News

Columbia Journalism Investigations Fellow Talks About Her Collaboration with The Times

The Beat is an interview series with the Journalism School's postgraduate fellows that aims to provide insight into how deeply reported investigations are made. Bridget Hickey, '18 M.S., talks about her recent collaboration with The New York Times on a story about McKinsey and Company, one of the world's most influential management consulting firms.

A Publication For Ambitious Narrative Nonfiction Writers and Readers

The Delacorte Review is the Journalism School's latest publication dedicated to providing readers with new, original works of ambitious narrative nonfiction, often by writers they are reading for the first time. And second, allowing our readers to discover how those stories came to be told.

Image by Casey Chin

How We Got Published in WIRED

Dual Degree students, Shreya Vaidyanathan and Erin Riglin, share how their class project came to be published in WIRED.

Columbia Journalism Names Winners of 2019 Alumni Awards

This year’s winners are leading coverage of Washington developments, they are hosting the most-listened to radio news show, they have spent half a century delivering quality investigative journalism and they are reporting in English and Spanish.

Legal issues in data journalism

Data Journalism and the Law

As data has grown as a driver of reporting, so too have legal concerns regarding the accuracy of information, acceptable methods of information gathering and what is considered proprietary information. This report from the Columbia Journalism Review examines these emerging concerns as well as shifts and gray areas in the law.

How I Got Published in Quartz

Kevin Sun, '17 M.S. Data Journalism, tells how he got published in the global news site while still pursuing his degree at Columbia Journalism School. Includes advice for developing and pitching stories.

Muck data journalism tool

Muck: A build tool for data journalists

In this installment from the data journalism series, we revisit Muck, an ambitious build tool for data analysis projects that makes it easier to create reproducible projects with clean data and comprehensible code, all while allowing for iterative data transformations and expansions to data sets.