This week, I’ll reunite with my California Civic Data Coalition colleagues to co-teach a six-hour, hands-on session at the annual Computer-Assisted Reporting Conference in Chicago.

Our class, “First Python Notebook,” is for journalists who want to learn data science. We’ll show our students how to use computer-assisted techniques to report on money in state politics, but the skills could apply to any reporting beat where data are available.

Why should journalists learn data science? At the end of the day, data science is another well to draw from in finding stories that matter. As the institutions we cover increasingly rely on data and algorithmic decision-making, our jobs will demand more of us than finding compelling anecdotes and getting great quotes.

The specific tools we’re teaching — Jupyter Notebooks and pandas — fit well into the workflows of data-driven journalism. They’re powerful, yet easy enough to set up on your own laptop. The metaphor of a notebook is also something that journalists understand intuitively as a space to interrogate data and explain their findings in plain English.

Los Angeles Times Data Desk Editor Ben Welsh developed our curriculum, which is free to use by anyone. To date, the class has been taught at NICAR 2017, Stanford University, Northwestern University’s Washington, D.C., campus and as a MOOC or massive open online course.

The 2018 CAR Conference is March 8-11 at the Chicago Marriott Downtown Magnificent Mile. Conference sponsors include the Donald W. Reynolds Journalism Institute and the Missouri School of Journalism.