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Thursday, November 15 • 10:40am - 11:00am
FP for Data Science: For-Loops Considered Harmful

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Data science, machine learning and probabilistic programming are ready to reap to the benefits of functional programming (FP). FP is well known for the benefits of correctness, expressiveness, composability, parallelism, and more. While functional languages have taken off in many different domains, it has not yet deeply penetrated into modeling and data science, where Python is most common. In this talk, I will demonstrate in both code and design how functional programming patterns are a major benefit to data science practitioners - even when you work with Python. You will come away with a survey of FP techniques and how they relate to modeling and data science, how they fix the pitfalls of imperative languages and design, and a new perspective on why industry should move toward more FP techniques in the domain of data science.

Speakers
avatar for Aris Vlasakakis

Aris Vlasakakis

Machine Learning Engineer, Credit Karma
My interests are in machine learning, functional programming, distributed systems amongst others.


Thursday November 15, 2018 10:40am - 11:00am
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Attendees (25)