At the beginning of April, I attended Big Data TechCon, a meeting aimed at teaching big data tools. I definitely learned a lot, in particular about the pitfalls in big data analytics. It was good to meet people from vastly different industries who were all united by the fact that they had some kind of ‘big data’ in their field. I attended the meeting with a free pass from KDnuggets, which I won by participating in a data science poll organized by Gregory Piatetsky-Shapiro – thanks a lot! Continue reading
On January 24, I attended a 1-day data science symposium at Harvard University with the fun title ‘Weathering the Data Storm‘. I imagine being in a tiny boat on the endless beautiful sea of data, and then a big data storm comes up! Numbers and pieces of text fly through the air… they hit me hard in the face like hail, pile up in my boat… and I’m in dire need of some clever algorithms to take care of all that data, so that I won’t get hurt, my boat won’t sink! Continue reading
Awesome scikit-learn machine learning algorithm cheat sheet, by Andreas Mueller.
On Jan 14, I attended a Big Data event for librarians, Big Data & You: Preparing Current and Future Information Specialists, organized by NEASIST (New England Chapter of the Association for Information Science & Technology). I hadn’t really thought about it before, but it’s obvious that ‘Big data’ is hitting the field of library science, too. Continue reading
On Jan 7, I attended the Critical Data conference at MIT (an event coupled to the Critical data hackathon the weekend before). It was all about big data in healthcare, with speakers from both the medical and the data science communities, and from both academia and industry. Everyone agreed that there is great potential in the enormous amounts of data than can – and are – collected to improve the current healthcare system. Continue reading
The weekend of Jan 3-5, I participated in the Critical Data Hackathon at MIT – a weekend that brought together clinicians and data scientists to make use of an awesome medical database called MIMIC (Multiparameter Intelligent Monitoring in Intensive Care). It contains 200GB clinical data from 40,000 ICU stays, with matched physiological signals for 7,000 patients – a lot of data to play with! Continue reading