Data science and decision science both are related to extracting meaningful insights out of raw data however their approach and end goals are quite different.
To explain in simple terms, data scientists are professionals who mainly work with big data, and employ tactics like mathematics and statistics, machine learning, and their expertise in programming language to identify patterns and trends from data. As per a recent IBM study, 90% of organizations reported they use data and analytics for improved and data-driven decision-making. Data scientists ensure to build effective data science models that can translate complex insights into actionable insights.
On the other hand, decision scientists take the work of data scientists further. They require more advanced skills including mathematics and statistics, along with business acumen in the fields of finance, retail, etc. Their role is to use the insights and patterns found by data scientists to make decisions by using other advanced technologies like AI, their SMEs, and business contexts. Decision scientists are focused on using data for informed decisions.
So, if you are looking to make a career in data science, you must have a clear understanding of the distinction between data scientist and decision scientist, which will help you build your data science career roadmap properly and enable you to learn the right skills.
Therefore, check out our detailed infographic on data science vs. decision science, and empower yourself with the right knowledge.
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