Blog: Data Science Insights

Simple Random Sampling: Not So Simple

Kellie Ottoboni / February 3, 2017

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

Laura Norén / February 3, 2017

Defining Diversity and Inclusivity

Automatic Differentiation and Cosmology Simulation

Yu Feng / February 3, 2017

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

Gender Issues Roundtable Discussion: A Case Study in Uncomfortable Conversations

Kellie Ottoboni / December 14, 2016

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

With Data, Bigger Might Not Always Be Better

Jasmine Nirody / March 21, 2016

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

Lorena Barba / March 8, 2016

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

Kyle Barbary / February 29, 2016

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

Kellie Ottoboni / February 16, 2016

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.

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