As the demand for more data scientist positions continues to grow, so does the need for tools and techniques that go beyond descriptive and predictive analytics.
Prescriptive analytics—and mathematical optimization in particular—can turn data insights into actionable, value-driven decisions. This technology is gaining traction among data scientists who want to answer not only “What happened?” or “What will happen?” but also “What should we do about it?”
In this post, we’ll explore why optimization should be in every data scientist’s toolkit.
What’s driving optimization’s growing prominence in data science? First, businesses are increasingly seeking solutions that deliver more than just predictions—they want reliable strategies. Optimization enables data scientists to provide prescriptive recommendations that directly support organizational goals.
Second, optimization tools have become more accessible, with advancements in solver technology and integration with popular programming languages like Python. This has helped to lower the barrier to entry, making it easier for data scientists to incorporate optimization into their workflows.
Finally, the synergy between data science and optimization is becoming clearer. Machine learning and mathematical optimization complement one another, with optimization refining and operationalizing the outputs of predictive models.
The infographic below explores some of the trends we’ve seen in how data scientists are using and learning about optimization, with insights from our annual State of Mathematical Optimization in Data Science report.
Optimization and machine learning address two different questions, but that doesn’t mean they aren’t complementary.
In fact, according to our annual report, 66% of respondents currently use mathematical optimization in combination with machine learning, or plan to do so in the near future.
Here are a few examples of how these two technologies can be used together to make an even bigger impact:
Despite optimization’s growing prominence in the data science field, there’s still plenty to be learned, especially as the technology continues to evolve and more resources become available to help data scientists master optimization.
According to our survey, 47% of data scientists still rely on heuristics alone because they are “good enough,” with 15% stating that they use heuristics because they are unfamiliar with optimization techniques.
While heuristics alone may sometimes be “good enough,” the difference between a locally optimal heuristic-based solution and a globally optimal one could be millions in savings or efficiency gains.
Gurobi has a plethora of resources dedicated to helping data scientists leverage optimization. To get started, visit gurobi.com/learn.
To learn more about the State of Mathematical Optimization in Data Science, download the complete report.
Senior Data Science Strategist
Senior Data Science Strategist
Mr. Yurchisin has over ten years’ experience applying operations research, machine learning, statistics, and data visualization to improve decision making. Before joining Gurobi, Jerry (who also goes by Jerome) was a Senior Consultant at OnLocation, Inc. where he customized several linear programming models within the National Energy Modeling System (NEMS) to analyze implementing specific energy policies and utilizing new technologies. Prior to OnLocation, Jerry was an Operations Research Analyst & Data Scientist at Booz Allen Hamilton for over seven years. There he formulated scheduling and staffing integer programming models for the US Coast Guard, as well as led a project to quantify the maritime risks of offshore energy installations with the Research & Development Center. Further, Jerry was the technical lead on several Coast Guard studies including Living Marine Resources and Maritime Domain Awareness, providing statistical analysis and building supervised and unsupervised machine learning models. He also performed statistical analyses, machine learning modeling, and data visualization for cyberspace directorates at DoD and DHS. Jerry has several years of experience teaching a wide variety of college-level mathematics and statistics courses and has a passion for education. He also enjoys golfing, biking, and writing about sports from an analytics point of view. He lives in Alexandria, Virginia with his wife, son, and two dogs. Jerry holds B.S., Ed. and M.S., Mathematics degrees from Ohio University and an M.S. in Operations Research and Statistics from The University of North Carolina at Chapel Hill.
Mr. Yurchisin has over ten years’ experience applying operations research, machine learning, statistics, and data visualization to improve decision making. Before joining Gurobi, Jerry (who also goes by Jerome) was a Senior Consultant at OnLocation, Inc. where he customized several linear programming models within the National Energy Modeling System (NEMS) to analyze implementing specific energy policies and utilizing new technologies. Prior to OnLocation, Jerry was an Operations Research Analyst & Data Scientist at Booz Allen Hamilton for over seven years. There he formulated scheduling and staffing integer programming models for the US Coast Guard, as well as led a project to quantify the maritime risks of offshore energy installations with the Research & Development Center. Further, Jerry was the technical lead on several Coast Guard studies including Living Marine Resources and Maritime Domain Awareness, providing statistical analysis and building supervised and unsupervised machine learning models. He also performed statistical analyses, machine learning modeling, and data visualization for cyberspace directorates at DoD and DHS. Jerry has several years of experience teaching a wide variety of college-level mathematics and statistics courses and has a passion for education. He also enjoys golfing, biking, and writing about sports from an analytics point of view. He lives in Alexandria, Virginia with his wife, son, and two dogs. Jerry holds B.S., Ed. and M.S., Mathematics degrees from Ohio University and an M.S. in Operations Research and Statistics from The University of North Carolina at Chapel Hill.
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