Analytics and Data Visualization for Journalism
NYU School of Continuing and Professional Studies
Wednesdays, 6:45pm-9:15pm, September 25 - December 4
Data-based journalism is increasingly important at all levels of news organizations. This course will introduce you to fundamental concepts and skills for gathering, analyzing, and visualizing data. It’s also a hands-on class; you’ll apply the techniques you learn each week to a semester-long data-reporting project.
The hardest part is learning what to Google. That’s a bit facetious. It’s grounded, though, in the fact that there’s an extraordinary wealth of resources available online but a sharp learning curve. Rather than try to cover the entirety of this constantly growing field, this class will teach you fundamental skills that will make it much easier to learn the rest:
How to think about data like a programmer — and as a journalist.
How to understand common data formats: CSV, XML, and JSON.
How to find preexisting datasets, and assemble your own.
How to do basic, yet fundamental, analysis on data.
How to translate your data and analyses into line charts, bar charts, histograms, and other useful visualizations.
What is required:
A basic comfort with computers, Web browsers, and spreadsheets
An interest in journalism and accuracy
What is not required:
Any fancy spreadsheet skills
Any particular programming knowledge
Any particular background in journalism
All assigned readings in this course are available online, for free. The required and optional readings for each class are noted in the course outline for each class.
Class will take place in a computer lab equipped with Mac computers, so you don’t need to bring a computer of your own. If you’d prefer to use your own computer, that should be fine, too.
Class attendance and participation: 25%. Simple enough.
Weekly assignments: 50%. After each class, except for the final two, you’ll be assigned work relevant to the previous lesson, long-term projects, or both. In total, each week’s assignments should take between 1-3 hours to complete. You’ll be graded both on completeness and insight.
Final project: 25%. Over the course of the semester, you’ll apply the skills you learn to a long-term data-journalism project. The particular shape it takes is up to you, and will likely depend on the particular topic you choose. Possibilities include, but are not limited to: a traditional news/investigative article bolstered by data analysis; a series of well-narrated charts; a deep, interactive graphic. I’ll be available during and outside of class to help you brainstorm topics and approaches. You’ll be graded chiefly on the soundness of your analysis, but also the quality of its presentation.
All students are expected to be honest and ethical in all academic work. This trust is shared among all members of the University community and is a core principle of higher education. Any breaches of this trust shall be taken seriously. A hallmark of the educated student and good scholarship is the ability to acknowledge information derived from others. NYU-SCPS expects that a student will be scrupulous in crediting those sources that have contributed to the development of his or her ideas. Plagiarism is a form of academic dishonesty.
Students with Disabilities
New York University is committed to providing equal educational opportunity and participation for students with disabilities. Any student who needs a reasonable accommodation based on a qualified disability is required to register with The Henry and Lucy Moses Center for Students with Disabilities (CSD) for assistance.
Jeremy Singer-Vine. I’m a reporter and computer programmer at the Wall Street Journal, where I work on data-gathering, data-analysis, and data-visualization for news and investigative projects. Before that, I worked at Slate Magazine as a staff writer and interactive-graphics programmer. I enjoy people, generally, so please don’t hesitate to get in touch with questions, anxieties, et cetera. The easiest way to reach me is via email: firstname.lastname@example.org