Data Science
Last Updated: July 29, 2026 | By Mihail Sebastian | AI Dictionary
The discipline combining statistics, programming, and domain knowledge to extract insight from data and build systems that predict and decide.
What is Data Science?
Data science is the discipline that combines statistics, programming, and domain knowledge to extract insight from data and build systems that predict and decide.
The label covers the full arc of working with data: collecting and cleaning it, exploring it, training machine-learning models, and putting the results into production. What separates data science from plain analysis is that last step – the goal is a working system, not just a finding.
How Data Science Works
A project starts with a question the organization cares about: which customers will cancel, which transactions are fraudulent, which machine will fail next. The data scientist gathers the relevant data and cleans it, a stage that routinely consumes more time than any other.
Exploratory data analysis comes next: plotting distributions, checking quality, testing hunches. Only then does modeling begin, with machine learning or statistical methods evaluated against held-out data. The finished model ships into production, where the team monitors it for data drift and retrains it as the world changes.
Data Science vs Data Analytics
The practical difference: data analytics answers business questions from existing data, while data science builds predictive systems that keep answering after the analyst walks away. An analyst tells you which customers churned last quarter and why; a data scientist ships a model that scores every current customer’s churn risk each morning.
| Criterion | Data Science | Data Analytics |
|---|---|---|
| Core question | What will happen, and can we build a system around it? | What happened, and why? |
| Typical output | Predictive models, algorithms, data products | Reports, dashboards, recommendations |
| Main methods | Machine learning, statistical modeling, software engineering | SQL, statistical analysis, visualization |
| Time orientation | Future-facing | Past and present |
Example of Data Science
Predictive maintenance in manufacturing. A factory instruments its machines with vibration and temperature sensors, and a data science team assembles years of those readings alongside maintenance logs that record every past failure.
The team trains a model to recognize the sensor patterns that precede a breakdown, then deploys it against the live feed. When a bearing starts producing the telltale vibration signature, the system flags the machine days before it fails, and maintenance happens on schedule instead of after a production line stops.
Related AI terms: Data Analytics · Data Analyst · Data Mining · Exploratory Data Analysis · Machine Learning
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Mihail Sebastian — Writes about AI governance, regulation, and the technology behind them. Placeholder bio — replace with a real credential line. About