Hackathons provide an opportunity for a group of individuals to work toward a common goal – a “hacked-together” software product – in a short amount of time. Usually in-person events, the Hackathons have a social component as well as an educational or creative one. Hackathons might focus on particular target participants, such as college students, or particular data sets, such as the Allen Brain Atlas. NIH held its first Hackathon on campus in January (see prior blog post) using data housed at NCBI, and the second one is currently going on.
Missed it? Not a problem -- there are more opportunities. In fact, here’s one, a DaSH – Data Science Hackathon. The NIH Big Data to Knowledge (BD2K) program and the NIH Library are pleased to join the Johns Hopkins (JHU) Bloomberg School of Public Health Department of Biostatistics in announcing the first JHU DaSH on September 21-23, 2015 in Baltimore, Maryland.
The organizers– Drs. Brian Caffo, Leah Jager, Jeff Leek and Roger Peng – include JHU professors who teach the popular Coursera Data Science Specialization. This Data Science Hackathon will provide an opportunity for hands-on training that reinforces and builds on data management and analysis skills such as those covered in the MOOC specialization (completion of the specialization is not a prerequisite).
“This event will be an opportunity for data scientists and data scientists-in-training to get together and hack on real-world problems collaboratively and to learn from each other. The DaSH will feature data scientists from government, academia, and industry presenting problems and describing challenges in their respective areas. There will also be a number of networking opportunities where attendees can get to know each other.”
-- Simply Statistics blog post
JHU DaSH will provide a local opportunity for NIH scientists and trainees to participate in a data science Hackathon. For more information, see https://regonline.com/jhudash. NIH staff or trainees who would like to attend should complete the application at https://www.surveymonkey.com/r/NIH-JHUDaSH (no later than Aug 14th) rather than the one on the https://regonline.com/jhudash website. For questions, contact Lisa Federer (firstname.lastname@example.org) in the NIH Library. Non-affiliates of NIH should apply directly through https://regonline.com/jhudash.