Go From Data to Decisions

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 go beyond delivering high-confidence predictions and directly influence decision-making? With Gurobi, you can.

  • Step 1: Discover the power of optimization for data science
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  • Step 2: Explore how optimization can level-up your skill set
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  • Step 3: Experiment with our Python example library
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  • Step 4: Learn optimization with our free educational resources
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Report: State of Data Science 2022

Are you ready to explore the cutting-edge intersection of mathematical optimization and data science? Dive into the “State of Mathematical Optimization in Data Science 2022” report—a comprehensive analysis that uncovers the latest trends, applications, and insights in the field. Read the Report


Jump in with Our Examples

With our examples, you can be tinkering around with optimization in just minutes—and learn how to incorporate optimization into your machine learning projects.

Start Your Journey


  • Rich Python Modeling Environment
  • Lightweight Programming Interfaces
  • Multiple Scenario Analysis
  • General Constraints
  • Simple General Constraints
  • Rich Python Modeling Environment
  • Lightweight Programming Interfaces
  • Multiple Scenario Analysis
  • General Constraints
  • Simple General Constraints
  • Gurobi Optimizer Delivers Rich Python Modeling Environment

    Rich Python Modeling Environment

    Our Python API includes higher-level modeling constructs that make it easier to build optimization models. Choose from Anaconda Python distributions with pre-built libraries to support application development, Spyder for graphical development, and Jupyter for notebook-style development.

    • Python interactive interface for powerful prototyping and quick testing
    • Language extensions for easier coding of a model
    • Documented best practices to get you started quickly
    • Pre-built Python libraries support full application development
    • Python Matrix API for matrix-oriented modeling using NumPy or SciPy matrices

  • Gurobi Optimizer Delivers Lightweight Programming Interfaces

    Lightweight Programming Interfaces

    Integrate Gurobi into your applications easily, using the languages you know best. Our programming interfaces are designed to be lightweight, modern, and intuitive, to minimize your learning curve while maximizing your productivity.

    • Object-oriented interfaces for C++, Java, .NET, and Python
    • Matrix-oriented interfaces for C, MATLAB, and R
    • Links to standard modeling languages: AMPL, GAMS, AIMMS, and MPL
    • Links to Excel through Frontline Solvers

  • Gurobi Optimizer Delivers Multiple Scenario Analysis

    Multiple Scenario Analysis

    Use Gurobi as a “digital twin” for your business—enabling you to explore the business impact of certain decisions or what-if scenarios, before they happen.

  • Gurobi Optimizer Delivers General Constraints

    General Constraints

    By supporting variable relationships directly in the Gurobi API, we simplify the modeling process—performing the transformation to a corresponding MIP formulation automatically and transparently during the solution process.

  • Gurobi Optimizer Delivers Function Constraints

    Simple General Constraints

    Express common modeling constructs like MIN/MAX, ABS, AND/OR, and IF/THEN at a higher level, making such models easier to build and maintain.

Frequently Asked Questions

  • What is Prescriptive vs. Predictive Analytics?

    Prescriptive analytics tools help you make decisions based on your real-world 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).

    Predictive analytics tools seek to find patterns in data, in order to predict what might happen in the future. For example, predictive analytics can predict who will launch which cyberattack, which experiments are more likely to prove the hypothesis, imminent machine failure, supply chain issues, infrastructure maintenance needs, and price movements—all before they happen.

  • What Is an Example of Prescriptive Analysis?

    Although there are countless ways to use prescriptive analytics, here are some real-world examples, with links to their stories:

    • Transportation providers, such as Air France, Swissport, and Uber, use prescriptive analytics to create optimal routing, staffing, and maintenance plans.
    • Professional sports leagues, including the National Football League and Beko BBL, plan their game schedules using prescriptive analytics.
    • Manufacturers use prescriptive analytics to plan and manage the procurement, production, and distribution of their products.
  • What Is the Goal of Prescriptive Analytics?

    Prescriptive analytics tools provide a detailed set of recommendations for how you can best achieve your goals, given your limitations. Although you can use it to automate decision-making, you can use it to inform your traditional decision-making processes. Its ability to explore what-if scenarios can be particularly helpful.

  • How Can Prescriptive Analytics Be Used with Predictive Analytics?

    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.


  • What Is an Advantage of Prescriptive Analytics?

    Prescriptive analytics doesn’t rely on historical data—which means you can make decisions for the future, even when it doesn’t look like your past. To use prescriptive analytics, you need to know three things:

    • The goals you need to achieve (“objectives”)
      • Such as minimizing product costs
    • The limitations you’re facing (“constraints”)
      • Such as minimum production of a given product, required manufacturing time and cost of a particular machine, raw material inventory, and finished goods inventory capacity
    • The questions you’re asking (“decision variables”)
      • Such as, “In which order should we produce which products?”, “In which manufacturing facilities?”, “On what product lines?”, and “In what quantities?”

    With this information, the prescriptive analytics tool can generate a detailed action plan for achieving your goals, given your limitations.

Guidance for Your Journey

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