F# for Data Science: Breaking Boundaries with Strong Typing and Expressive Pipelines

**Think you've seen it all in data science with Python?** Get ready to dismantle your assumptions. In this talk, we’ll explore **F#**, the functional language of the **.NET** ecosystem, which is beginning to infiltrate the workflows of data scientists seeking **greater safety, higher speed, and fewer bugs**. You’ll discover how its **strong, expressive typing**, combined with its **functional pipelines**, enables writing data analyses that are clearer, more correct, and more maintainable. Moreover, we’ll show how to combine F#’s elegance with the full power of the .NET platform: * **Full interoperability with C#** * Use of **machine learning** libraries * Integration with scientific tools * Performance that surprises even Python’s most loyal fans *** **Talk Content** * Why F# is a real alternative to Python for data science * Functional pipelines: from “spaghetti code” to expressive fluency * Strong typing that detects issues before they become bugs * Interoperability with C#: how to leverage the entire .NET ecosystem without sacrificing functional style * Machine learning with F#: available libraries and frameworks * Real-world use case: biological data analysis with F# at iGEM UMA *** If you’re interested in writing **more robust and elegant code**, this talk will transform your way of thinking. Because data science doesn’t have to be a constant battle against silent errors, unreadable notebooks, and uncertain types. **Discover how F# can become your new Swiss Army knife for analyzing, modeling, and building scientific solutions with far less friction.** *** **Target Audience** Developers, data scientists, and engineers interested in exploring new tools and approaches for data science—especially those seeking to improve the **quality, maintainability, and performance** of their analyses and models through functional and strongly typed languages.