Intro to Optimization Through the Lens of Data Science Pt. 1
Requirements
- Basic Python, college-level mathematics, experience with Jupyter Notebooks, and the ability to open python (.py) and Jupyter Notebooks (.ipynb) files on your machine
Description
Welcome to Introduction to Optimization Through the Lens of Data Science!
This free 4-part course was developed to help teach data scientists how to add optimization to their toolbox and when to use it in their advanced problem-solving. We will cover a comprehensive introduction to optimization, when optimization is the best tool to solve a problem, and how to translate real-life problems into optimization.
We will introduce you to world-class tools to help you problem solve, and provide everything from basic hands-on exercises to more advanced full real-world use cases to reinforce all new concepts of prescriptive analytics as you learn them. We look forward to having you learn optimization (and gurobipy) with expertise from Dr. Joel Sokol and the team of Ph.D. experts from Gurobi Optimization, who helped develop this comprehensive introduction to mathematical optimization.
In part 1, you will see optimization in action using new educational tools and resources 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.
Hands-on Exercises:
Please check the resource section of many of the lectures to find self-assessments in the form of exercise files and solution files. You will also notice we have data and code files available to help you work your way through these practice exercises.
Who this course is for:
- Data scientists and problem solvers curious about mathematical optimization/prescriptive analytics.
Instructor
- 4.7 Instructor Rating
- 706 Reviews
- 5,081 Students
- 4 Courses
Dr. Joel Sokol is the Harold E. Smalley Professor and Director of the Master of Science in Analytics Program (on-campus and Online) at The Georgia Institute of Technology.
He received his Ph.D. in operations research from MIT and his bachelor’s degrees in mathematics, computer science, and applied sciences in engineering from Rutgers University.
His primary research interests are in sports analytics and applied operations research. He has worked with teams or leagues in all three of the major American sports. Dr. Sokol's LRMC method for predictive modeling of the NCAA basketball tournament is an industry leader, and his non-sports research has won the EURO Management Science Strategic Innovation Prize. Dr. Sokol has also won recognition for his teaching and curriculum development from IIE and the NAE, and is the recipient of Georgia Tech's highest awards for teaching.