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Start Solving a Different Class of Problems

As a data scientist, your curiosity, diligence, and creativity drive you to extract immense value from your data and models. But what if you could generate optimized decision recommendations, based on your predicted future—to directly influencing business decision-making? With Gurobi, you can.

The World’s Leading Enterprises Optimize with Gurobi

Insight for Data Scientists

Explore these specially curated content pieces. 

Event
Gurobi Days Digital 2024

Join us for our Gurobi Days Digital experience on June 25- 26, 2024!

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Event
PSCC 2024 - Power Systems Computation Conference

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Resource > Blog
Using Snowsight: Streamlining Gurobi Powered Decision-Making in Snowflake (Part Two)

Learn how you can use Snowsight to streamline the execution of Gurobi models within Snowflake.

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Resource > Blog
Next-Level Data Science: Enabling Optimization With Gurobi in Snowflake (Part One)

Your step-by-step guide to integrating Gurobi with Snowflake.

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Resource > Blog
Completing Your Decision Pipeline

Confidently bridge the gap between simplified prediction models and operational complexity.

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Event
Gurobi Connect Stockholm

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Resource > Blog
Bridging the Optimization Skills Gap With Generative AI

How GenAI can make optimization tools more accessible and effective

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Resource > Blog
Optimizing Complex Army Logistics: Gurobi Solver Triumph

Discover how Decision Lab and Gurobi teamed up to enhance the efficiency and effectiveness of army training programs.

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Resource > Blog
Mathematical Optimization Is Strong: So What Stands In Its Way?

Explore key trends across a widening field.

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Resource > Training
Introduction to Optimization Through the Lens of Data Science

Unlock the power of optimized decision-making with this online course developed by Gurobi in partnership with Dr. Joel Sokol, professor at Georgia Tech.

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Open-Source Projects

We believe optimization has the power to make the world a better place. So we’ve created some innovative, open-source tools that help get optimization into more people’s hands—especially those without prior knowledge of optimization and mathematical modeling.

“We’re aiming to connect the world of data science with the world of optimization. With Gurobi, you can take your machine learning ‘black box’ that’s generating your predictions and plug it directly into your optimization model—enabling you to connect your forecasting with optimization.”
Dr. Tobias Achterberg, Vice President of Research and Development, Gurobi Optimization

  • Gurobi Machine Learning
  • Gurobipy Pandas
  • Gurobi OptiMods
  • Gurobi Machine Learning
  • Gurobipy Pandas
  • Gurobi OptiMods
  • Gurobi Machine Learning

    Gurobi Machine Learning

    With Gurobi Machine Learning—an open-source Python project to embed trained machine learning models directly into Gurobi—data scientists can more easily tap into the power of mathematical optimization.

  • Gurobipy Pandas

    Gurobipy Pandas

    Gurobipy Pandas is our convenient wrapper library to connect pandas with gurobipy. It enables users to efficiently build mathematical optimization models from data stored in DataFrames and Series and extract solutions as pandas objects.

  • Gurobi OptiMods

    Gurobi OptiMods

    Gurobi OptiMods is an open-source Python repository of implemented optimization use cases using Gurobi, each with clear and informative documentation that explains how to use it and the mathematical model behind it.

Frequently Asked Questions

  • Prescriptive Analytics

    • What is prescriptive analytics?

      Prescriptive analytics tools like mathematical optimization help you make decisions based on your real-world business goals (“objectives”) and limitations (“constraints.”) This can be especially useful when you’re facing a business problem with multiple, conflicting goals (such as cutting spending while increasing production) and multiple constraints (such as time, distance, product availability).

      Learn more about prescriptive analytics in our article, “What is Prescriptive Analytics?”

    • What is the difference between predictive analytics and prescriptive analytics?

      Predictive analytics seeks to identify patterns in data to forecast future events, such as predicting cyberattacks or imminent machine failures. Prescriptive analytics, on the other hand, utilizes mathematical modeling to guide decisions based on real-world objectives and constraints, such as minimizing costs or managing raw material inventory.
      While predictive analytics tells you what might happen, prescriptive analytics provides actionable recommendations on how to achieve specific goals, given certain limitations.

      Learn more about the difference in our article, “Predictive Analytics vs. Prescriptive Analytics.”

    • What are some examples of prescriptive analytics in the real world?

      In the real world, prescriptive analytics has diverse applications, including transportation providers like Air France and Uber using it to create optimal routing, staffing, and maintenance plans. Professional sports leagues, such as the National Football League, plan their game schedules using prescriptive analytics. Additionally, manufacturers utilize prescriptive analytics to plan and manage the procurement, production, and distribution of their products, aligning decisions with real-world goals and constraints.

      Learn more about examples in our article, “Examples of Prescriptive Analytics.”

    • Can I improve my machine learning applications by applying optimization?

      Yes! By using machine learning predictions as valuable input for mathematical optimization solutions, or conversely, using mathematical optimization to inform machine learning predictions, you can leverage the problem-solving power of mathematical optimization to enhance machine-learning applications.
      Learn more in our article, “Improving Machine Learning Applications with Prescriptive Analytics.”

    • How can prescriptive and predictive analytics work together?

      Say you were planning a trip. Predictive analytics can predict what you may encounter along your journey (weather, traffic, engine trouble), and prescriptive analytics can, given those predictions, identify the route that best helps you achieve your goals (fastest, cheapest, safest route), given your constraints (time, budget, speed limits).
      Here are some additional examples:

      • Use predictive analytics to predict supply chain issues, and use prescriptive analytics to identify the least costly way to reroute shipments.
      • Use predictive analytics to predict cyberattacks before they happen, and use prescriptive analytics to identify the right investigators based on cost and skill.
      • Use predictive analytics to predict imminent machine failure, and use prescriptive analytics to identify the best time to shut down the production line.
      • Use predictive analytics to predict customer likelihood to buy more with targeted offers, and use prescriptive analytics to identify how many discount coupons to offer, in order to maximize revenue.

      Learn more in our article, “How Can Prescriptive and Predictive Analytics Work Together?”

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Evaluation License
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Gurobi supports the teaching and use of optimization within academic institutions. We offer free, full-featured copies of Gurobi for use in class, and for research.
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