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John Lakos 2
Data Science

How Bloomberg is Advancing C++ at Scale

John Lakos manages the Bloomberg Development Environment group, which offers a set of C++ software libraries, development tools, and methodology to well over a thousand Bloomberg developers. In this conversation, Lakos discusses the importance of instilling process and discipline in all software development projects.

Geolocation Twitter
Data Science

Geolocation for Twitter: Timing Matters

Mark Dredze, Miles Osborne, and Prabhanjan ‘Anju’ Kambadur recently published a paper titled Geolocation for Twitter: Timing Matters at the Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), one of the premier conferences on natural language processing. Their paper documents the exploration they’ve done into determining how time and place relate to each other in the Twitter stream.

JupyterLab Screenshot
Open Source

Inside the Collaboration That Built the Open Source JupyterLab Project

Today at SciPy 2016, Bloomberg joined Continuum Analytics and Project Jupyter to reveal the new JupyterLab platform so that early adopters can help test the alpha release. JupyterLab creates a more desktop-like experience on the Web, rivaling expensive software suites that allow programmers to use familiar tricks like keyboard shortcuts, tabs and configurable editor layouts.

Christine Poerschke
Open Source

Bloomberg Female Engineers Commit to Open Source

Increasing participation and diversity in open source software projects is critical to yielding unique and innovative solutions says Bloomberg Regional Engineering Manager, Christine Flounders. This is driving the company to sponsor this year’s WISE International Open Source Award to recognize female contributors of open source projects.

Data Science

Mining Data for the Public Good #D4GX

On September 25, 2016, Bloomberg’s third annual Data for Good Exchange aims to advance the promising field by focusing on the theme of “better governance.” Papers presented at the Exchange will address ways that data science can be applied to solve public interest problems in areas such as justice and fairness, economic development, security and safety and public service delivery.

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