Blog: Data Science Insights

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

Introducing TextThresher 1.0 beta

After 2 years of building TextThresher, I am very pleased to announce that... A demo is provided below. But first, let me tell you a bit about what TextThresher does, how people use it, and how you can get your hands on it. 

Tales from the Docathon: How to Get Communities to Write Documentation

by Chris Holdgraf and Nelle Varoquaux

Beauty vs. Function: Not a Problem in Superheat

by Kasia Metkowski Data meets narrative in Rebecca Barter’s Superheat, an R package that creates colorful and customizable heatmaps. 

Searchable Datasets in Python: Images across Domains, Experiments, Algorithms, and Learning

Predicting Protest Policing (Research)

The State of Jupyter

This post was originally published at the O'Reilly Ideas site on January 26, 2017. In this post, we’ll look at Project Jupyter and answer three questions:

Simple Random Sampling: Not So Simple

Simple random sampling is drawing k objects from a group of n in such a way that all possible subsets are equally likely. In practice, it is difficult to draw truly random samples. Instead, people tend to draw samples using