Healthcare organizations generate huge amounts of patient and provider data daily. In this blog, we use Python to analyze the HC Dataset from Kaggle, focusing on patient discharge trends, provider specialties, and ambulatory visit patterns. Key insights include variations in length of stay (LOS), discharge dispositions, and visit volumes, which help hospitals optimize staffing, improve operational efficiency, and improve patient care.

Hi team! Check out my latest blog on why data cleaning is critical for accurate visualizations and better business decisions. I also show a real-world COVID-19 dataset case study using Python.

The Python hackathon gave me hands-on experience analyzing the Flatten COVID-19 dataset in Jupyter Notebook—the digital lab book where data scientists prototype Python, R, and more. Before writing a single line of code, I learned the first rule of data analysis: deeply understand your dataset first. Dataset Reality Check: This wasn't hospital lab data. It contained self-reported COVID-19 symptom surveys from Ontario residents during early 2020, split across 3 evolving schemas

Visual storytelling  in data analysis is the practice of using visuals—charts, graphs, plots, and annotated figures—to communicate...