Python for Data Science - Real-World Workflow

This article discusses how Python is the backbone of Data Science and AI, with its simplicity and ecosystem making it ideal for real-world workflows.

Details

The article outlines a typical Python Data Science workflow, which includes data loading using Pandas and NumPy, data cleaning to handle missing values and outliers, exploratory data analysis (EDA) to understand the data using visualizations, modeling using machine learning techniques from Scikit-learn, evaluation to measure performance, and finally, communicating the insights clearly. Python's simplicity and extensive ecosystem make it an essential tool for Data Scientists and AI Engineers, whether they are analyzing business data or building AI models.