Data Analytics

Data analytics is the practice of inspecting, transforming, and summarizing data to support decisions. It is commonly divided into descriptive (what happened), diagnostic (why), predictive (what may happen), and prescriptive (what to do) analysis.

Why it matters

Analytics turns raw numbers into decisions: where traffic comes from, which feature people use, which pages fail. It differs from data science mainly in emphasis; analytics leans on existing data and reporting, while data science leans more on modeling.

Example

A basic descriptive query:

SELECT date_trunc('week', created_at) AS week,
       COUNT(*) AS signups
FROM users
GROUP BY week ORDER BY week;

The weekly result might reveal a dip that diagnostic work then traces to a broken form.

Common mistakes

• Trusting a metric whose definition nobody has written down.
• Reporting averages when the median or a percentile describes the experience better.
• Drawing conclusions from tiny samples or short windows.

Related

• Dashboard
• Data warehouse
• A/B testing
• Data cleaning

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