It's very likely that throughout your journey with Python, you've heard people say that Python is slow. While there is a gap between interpreted and compiled languages ​​that favors compiled languages, Python has ways to improve the performance of your programs, but these aren't widely known among coders. In this talk, we'll explore some tools and programming patterns that will help you improve the performance of your programs, thereby improving the speed of your applications, tests, and products. After the presentation, you'll have a list of techniques you can apply to your code, as well as the necessary steps to continue exploring code optimization. No prior knowledge of code profilers or advanced techniques is required to attend this talk.

This session will provide an introduction to applications of machine learning to optimization. Optimization (often called prescriptive analytics) is a branch of data science that recommends the best actions for maximizing a desirable outcome (or minimizing an undesirable outcome). Modern applications often involve a combination of machine learning and mathematical programming. Attendees will get an introduction to modern applications of prescriptive analytics, illustrated through a variety of real world use cases. These use cases include optimizing treatments to maximize health outcomes, optimizing pricing to maximize profits, and optimizing maintenance operations to minimize cost. A review of these real world applications will enable attendees to explore how prescriptive analytics might contribute value to their own organizations.

This session will provide an introduction to applications of machine learning to optimization. Optimization (often called prescriptive analytics) is a branch of data science that recommends the best actions for maximizing a desirable outcome (or minimizing an undesirable outcome). Modern applications often involve a combination of machine learning and mathematical programming. Attendees will get an introduction to modern applications of prescriptive analytics, illustrated through a variety of real world use cases. These use cases include optimizing treatments to maximize health outcomes, optimizing pricing to maximize profits, and optimizing maintenance operations to minimize cost. A review of these real world applications will enable attendees to explore how prescriptive analytics might contribute value to their own organizations.