pythonFrames

Data Loading and Basic Exploration (2-3 hours)

Download and load the data

Download only the metadata.csv file from the CORD-19 dataset

Load it into a pandas DataFrame

Examine the first few rows and data structure

Basic data exploration

Check the DataFrame dimensions (rows, columns)

Identify data types of each column

Check for missing values in important columns

Generate basic statistics for numerical columns

Data Cleaning and Preparation (2-3 hours)

Handle missing data

Identify columns with many missing values

Decide how to handle missing values (removal or filling)

Create a cleaned version of the dataset

Prepare data for analysis

Convert date columns to datetime format

Extract year from publication date for time-based analysis

Create new columns if needed (e.g., abstract word count)

Data Analysis and Visualization (3-4 hours)

Perform basic analysis

Count papers by publication year

Identify top journals publishing COVID-19 research

Find most frequent words in titles (using simple word frequency)

Create visualizations

Plot number of publications over time

Create a bar chart of top publishing journals

Generate a word cloud of paper titles

Plot distribution of paper counts by source

Streamlit Application 

Build a simple Streamlit app

Create a basic layout with title and description

Add interactive widgets (sliders, dropdowns)

Display your visualizations in the app

Show a sample of the data