Python for Data Science - Real-World Workflow

Python is the backbone of Data Science and AI. Its simplicity and ecosystem make it ideal for real-world workflows.
A typical Python Data Science workflow looks like this:

1. Data Loading - Pandas, NumPy
2. Data Cleaning - Handling missing values, outliers
3. EDA - Understanding data using visualizations
4. Modeling - Machine Learning using Scikit-learn
5. Evaluation - Measuring performance
6. Insights - Communicating results clearly

Python allows Data Scientists and AI Engineers to move quickly from raw data to actionable insights. Whether you are analyzing business data or building AI models, Python remains an essential skill.
This blog will continue sharing practical Python workflows, projects, and AI concepts to help you grow in your career.

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