PyData Meetup @ Jumbo: Solving Business Problems with Python

It is time for another edition of the PyData Eindhoven meetup. Join us on the 25th of May at Jumbo for an evening of technical talks for technical people. We are organising this event around the theme: Solving Business Problems with Python.

Google maps pin for the parking lot: Parking at Jumbo

17:30 - Doors Open
18:00 - Welcome
18:05 - Welcome from Jumbo
18:10 - Use of Federated Learning to protect the most vulnerable newborns by Zuzana Leova from IBM
18:40 - Dinner
19:15 - Measuring the effectiveness of product assortment changes in retail with Causal Impact modeling by Nishant Jain from Jumbo
**19:45 - How Bol.com is optimizing their logistics network capacity by Christianne Wisse from Bol.com**
20:15 - Wrap/up20:20 - Networking Drinks
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Use of Federated Learning to protect the most vulnerable newborns by Zuana Leova from IBM
Description: Every second counts in the neonatal intensive care unit (NICU). The clock starts ticking the moment an infant is born, with neonatologists racing to identify and address potential healthcomplications. The earlier these doctors can detect a critical illness, the sooner they can intervene, start treatment and save precious lives. Given the nature of healthcare data, it is critical to ensure the privacy and protection of patient information. The goal is to show that, through IBM‘s Federated Learning tool, multiple healthcare providers can easily and efficiently train an ML model that leverages all their respective data without sharing sensitive information.
Presenter:https://www.linkedin.com/in/zuzana-leova-1a015125/
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Measuring the effectiveness of product assortment changes in retail with Causal Impact modeling by Nishant Jain from Jumbo
Description: Retail assortment managers often face the challenge of making effective product assortment decisions without reliable measures of their impact. Ineffective decisions can result in lost revenue and frustrated customers. A/B testing is not always an option, but there is another way to measure the effectiveness of changes to product assortments: Causal Impact time series modeling. In this talk, we'll discuss how we used the Causal Impact model to measure the impact of product assortment changes in a category at Jumbo. We'll provide context for why measuring the effectiveness of product assortment changes is challenging and how Causal Impact modeling can help. We'll explain the basics of the model, its advantages over other approaches, and any limitations or assumptions. We'll also describe our approach to using the model, including the data we used and the independent variables we included. By the end of this talk, you'll have a better understanding of how to use Causal Impact modeling to make more informed product assortment decisions in retail.
Presenter:
https://www.linkedin.com/in/nishant--jain/
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**How Bol.com is optimizing their logistics network capacity by Christianne Wisse from Bol.com**
Abstract: The complexity of the logistical landscape at [Bol.com](http://bol.com/) evolved heavily over the recent years. In this talk, we will explain how we use optimization techniques to optimize our logistics network.
Description: Our logistics operations have grown tremendously over the past years, currently shipping roughly 500.000 items per day from one of our five warehouses. To make sure these items can be processed according to the customer promise, we optimize our warehouse capacity by balancing between excess capacity and costs.
Because of this enormous complexity of our logistics network, we developed a model that optimizes the stock distribution over our warehouses.
In this hands-on talk, I’ll explain how we optimize capacity over the entire logistics chain. Along the way I’ll debate on how we innovate and test our systems, but also explain our lessons learned and the remaining challenges upon us.
Presenter:
https://www.linkedin.com/in/christiannewisse/
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Attendee profile:
* People who want to learn about Python, Julia, R in the context of Data
* Interested in seeing how others use Python for solving real problems
* Interested in being part of the Python Community in Eindhoven
* No Recruiters
* No Sales
* Only people who speak Data