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What is the difference between Data Science and Analytics?

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Data science is an umbrella term for a group of fields that are used to mine large datasets. Data analytics software is a more focused version of this and can even be considered part of the larger process. Analytics is devoted to realizing actionable insights that can be applied immediately based on...
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Data science is an umbrella term for a group of fields that are used to mine large datasets.

Data analytics software is a more focused version of this and can even be considered part of the larger process.

Analytics is devoted to realizing actionable insights that can be applied immediately based on existing queries.

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My teaching experience 12 years

Data science is an umbrella term for a group of fields that are used to mine large datasets. Data analytics software is a more focused version of this and can even be considered part of the larger process. Analytics is devoted to realizing actionable insights that can be applied immediately based on...
read more
Data science is an umbrella term for a group of fields that are used to mine large datasets. Data analytics software is a more focused version of this and can even be considered part of the larger process. Analytics is devoted to realizing actionable insights that can be applied immediately based on existing queries. read less
Comments

Data Analyst with 10 years of experience in Fintech, Product ,and IT Services

**Data Science**:- **Scope**: Broad field.- **Focus**: Predictive models and new insights.- **Techniques**: Machine learning, advanced stats.- **Tools**: Python, R, TensorFlow.- **Outcome**: Future predictions. **Data Analytics**:- **Scope**: Narrower focus.- **Focus**: Understanding past data.- **Techniques**:...
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**Data Science**:
- **Scope**: Broad field.
- **Focus**: Predictive models and new insights.
- **Techniques**: Machine learning, advanced stats.
- **Tools**: Python, R, TensorFlow.
- **Outcome**: Future predictions.

**Data Analytics**:
- **Scope**: Narrower focus.
- **Focus**: Understanding past data.
- **Techniques**: Descriptive stats, basic ML.
- **Tools**: Excel, Tableau.
- **Outcome**: Current insights.

In short, data science predicts the future using advanced tools, while data analytics analyzes past data for current insights.

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