Jeroen Janssens


Jeroen Janssens is an independent data science consultant and certified instructor. He enjoys 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.

Jeroen runs Data Science Workshops, a training and coaching firm that organizes open enrollment workshops, in-company courses, inspiration sessions, hackathons, and meetups. Clients include Amazon, Apple, eHealth Africa, KPN, Schiphol Airport, The New York Times, and T-Mobile.

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, published by O’Reilly Media. 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. For more information about Jeroen’s experience and education, download his CV.


Jeroen is available to provide consulting and training in the areas of data science, data engineering, and machine learning. He’s also available to speak at private and public events. If you would like to know more about his services, fees, and availability, then please email Jeroen. You can also find him on Twitter, GitHub, and LinkedIn.


  • Embrace the Command Line. My three-week cohort-based course. Next cohort starts on September 12, 2022.

  • Data Science NL. A community with over 3,000 members dedicated to sharing knowledge about the exciting multidisciplinary field of data science.

  • Data Science Toolbox. A batteries-included Docker image for polyglot data scientists.

  • raylibr. An R package that wraps Raylib, a simple and easy-to-use library to enjoy videogames programming.

  • tmuxr. An R package for managing tmux and interacting with the processes it runs.

  • scikit-sos. A Python implementation of the Stochastic Outlier Selection algorithm.

  • sample. Filter lines from standard input according to some probability, with a given delay, and for a certain duration.



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