What is the difference between data scientist, data engineer & data analyst?

• Data science, data analyst & data engineering are all closely related fields.

• However, each is focused discipline filling a unique role within a large enterprise.

• These three roles work together to ensure that organizations can make the most of their data.

Data Scientist:

Data scientists use machine learning, data exploration and other academic fields to predict future outcomes. Data science is an interdisciplinary field focused on making accurate predictions through algorithms and statistical models. Like data engineering, data science is a code-heavy role requiring an extensive programming background.

Data Analyst:

Data analysts examine large datasets to identify trends and extract insights to help organizations make data-driven decisions today. While data scientists apply advanced computational techniques to manipulate data, data analysts work with predefined datasets to uncover critical information and draw meaningful conclusions.

Data Engineering:

Data engineers are software engineers who build and maintain an enterprise’s data infrastructure—automating data integration, creating efficient data storage models and enhancing data quality via pipeline observability. Data scientists and analysts rely on data engineers to provide them with the reliable, high-quality data they need for their work.