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Community Chat on Gitter

In order to facilitate easy contact between data enthusiasts in the region we have an integrated community chat hosted on Gitter where you can easily interact with your peers. Sub-channels exist for focused discussion on specific themes. Please come and join the conversation in our Gitter chat.

Monthly Meetups

We also organise a monthly meetup where we mix and mingle in person, have a good time and support each other in all data related themes. Come, learn, connect and have a good time! You can sign up and find more information about our upcoming meetups here.

Community Members

Below is a list of community members. If you would like your name listed, either make a pull request directly on our Github repository or send the information you want to add via e-mail to Arno. (Listing your name here is of course entirely optional and voluntary.)

Name Position/Role Interests Picture
Arno Angerer Master student with focus on Business Analytics/Data Science at the Maastricht University School of Business & Economics R, Predictive Modeling, Machine Learning, Data Visualization Arno
Niels Hameleers Data Scientist at Maastricht University Python, Jupyter Notebooks, Health Services Research, interested in machine learning  
Praveen Koshy Sam Master student in Data Science for Decision Making at Maastricht University Department of Knowledge Engineering Python, Jupyter Notebooks, TensorFlow, Machine Learning, Bayesian Inferencing, Theoretical chitter-chatter  
Kody Moodley Postdoc at Institute of Data Science, Maastricht University Python, R, Jupyter Notebooks, Machine Learning, Automated Reasoning, Ontologies, Data Science in the legal domain Kody
Paul Kleinschmidt Master student with focus on Business Analytics/Data Science at the Maastricht University School of Business & Economics R, Python, Machine Learning, Deep Learning, Kaggle  
Amrapali Zaveri Postdoctoral researcher at Institute of Data Science, Maastricht University Linked Data, Data Integration, Data Quality, Crowdsourcing, Machine learning, Biomedical (meta)data analysis and quality Amrapali
Frederik Calsius Bachelor student Data Science and Knowledge Engineering at Maastricht University Python, Java, Machine Learning, Data Science  
Ibrahim Hashim Research assistant at DeWeerd Lab Python, Machine Learning, Data Science, Aritifical Intelligence  
Indra Gesink Master student in Econometrics and Systems Biology at Maastricht University Machine learning, Darwinian approaches, like self-play. Making life easier and interesting with Data Science, Modeling  

Code of Conduct

1) We treat each other with respect

Our community members come from diverse backgrounds and many different scientific disciplines. This implies that we may be accustomed to different methods and approaches to achieve similar outcomes. Please come with an open mindset and be open that others may approach problems in a different way. We are all hear to learn and grow from and with each other.

2) Remember: Meetup time is fun time

We believe to foster a healthy attitude towards growth and learning it is important to create the right kind of atmosphere. If we enjoy what we are doing and can approach Data Science with a relaxed, even playful state of mind it is much easier to stay motivated and deepen our learning. Playfulness is also a great creativity booster and aids in problem solving, one of the most essential qualities for a Data Enthuiast to have.

So we want to practice data science in a joyful way, free of judgement. And once we enjoy what we are doing, no matter where we currently are, we will naturally want to do more of it and become better in the process. Simply come as a curious mind, eager to learn and make some new friends.

3) You are a perfect fit for the community, exactly where you are at this moment

Whenever you interact with our Data Science community and join the Meetups, we warmly encourage you to leave all your worries, fears and inhibitions behind. There is no need to be afraid if you are a beginner still – everyone starts small. Neither should you be afraid if you are already considered an ‘expert’ but feel you don’t know enough. No matter where you are at this moment. You are exactly in the perfect place to start going deeper.

There is a lot to learn, but don’t feel overwhelmed. Start with a minimal subset of useful skills and build up over time. To paraphrase the Zen monk Shunryu Suzuki: “Each level of skill is perfect the way it is — and it can use a little improvement”. (This sentence is heavily inspired by Hadley Wickam who was speaking about R packages at the time. Yet I feel it fits equally well here.)

Welcome to our joyful Data Science community!