How we amplify privilege with supervised machine learning
Michael Williams uses sentiment analysis to show that supervised machine learning has the potential to amplify the voices of the most privileged people in society.
Perils of big data
Ideas and resources for understanding the perils of data.
Stories from the trenches: The challenges of building an analytics stack
Fangjin Yang and Xavier Léauté describe how they architected their analytics stack around Druid, and overcame the challenges around scaling the system, balancing features with cost, and making performance consistent.
Unraveling Möbius strips of edge-case data
Connecting data scientists with domain expertise makes more of the unknowns known.
Recognizing and rescuing a failing big data project
Common pitfalls and best practices every manager should know.
Suppressing false alerts in data security
How to prevent false positives from limiting intrusion detection systems
10 problems with qualitative data
Pitfalls to avoid when you're dealing with qualitative information.
Haunted by data
It's time to tackle data's disposal problem. Maciej Ceglowski makes the case for adopting enforceable limits for data storage.
Privacy protection and reproducible research
Daniel Goroff discusses recent mathematical ideas, like differential privacy, that offer new ways of reaching robust conclusions while protecting personal information.
Year Zero: Our life timelines begin
In the next decade, Year Zero will be how big data reaches everyone and will fundamentally change how we live.