Blog: Data Science Insights

2018 ImageXD Workshop – using images to cross science boundaries and domains

BIDS hosts the inaugural GraphXD workshop

During spring break (March 27–29, 2018), BIDS hosted the first annual Graphs Across Domains (GraphXD) workshop. Lauren Ponisio

UC Berkeley Celebrates Love Data Week with Great Talks and Tips

Love Data Week - February 12-16, 2018

The University Library, Research IT, and Berkeley Institute for Data Science (BIDS) invite you to a series of events on February 12th-16th for Love Data Week 2018. Love Data Week is a nationwide campaign designed to raise awareness about data visualization, management, sharing, and preservation.

Automatic MCMC hyperparameter sensitivity measurements in Stan -- A worked example

We will now follow up our previous post on automatic MCMC hyperparameter sensitivity with a detailed use case taken from the Stan examples. All the code to produce the results below, as well as more examples, can be found in the examples folder on the rgiordan/StanSensitivity git repo. This simple, real-life example has a number of interesting features:

Automatic MCMC hyperparameter sensitivity measurements in Stan

A Bayesian approach to statistical modeling comes with many advantages. For example, it's the only logically coherent way to model uncertainty of parameter estimates! Being Bayesian has never been easier than it is now, thanks to high-quality, easy-to-use automatic tools like Stan.

Binder 2.0 has arrived!

The Berkeley Institute for Data Science (BIDS) is pleased to announce the release on Binder 2.0! Binder is a collection of technology that makes it easy to create sharable, interactive computing environments that run in the cloud. It makes it possible to run a server that enables users to specify their computational environment with commonly-used text files (such as requirements.txt for python users).

Most developers think we should spend more time on documentation

Farewell and Thanks to BIDS

As today marks my last day in the role of Executive Director for the Berkeley Institute for Data Science (BIDS), I’d like to take the opportunity to reflect on BIDS’ journey so far and to offer my thanks to the countless people who have worked so hard, and who continue to advance a data science approach to research. It has been an honor and a privilege to work with such an incredible community, and to help establish BIDS as an active, thriving - and growing! - research organization here on the UC Berkeley campus. 

Graph Clustering Algorithms