Blog: Data Science Insights

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

A Beginner's Guide to Diversity and Inclusivity

Defining Diversity and Inclusivity

Automatic Differentiation and Cosmology Simulation

One project at Berkeley Center for Cosmological Physics studies the reconstruction of the cosmic initial condition based on observations of the later-time universe.

Visualizing Publication Bias: The Case of Funnel Plots

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Gender Issues Roundtable Discussion: A Case Study in Uncomfortable Conversations

by Kellie Ottoboni, Rebecca Barter, Ryan Giordano, Sara Stoudt

Moderating Harassment in Twitter with Blockbots

With Data, Bigger Might Not Always Be Better

Every conversation I’ve heard about what it means to be a data scientist consists of tons of ideas but no consensus. While it seems like nobody can agree on the sufficient conditions for obtaining this illustrious title, a lot of people are vehement about a necessary one: being a data scientist means you work with big data.

Computational Thinking: I Do Not Think It Means What You Think It Means

BIDS visiting scholar Lorena Barba posted a great blog post on computational thinking and was kindly willing to let us cross-post on our blog. Check it out below.