Live build: How to harness streaming data in real time to track, transform and build on heart rate data
06-13, 11:50–12:50 (Europe/Berlin), Frannz Salon

This case study offers an entertaining way to learn about the possibilities of stream processing, which can be applied to projects in fields that require easy access to current information, such as finance, mobility and energy. We’ll use the Quix platform to set up a series of open source data sets and code samples that collect, transform and deliver data under a machine learning model that learns to handle real-time heart rate data. We’ll show how to include complex transformations to the data, such as how to calculate calories burned with Python.

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Tomas Neubauer is cofounder and CTO at Quix, responsible for the technical direction of the company across the full technical stack, and working as a technical authority for the engineering team. He was previously technical lead at McLaren, where he led architecture uplift for Formula One racing realtime telemetry acquisition. He later led platform development outside motorsport, reusing the knowhow he gained from racing.

Javier Blanco Cordero is a senior data scientist at Quix, where he helps customers get the most out of their data science projects. He was previously a senior data scientist at Orange, developing churn prediction, marketing mix modeling, propensity to purchase models and more. Javier is a master's lecturer and speaker specializing in pragmatic data science and causality.