4 Reasons Why Data Scientists Should Add Mathematical Optimization to Their Analytics Toolbox


Today’s data scientists need to have a full analytics toolbox at their disposal. But which tools do they actually need?

In addition to machine learning, visualization, heuristics, and other common tools, mathematical optimization is becoming an essential technology for more and more data scientists.

With a full set of analytics tools including mathematical optimization, data scientists can maximize the business value of their data–by using it make accurate predictions and optimal decisions

Indeed, mathematical optimization is a powerful prescriptive analytics technology that should be included in every data scientist’s analytics toolbox. Read this new management paper to find out why.


Mathematical Optimization technologies are powerful, fast and easy to use.


Machine learning enables you to generate predictions, optimization empowers you to make decisions.


Mathematical optimization can be used to improve the accuracy of machine learning-based predictions.


Data Scientists can build and train interpretable models for classification and prediction problems.


Why Should You Use Optimization?

In this video, Gurobi CEO and Co-founder Ed Rothberg explains how mixed-integer programming (MIP) combines expressiveness and robustness to produce high-quality, reliable solutions.

The Next Step for Enterprises: Optimization Transformation

In this article, Gurobi Technical Fellow and VP Dr. Gregory Glockner details how organizations are applying mathematical optimization, a powerful prescriptive analytics technology, to power digital transformation, decision optimization, and competitive advantage.