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What is the difference between data analytics and data science?
Data analytics mainly describes and explains what has happened using reports, queries and statistics. Data science goes further by building predictive or automated models, often using machine learning and programming. Analytics supports everyday decisions, while data science is typically used for forecasting, classification or recommendations once the foundations are in place.
The boundary is blurry, and job titles vary between organisations. Broadly:
- Analysts focus on business questions, metrics and communication
- Data scientists focus on modelling, experimentation and prediction
Most smaller companies get more value from strong analytics first. Predictive models are only as good as the underlying data, so skipping the foundations tends to produce models nobody trusts or uses.
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