What we're about

Data Science NL is a community dedicated to sharing knowledge about the exciting multidisciplinary field of data science. Our community consists of people from businesses, startups, and academia that apply statistics and machine learning to data problems that range from a recommender system for a startup, a solution that reduces costs for an enterprise, to a predictive algorithm that saves lives.

We try to mix hard-core talks with more accessible talks that inspire. Talks will focus more on use cases and less on the tools.

Come and join us if you want to meet peers, learn about cutting edge applications of data science, or see what the sexiest field of the this decade is about while enjoying free pizza and drinks.

Upcoming events (1)

Hybrid Event: Data Science NL meets Data & Drinks

Needs a location

After 2.5 year hiatus, we're back with another event! We're proud to be co-hosting this one with Xomnia. If you'd like to attend this event, please register on Xomnia's meetup page. (You're welcome to register here as well, but it only counts if you've registered on Xomnia's meetup page.)

Program:

  • 5:30 PM: Walk-ins, dinner and drinks
  • 6:30 PM: Introduction by Jeroen Janssens (Data Science Workshops)
  • 6:35 PM: Welcome by Bas Hendrikse (Xomnia)
  • 6:45 PM: Talk 1 by Longhow Lam: Product recommendation and the Dutch movie world
  • 7:15 PM: Talk 2 by Samantha Biegel: Machine learning with limited labels: How to get the most out of your domain expertise?
  • 7:45 PM: Book Raffle
  • 7:50 PM: Talk 3 by Jeroen Janssens: Video Game Programming for Data Science
  • 8:20 PM: Community Announcements
  • 8:30 PM: Drinks
  • 9:30 PM: End

Talks & Bios:

Talk #1: Product recommendation and the Dutch movie world
This talk will discuss two product recommendation approaches, one in a "hobby-project" restaurant setting, and the other in a more serious web/app transactions setting. We'll finish off with an insight into the Dutch movieworld by using some simple graph techniques.

About Longhow Lam
Longhow Lam is a freelance data scientist. He has worked on several projects at different companies. Among others: Customer Churn at Parkmobile, text mining complaints at SVB, predicting customer mortgage default at ING, customer clustering and insights at Pearle.

Talk 2: Machine learning with limited labels: How to get the most out of your domain expertise?
The success of a machine learning project often relies on the availability of good-quality labeled data. Machine learning models learn by seeing examples of the data, and both the number of examples and their quality make a difference in how well the model learns. Therefore, putting effort into obtaining a large number of examples with corresponding labels can be of big help in training your machine learning model. This, however, can be quite difficult in practice.
Samantha will speak about how we can use active learning and weak supervision to turn an unsupervised problem into a supervised one when labeling is difficult.

About Samantha Biegel
Samantha is a Machine Learning Engineer at Xomnia. She graduated cum laude in MSc. Artificial Intelligence and worked on numerous machine learning projects within the energy sector, financial sector and commercial sector. She enjoys working on making machine learning accessible to real-world problems and is passionate about developing trustworthy AI systems for a positive societal impact. These are some reasons why she is also currently working on an object detection pipeline for drone imagery to findrhino poachers in South Africa.

Talk 3: Video Game Programming for Data Science
In this talk, Jeroen will introduce Raylib, demonstrate how to use it from Python and R, and discuss its potential. Raylib is a C/C++ library for programming video games. This includes working with 2D & 3D OpenGL graphics, sounds & music, keyboard & mouse interactivity, and even gamepads & VR headsets. You can use Raylib in many languages, including Python, Julia, and JavaScript. And soon, you’ll be able to use all of Raylib’s functionality directly from R, thanks to a package called raylibr that I’m currently developing. I’m not expecting any Triple-A games, but I do believe that having such functionality is useful, especially for research, education, and simply having fun.

About Jeroen Janssens
Jeroen Janssens, PhD, is a data science consultant and certified instructor. His expertise lies in visualizing data, implementing machine learning models, and building solutions using Python, R, JavaScript, and Bash. He’s passionate about helping and teaching others to do such things. Since 2013, Jeroen runs Data Science Workshops, a training and coaching firm that organizes open enrollment workshops, in-company courses, inspiration sessions, hackathons, and meetups. Previously, he was an assistant professor at Jheronimus Academy of Data Science and a data scientist at Elsevier in Amsterdam and various startups in New York City. He is the author of Data Science at the Command Line (O’Reilly Media, 2021). Jeroen holds a PhD in machine learning from Tilburg University and an MSc in artificial intelligence from Maastricht University. He lives with his wife and two kids in Rotterdam, the Netherlands.

Past events (13)

Data Science NL Meetup meets Beer & Tech

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