselves.

🌐 Recruiter-Friendly Signal

This project proves that you can bridge the gap between data and business stakeholders — a skill that hiring managers and recruiters actively seek in data science roles.

🔗 Project Links

• GitHub Repository: GitHub Repo Link
• Live Demo (Streamlit): Live Demo Link