We’re thrilled to announce that, after a three-year hiatus, we’re back with another event. On June 20, the 10th Data Science NL Meetup will take place at Picnic in Amsterdam.
We have three great speakers who will kindly share their data science projects and experiences with us. We’re also excited to introduce Community Announcements. This will be an opportunity for you to mention something you think is of interest to the audience. For instance, a vacancy at your team or an open source project you’ve been working on. And last but not least, you get a chance to win a book from O’Reilly in the two book raffles.
Many thanks to Picnic for sponsoring and hosting, to O’Reilly for sponsoring the book raffle, and to AIgents for co-organising. Below you’ll find the details, the programme, the talk abstracts, and the speaker biographies.
- Date: June 20, 2019
- Location: Picnic in Amsterdam, the Netherlands
- Included: Food and drinks
- Price: Free
- Sign up: On the meetup page
- 5:30 PM: Walk-in with food and drinks
- 6:30 PM: Introduction by Jeroen Janssens
- 6:35 PM: Community Announcements
- 6:40 PM: Talk 1: “Data Science in the Humanities” by Marieke van Erp
- 7:15 PM: Book Raffle 1: “Data Science from Scratch” by Joel Grus
- 7:20 PM: Talk 2: “Searching to be Entertained” by Daan Odijk
- 7:55 PM: Book Raffle 2: “Python for Data Analysis” by Wes McKinney
- 8:00 PM: Talk 3: “AI at Your Service: Supporting Customer Success With NLP Techniques” by Sharon Gieske
- 8:30 PM: Drinks
- 9:30 PM: End
Abstracts and Speaker Biographies
Talk 1: Data Science in the Humanities
Humanities is a broad research field that covers (but is not limited to) history, literature studies, linguistics and ethnology. Digital methods are becoming more common in day-to-day humanities practice, but these methods don’t always translate seamlessly to the humanities domain. In this talk, I will present use cases from the Dutch Royal Academy’s Humanities Cluster that show the potential as well as frictions in the application of digital analysis methods in this domain.
Marieke van Erp is a researcher and team leader of the Digital Humanities Lab at the Royal Netherlands Academy of Arts and Sciences Humanities Cluster in Amsterdam, the Netherlands. Her research is focused on applying natural language processing in semantic web applications with a particular interest in digital humanities. She previously worked on the European NewsReader project, which was aimed at building structured indexes of events from large volumes of financial news and the CLARIAH project, a large Dutch project to develop infrastructure for humanities research.
Talk 2: Searching to be Entertained
RTL, the largest commercial broadcaster in a declining Dutch TV market, is making a transition from a traditional TV company to a consumer-focused media company. Daan will share how we are using data science and AI to help our users find the right content for them, ranging from the 1M daily visitors on our news website to the over 2B yearly video plays we have, most of these on our rapidly growing video-on-demand platform Videoland.
Daan Odijk is the lead data scientist at RTL. In 2016, he obtained his PhD on search algorithms for news. Subsequently, he joined journalism start-up Blendle, leading the personalization team. At RTL, Daan leads data scientists and engineers, delivering data-powered products across RTL, including personalization for RTLNieuws and Videoland.
Talk 3: AI at Your Service: Supporting Customer Success With NLP Techniques
Picnic is the world’s fastest growing online supermarket with a focus on accessible and personal customer service. With growth at this scale, support agents must be able to handle an ever-increasing volume and diversity of customer feedback. In this talk, Sharon will share how Picnic uses AI to reduce the workload of support agents without compromising its customer experience.
Sharon Gieske is a data scientist at Picnic, with a strong background in Artificial Intelligence and Natural Language Processing. Her fascination lies with the diverse challenges that user-generated text brings. In her recent work, she leverages machine learning to support customer success in faster resolution of customer feedback.