Welcome to “Introduction to Optimization Through the Lens of Data Science,” a groundbreaking massive open online course (MOOC) developed by Gurobi in partnership with Dr. Joel Sokol, professor at Georgia Tech.

This course provides a unique opportunity for data scientists to enhance their skill sets and for educators to bring cutting-edge, practical knowledge into their classrooms.


Course Highlights

  • Expert-Led Curriculum: Learn from Dr. Joel Sokol, a leading expert in optimization and analytics, and founder/director of the Master of Science in Analytics Program at Georgia Tech, who brings over two decades of experience in both academic and practical applications of optimization.
  • Designed for Data Scientists: Tailored specifically for students and professionals in data science, this course makes optimization accessible, bypassing the deep dive into theory and focusing on practical, actionable skills.
  • Code-Focused Examples: The course is designed to meet data scientists where they are in their problem-solving journey, presenting the code right alongside the math and English language versions of each problem.
  • Three-Part Learning Journey: Engage with the course through three main components—motivation, modeling, and cases—to understand not just the “how” but also the “why” behind optimization.
  • Hands-On Learning: From the very basics to complex real-world problems, get hands-on experience with optimization models using the Gurobi Optimizer and Python’s gurobipy library.
  • Real-World Applications: Dive into case studies ranging from a mobile retail location example with Gurobi’s Burrito Game to power generation challenges, illustrating the profound impact of optimization on decision-making in diverse contexts.

Course Overview

Part 1
See optimization in action using the Burrito Optimization Game and be exposed to a wide variety of successful use cases. Learn the building blocks of mathematical optimization and get comfortable with the key concepts required to create your first optimization models with supplemental material for establishing best practices going forward.

View Part 1

Part 2
Dive deeper into the relationship between optimization and data science. Work with more complex constraints, understand model reusability, analyze sensitivity, and understand infeasibility. Classify types of optimization problems and see how they are solved at a high level.

View Part 2

Part 3
Model yes/no decisions and complex logical constraints with binary variables and link them to continuous variables. Explore classic optimization model archetypes.

View Part 3

Part 4
The final part of the course puts everything from parts 1 through 3 together: solving real-world examples, working from problem statements to mathematical formulations, to code, and to solutions.

View Part 4

For Educators

Bring the latest in optimization to your classroom. This course offers a comprehensive curriculum that you can integrate into your teaching, providing students with skills that are in high demand in the industry. With a blend of theoretical knowledge and practical applications, you can prepare your students for the challenges of tomorrow.


Course Tools

  • If you are currently a member of the academic community and affiliated with a university, you can download a free, full-featured Gurobi Optimizer license.
  • For all other learners, you can access your free limited Gurobi Optimizer license to use while completing exercises and cases throughout the course.


Meet Dr. Joel Sokol
Dr. Sokol is a Harold E. Smalley Professor at Georgia Tech’s H. Milton Stewart School of Industrial and Systems Engineering and founding Director of Georgia Tech’s Master of Science in Analytics degree.


* This is an open education course, and we encourage faculty and students to use the exercises and materials included to help meet educational and instructional goals. Gurobi Optimization, LLC (“Gurobi”) is the owner of the content (all right, title, and interest) of this course (“Content”). Content is not designed or intended for commercial use or for the sale of courses. Gurobi makes this Content available at no cost for educational and instructional use via faculty and student download and use of Content in courses offered at your institution/organization; provided that such use does not scale or otherwise result in the use of Content for monetary gain including in any other online course. Content is offered as-is and all risk of use resides with you. Gurobi makes no warranties or guarantees of a particular outcome and accepts no liability, direct, consequential or otherwise, arising from the access to or use of the course and Content contained in it. 

Resources for Data Scientists

Resource > Report
Report: State of Mathematical Optimization in Data Science 2023

Discover key data science trends in learning, team structures, and problem-solving methods.

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Gurobi OptiMods: Simple APIs for Common Optimization Tasks

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new content
Burrito Optimization Game

The game was developed as a free educational tool for introducing students to the power of optimization. In order to play the game, you will need to be logged in to your Gurobi account.

 Learn More

Guidance for Your Journey

30 Day Free Trial for Commercial Users

Start solving your most complex challenges, with the world's fastest, most feature-rich solver.

Always Free for Academics

We make it easy for students, faculty, and researchers to work with mathematical optimization.

Try Gurobi for Free

Choose the evaluation license that fits you best, and start working with our Expert Team for technical guidance and support.

Evaluation License
Get a free, full-featured license of the Gurobi Optimizer to experience the performance, support, benchmarking and tuning services we provide as part of our product offering.
Academic License
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.
Cloud Trial

Request free trial hours, so you can see how quickly and easily a model can be solved on the cloud.