Blog: Data Science Insights

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.

The Joy of Code Refactoring

If you write software for your research, you have most likely had the experience of looking at your code and realizing it has become a tangled mess. Perhaps it has even gotten to the point where you, the original author, have a hard time remembering how all the pieces fit together. Don’t despair! This is perfectly natural in research software; it is just time to refactor.

Bringing Data Science Back to Statistics

One of the sessions that I attended at the 2015 Moore-Sloan Data Science Environment Summit was titled "Isn't Statistics Part of Data Science?" It is a niggling question I often consider, especially given how few statisticians there are at BIDS. A group of about forty people from statistics, computer science, and applied domains convened to discuss differences in practice and culture that divide statistics from data science. I am an applied statistician and a fellow at BIDS straddling these two worlds. I find it difficult to identify the line dividing these roles.