Journalism in the 21st century involves finding, collecting and analyzing data for storytelling, presentation and investigative reporting. The journalism school offers a Master of Science in Data Journalism for students interested in advanced skills.
Who Should Apply
The M.S. in Data Journalism is a three-semester program that provides the hands-on training needed to tell deeply reported data-driven stories in the public interest.
The current era needs journalists able to extract stories and meaning from data and massive information flows. This new degree trains students to be confident about using data in furtherance of the journalistic mission.
Applicants do not need to have experience with data or computation to enroll in this program. All students are required to attend foundational courses (see below) that allow those with no data experience to hone their skills in data acquisition, extraction and analysis.
Students who enroll in the data journalism degree are not eligible for admission to the Stabile investigative or documentary programs. They have the option, however, to take investigative, video and other classes.
Data Journalism students begin their program in mid-May, taking foundational computational and data courses as well as a course on the fundamentals of reporting. In the fall, they continue honing their journalistic skills with the core course, Reporting II, along with Writing with Data, and the Data, Computation, Innovation I workshop, where they will explore cutting-edge storytelling using data and computation. In the fall, students begin work on the Master’s Project, a substantive piece of data-driven journalism that they are expected to complete in the spring. They also join the Master of Science students in taking a suite of courses called Journalism Essentials, which covers the business, historical, legal and ethical issues of the field. In the spring, they take two, 15‐week seminar and production courses with the Master of Science students. One will have a data focus and the other can be any subject/medium. They also take Data, Computation, Innovation II and finish the Master’s Project.
Semester 1 - Summer
Foundations of Computing
The course is an introduction to the ins and outs of programming and data analysis using the Python programming language, with which students will build a foundation for future coding-intensive classes and journalistic work. After this course, students will be able to find and execute solutions to most coding- or data-related problem they encounter in the newsroom. The course focuses on cleaning and analysis using the Python programming language, the command line, Jupyter Notebooks and the data package pandas.
Data & Databases
Students will become familiar with a variety of data formats and methods for storing, accessing and processing information. Topics covered include comma-separated documents, interaction with web site APIs and JSON, raw-text document dumps, regular expressions, SQL databases and more. Students will also tackle less accessible data by building web scrapers and converting difficult-to-use PDFs into useable information.
Data Analysis Studio
In this project-driven course, students work on their own projects and learn everything from obtaining and cleaning data to data analysis and final presentation. Data is explored not only as the basis for visualization, but also as a lead-generating foundation, requiring further investigative or research-oriented work. Regular critiques from instructors and visiting professionals are a critical piece of the course.
In this introductory reporting course, each student will be assigned a beat and will be expected to produce news stories on deadline. Students will learn to think like reporters and to practice the core skills of the trade: developing sources, conducting interviews, structuring a story, writing clearly and getting the facts right. As data journalists, they will also seek out and analyze data, both to deepen their reporting and to identify promising leads. In this way, the tools and techniques learned during the summer will be immediately applicable as data students begin to develop a journalistic mindset and the capacity to find and produce journalistic stories.
Semester 2 - Fall
Students will continue to learn how to apply their data and computational skills to real-world journalism. They will hone their ability to construct a narrative from both quantitative and qualitative sources, how to think critically, how to report under deadline and how to document so that others can replicate and critique their work.
In this class, students will produce polished reports that mix qualitative and quantitative observations and analyses and that include "backstory" pieces describing the computation they performed and the basis for the inferences they have drawn in the story.
Data, Computation, Innovation Workshop I: Storytelling with Data
Semester 3 - Spring
Students in the Data Journalism track choose from the many spring Seminar and Production classes that are open to all students. But they are also required to take one data-focused Seminar and Production class.
Data, Computation, Innovation Workshop II: Algorithms
Machine learning and data science are integral to processing and understanding large data sets. Whether you're clustering schools or crime data, analyzing relationships between people or businesses, or searching for a needle in a haystack of documents, algorithms can help. Students will generate leads, create insights, and evaluate how to best focus their efforts with large data sets. Topics will include building and managing servers, linear regression, clustering, classification, natural language processing, and tools such as scikit-learn and Mechanical Turk.
The following are 15-week data classes offered in the spring semester and open to all master's students:
Please note: The classes listed here represent recent offerings at the Journalism School. Choices vary each semester depending on faculty availability and other considerations. Classes described now may change or be dropped to make room for new additions.