11th Rotterdam Data Science Meetup – Applied Data Science in Person

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11th Rotterdam Data Science Meetup – Applied Data Science in Person

25 January 2023 @ 17:30 20:30


Hello AI and DS enthusiasts!
We would like to invite you to our 11th DS Rotterdam Meetup on Applied Data Science. We are excited to open the doors and gather again in person.
Please RSVP below
Where: ECDA (Polack building), Burgemeester Oudlaan 50, 3062 PA Rotterdam, The Netherlands
Note that we have a limited number of spots available. So, if you can no longer make it, please let us know as soon as possible – that way we can assign your seat to someone on the waiting list.
17:30 – 17:40: Intro by your hosts
17:40 – 18:00: Talk 1
18:00 – 18:20: Talk 2
18:20 – 18:30: Closing remarks
18:30 – 20:30: Networking with food and drinks

Talk 1: Erasmus Q-Intelligence | Dr. Koen Bel | Demand forecasting for Fast Moving Consumer Goods
Abstract : Accurate demand forecasts lead to better understanding of demand, better storage positions, less wastage and higher profit. In this talk, we dive into the techniques behind demand forecasting, how to explain and handle uncertainty in a real-time application and how to utilize the information from a demand forecasting tool. We focus both on econometric theory and real-life application. How can data science be implemented and have impact in daily practice?

Bio: Koen is data scientist at Erasmus Q-Intelligence. With his background as econometrician, he applies techniques such as random forest, GLM, conjoint analysis and more to topics like demand forecasting, marketing and HR analytics. He combines academic knowledge with practical implementation to get the most out of data analytics.

Talk 2: Atos | Bram Miedema & Sébastien Naus | Design and productization of NLP- solution

Abstract : Atos is handling a large project at a manufacturer of aircraft parts in Denmark. This project automates the processing of Requests for Quotations coming from customers around the world. These requests come via different channels and in free text form. Atos has developed a cloud-native serverless application and a suite of Machine Learning models that automate the end-to-end RFQ flow using Natural Language Understanding. The business context and functional flow of the solution will be introduced before putting an emphasis on the Machine Learning models created and their operationalization using fully-automated MLOps pipelines.

Bio: Bram Miedema designs and matures data driven solutions for several delivery projects and customers within Atos. He has been part of the team since the start of the project and saw through to make the total system is designed to meet industry standards, maturing it, and keeping it maintainable. Bram has a background in Bioinformatics. From the start of his career Bram worked on the many aspects of making AI model adaptation more easy, robust, and maintainable towards the future.
Sébastien Naus is the solution lead and Machine Learning engineer on the project. He has a broad interest in all aspects of data science and machine learning with a specific emphasis on the operationalisation of ML models (MLOps). Sébastien has a background in Engineering Physics with a speciality in semiconductor electronics. He started his career as a consultant in the telecoms sector before moving towards data science. For the past 4 years, he has worked at Atos as a data science consultant, delivering data solutions to large companies across Northern Europe.

Erasmus University Rotterdam

Burgemeester Oudlaan 50
Rotterdam,South Holland3062 PANetherlands
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