Data Science vs Data Engineering

Data scientists are focused on building and maintaining systems that process and handle large volumes, varieties, and velocities of data, while data engineers extract insights from raw data to generate value. (correct)

Data scientists capture and collate large volumes of data and generate approximations and predictions about the future, while data engineers apply mathematical modeling and communicate data insights.

Data scientists are subject matter experts who use data science to generate insights, while data engineers require analytical know-how, coding skills, and subject matter expertise.

Data scientists focus on building and maintaining systems that process and handle large volumes, varieties, and velocities of data, while data engineers extract insights from raw data to generate value.

Structured, unorganized, and relational.

Unstructured, relational, and hierarchical.

Structured, hierarchical, and unorganized.

Structured, unstructured, and semistructured. (correct)

To improve their lives, careers, and businesses. (correct)

To focus on building and maintaining systems that process and handle large volumes, varieties, and velocities of data.

To stay irrelevant.

To avoid using data insights.

<p>Collecting, querying, and consuming data, applying mathematical modeling, and communicating data insights.</p> Signup and view all the answers

<p>Python and R.</p> Signup and view all the answers

<p>To understand data significance, validate hypotheses, simulate scenarios, and make predictive forecasts.</p> Signup and view all the answers

<p>Analytical know-how, coding skills, and subject matter expertise.</p> Signup and view all the answers