— In the News —
In a sign of the times, UC Berkeley was recently gifted 252 million dollars for the construction of a new "Data Hub." The anonymous donation is the single largest gift in Berkeley’s history. An additional 300 million dollars will be raised to complete the center. Here's what they're doing.
— Tools and Techniques —
Game theory is experiencing a renaissance that's been driven by the evolution of AI. In this article, Jesus Rodriguez of Invector Labs walks-through classic and new ideas that data scientists should be aware of.
Spotify's internal library called "Lexikon" simplifies data discovery and helps Spotify's data teams make faster and better decisions. This is a great deep-dive into the use-cases, decisions, and iterations that drove the development of Lexikon and its success.
Aleph was developed for use by journalists in cross-border investigations. Think of it as a search engine for digging into disparate data sources. Looking for answers in a trove of PDFs, emails and Word documents? This is a good project to know about.
This survey paper describes what is known to date about the famous BERT model, synthesizing over 40 studies. It also provides an overview of proposed modifications and directions for further research.
Alegion Flex is the first ML data labeling solution designed for data science teams in their experimentation phase. It provides the flexibility and predictability needed for teams to focus on rapid iteration, not just on production initiatives. See how Alegion supports faster experimentation, unmatched flexibility across use cases, and better control over data labeling costs compared to traditional solutions.
— Data Viz —
Using coronavirus data as a vehicle, this article by Kenneth Field explores common issues and best practices for visualizing map-based data. Covers projections, choropleths, symbols, and things to avoid.
Observable and Jupyter are both computational notebooks for doing data science and visualization but they're completely different under the hood. This tutorial walks-through the biggest differences to help Jupyter users make the most of Observable.