Data science and decision science are two closely related yet distinctive areas of expertise. And for all the students or professionals looking to start or advance in their data science careers, a better understanding of the intricate difference between these two concepts is crucial.

Data science is one of the most popular fields of technology and a popular career path. The data science market is expected to reach $484.17 billion by 2029, as reported by Fortune Business Insights. Not just that, employment in this field is also expected to grow by 32% by 2030 as per the US Bureau of Labor Statistics.

Data science helps businesses find trends and actionable insights by processing and analyzing huge amounts of data. But decision science is quite different and it takes the work of data science a step further.

While data science is confined to extracting patterns and trends, decision science helps organizations use those findings to assist stakeholders in data-driven decision-making.

Decision scientists are proficient in mathematics, statistics, and computer programming, as well as industry-specific business knowledge. They use this business acumen to use data science reports and help with making business decisions.

Grab our detailed infographic on data science vs. decision science, and understand the thin line differentiating both these important concepts in the world of data-driven-decision-making.