Occasional writings and programming exercises...
We had another successful Hackathon at CUMC this weekend-you can read about it at our club's google site! Though it was a small group, we made progress on personal projects, including planning for developing a programmable robot!
We had another successful Hackathon today at CUMC-you can read about it at our club's google site! We worked on diverse projects ranging from following tutorials, using new packages, and on creating a web framework to visualize our own lab data. We then went to a taco bar afterwards! So fun!
We had another successful Hackathon today at CUMC-you can read about it at our group's new google site! We decided to switch platforms to allow for inclusive collaboration among members i.e. they can edit the site however is useful. I'm very pleased to see how our community is continuing to grow and reshape to better serve the CUMC graduate student community.
We had another successful Hackathon today at CUMC-you can read about it at our group's site to see all the progress being made! I'v very pleased to see how our community is growing.
I've been reading about bayesian thinking recently, which means assessing the chances of a given view of the world based on the data you observe from the world. I've decided to make notebooks along with me learning about this topic, especially after reading Think Bayes by Allen Downey (great book btw!). Here's the link to the first notebook on this journey. I hope to be expanding and making more notebooks corresponding to this topic. It's definitely helping me to understand how to think like a bayesian and conduct a very simple example initially. Hope you find it useful!
In this talk, I go over some of the tools, libraries, and analyses that I use and the wider community uses when doing biomedical data science. You can see the talk here. Enjoy!
As part of the Columbia University Neuroscience Outreach, I gave a talk at Late Night Science. You can view the talk here. Enjoy!
This R for Data Science solutions was produced through a study group with Jason Baik at Carnegie Mellon University. Thank you to the New York Open Statistical Programming MeetUp for letting us use a private repository and slack channel while completing this. I wanted to do this for two reason: 1) was to get an overview of the tidyverse and associated packages, and 2) was to provide a solutions guide that fully matches with the chapter numbers and exercises. Feel free to use this while you are reading R for Data Science as well as modify or expand the Rmarkdown file to add your solutions and correct my mistakes (though I fully recognize I heavily relied on Jeffrey Arnold's solutions).
Check out the group's site to see all the progress being made!
Hello everybody! Thanks for visiting my very under construction site. I'll be adding more notebooks and blog posts in the future. Hopefully they'll be useful and readable. Stay tuned!
Finding Columbia University author's research area using Bayes Classification. A fun weekend project...