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🐍 Why Python is asked for DA/BA roles: It signals problem-solving skills, helps with automation when Excel hits limits, keeps analysts future-ready, and is often added because many job descriptions are copied from Data Science roles.
📉 Why Python is used only ~5% in real DA/BA jobs: Most data already lives in databases where SQL is faster, stakeholders prefer Excel and dashboards over code, BI tools handle most analysis, and Python is needed only for messy data, large files, or automation.
📚 How much Python is enough for DA/BA: Basic Python syntax, NumPy, Pandas for reading/cleaning/grouping data, and optional basic visualization — anything beyond this gives low returns for analyst roles.
🧠 Why Python is critical for Data Scientists: Data scientists depend on Python for large-scale data cleaning, feature engineering, statistical analysis, model building, evaluation, and running ML/AI workflows daily.
⚙️ Why Data Engineers use Python every day: Data engineers build ETL/ELT pipelines, automate data ingestion, work with APIs and streaming data, and connect cloud systems where Python becomes the backbone.
🎯 Final truth most people miss: Python is a support skill for Data Analysts & Business Analysts, a core skill for Data Scientists, and a non-negotiable foundation for Data Engineers.
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@khan.the.analyst
