CORD-19 Research Data Explorer This project is a beginner-friendly data analysis and visualization assignment that explores the CORD-19 research dataset. It uses Python libraries such as pandas for data manipulation, matplotlib for visualization, and Streamlit to create an interactive web application.

Project Overview The main goal of this assignment was to practice a fundamental data science workflow, including:

Data Loading and Exploration: Loading a real-world dataset and examining its structure and contents.

Data Cleaning and Preparation: Handling missing values and preparing data for analysis.

Data Analysis and Visualization: Deriving insights and creating meaningful charts.

Interactive Application Development: Building a simple web app to present findings using Streamlit.

File Structure The project is organized into the following files and directories:

data/: Contains the metadata.csv file from the CORD-19 dataset.

analysis_notebook.ipynb: A Jupyter Notebook where the initial data exploration, cleaning, and analysis were performed.

app.py: The Python script that contains the code for the Streamlit web application.

README.md: This file, which provides an overview of the project.

How to Run the Application To run the Streamlit application on your local machine, follow these steps:

Clone the Repository: If you haven't already, clone this repository to your local machine.

Install Dependencies: Make sure you have Python installed. Then, navigate to the project directory in your terminal and install the required libraries using pip:

Bash

pip install pandas matplotlib seaborn streamlit Download the Data: Download the metadata.csv file from the CORD-19 dataset on Kaggle and place it inside the data/ folder.

Run the App: From your terminal, run the app.py script using the Streamlit command:

Bash

streamlit run app.py View in Browser: Your default web browser should open automatically and display the application.

Key Findings Through the analysis of the metadata.csv file, the project visualizes several key trends, including:

The rapid increase in research publications related to COVID-19 over time.

The distribution of research papers across different publication years.

(You can add other specific findings here, like top journals or word frequency.)

Reflection on Challenges and Learning This assignment provided valuable hands-on experience with the data science workflow. One of the main challenges was handling the large size of the dataset and deciding on a proper data cleaning strategy for missing values. Learning to build a simple but effective interactive application with Streamlit was a key takeaway, demonstrating how to present data insights in a more engaging way than static plots.

This project serves as a foundational example of how to go from raw data to a shareable, interactive data product.