I am biomedical scientist graduate student working at CUMC.

I'm starting to do data science/bioinformatics in my work, I do informatics work on a regular basis, or I just want to learn more about data science/bioinformatics/[fill in the blank] topic...

Where do I start, where can I do data sciece/bioinformatics with other students, or how can I help others on their journey?

How do I keep motivated and moving forward without dropping that Coursera course, reading that book, or learning R/Python/Machine and Deep Learning/[fill in the blank] topic?

I hit a roadblock in my data science/bioinformatics journey and Google won't show me the answer (or I haven't dug down that search engine-rabbit hole far enough) -- What now?

AHHHHJEYHTRWBDBVDSF!

Hold up! It's ok, we're here for you! Come to the coding workshop meetings meetings or talk to us on Slack to talk to your community!

• Want to solidify your understanding of a topic by giving a short seminar to others? Post on slack and we'll set it up!

• Want to mentor students on their journey? Be an active contributor of learning resources on this site, post blogs/articles/answer questions on Slack, and/or go to the hackathons to help others in their projects or start a collaborative project!

• Want to learn new skills so you can get a job after you graduate? We can help!

This is Your Group

Beginning

This group was initially formed by Daniel Feller, a former student in the Biomedical Informatics Ph.D. program, back in June 2017. After a two-month run, the group became divided between those already in the data science field and those looking to get into it, as well as learning the theory and math behind methods versus implementing methods.

New Direction

Nick Giangreco, along with Corentin Moevus, decided to re-organize the group to become more inclusive, and catered to those students wanting to learn about and implement data science in their personal life or in their research work.

The group started out giving lessons and presenting topics in introductory R programming, basic methods like dimension reduction (PCA), clustering (k-means), visualization (ggplot2), and supporting hackathons. The hackathons, in particular, showed the interest of CUMC biomedical scientists to learn data science techniques and the value of a community of practitioners coming together to learning, support, and give feedback for the benefit of the individual and the community.