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Choosing a Language for Data Work Print

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Analysis and machine learning.

WHAT PYTHON PROVIDES

The largest machine learning ecosystem General-purpose capability, so analysis and production share a language Strong data manipulation libraries

WHAT R PROVIDES

The deepest statistical package collection Superior visualisation, arguably Better reproducible reporting

WHAT SQL PROVIDES

The ability to work with data where it lives, rather than extracting it.

WHY THAT MATTERS

Filtering and aggregating in the database is far faster than extracting everything.

WHAT MOST PRACTITIONERS USE

SQL for extraction, and Python or R for analysis.

WHAT TO CHOOSE ON

What your team knows What your organisation already uses Whether the work is statistical research or production machine learning

WHAT TO PREFER FOR PRODUCTION SYSTEMS

Python, usually, since the model and the application can share a language.

WHAT TO PREFER FOR STATISTICAL RESEARCH

R, where the methods are.

WHAT NOT TO DO

Argue about the choice rather than doing the analysis.

WHAT MATTERS MORE THAN THE LANGUAGE

Understanding the data, and asking the right question.


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