The Tow Center provides journalists with the skills and knowledge to lead the future of digital journalism, in part by working with Columbia University’s Graduate School of Journalism faculty to integrate innovative digital reporting and presentation into Master of Science and Master of Arts Columbia Journalism School courses. We bring audiences, participatory journalismm and data visualization into the classroom. The Tow Center is actively defining areas of interest and scholarship at the intersection of journalism and technology. The following courses are taught by Tow Center staff and Tow Fellows.
Frontiers in Computational Journalism is a class offered to students in the dual master of science degree in journalism and computer science program. The course aims to familiarize students with current research and development within computer science that is directly relevant to journalism, so students will become more capable of participating in the design of future public information systems. The course is built around a “design” frame that examines technology from the point of view of its possible applications and social context. Students will learn about both the major unsolved problems of internet-era journalism, and the major areas of research within computer science that are being brought to bear on these problems. The scope is wide enough to include relatively traditional journalistic work, such as computer-assisted investigative reporting, and the broader information systems that we all use every day to inform ourselves, such as search engines. The course will provide students with a thorough understanding of how particular fields of computational research relate to products being developed for journalism, and provoke ideas for their own research and projects. Research-level computer science material will be discussed in class, but the emphasis will be on understanding the capabilities and limitations of this technology. Students with a CS background will have opportunity for algorithmic exploration and innovation, however the primary goal of the course is thoughtful, application-centered research and design. Read the class blog.
This course teaches students how to evaluate and analyze data for appropriateness, context and meaning. Students leave the class knowing how to apply basic statistical methods to numerical data sets, and interpret the results of these methods using appropriate measures of spread and central tendency. They will know how to build a histogram, recognize normal and skewed distributions, calculate the mean/standard deviation/normal score and median/quartiles where appropriate, and understand how these can (and cannot) be used to infer meaning about particular readings within a given data set. Visualization in this course will be used primarily for data analysis and story formation; graphics for publication will be addressed in the spring.
The goal of this class is to enable students to gain a deeper and more practical understanding of the new ways that digital platforms enable journalists to interact with users and sources. This will be done via a hands-on project in which small groups of students will be given a topic in the news, and then they will be expected to create a web site and other digital elements, using social media, aggregation and other methods to extend the reach and impact of their work. Students will work collaboratively and imaginatively, highlighting existing coverage from other sources as well as developing their own original journalism.
Columbia University Graduate School of Journalism offers digital courses that are not taught by Tow Center faculty. In Fall 2013 these include:
Anyone who works in a newsroom today — reporters, web producers, researchers – needs to understand the fundamentals of producing journalism for the Web. From the Huffington Post to the Washington Post, multiple tools are used to serve readers’ immense appetites and create new narrative forms. Aggregation has become the go-to reporting method when journalists’ access is restricted, as we saw almost two years ago during the “Green Revolution” in Iran or more recently in the Arab Spring. Students who take this class will become proficient in the basic methods of aggregation, learning how to assess and assimilate information. Students will learn how report and produce journalism for the web using tools such as Twitter and YouTube, and Storify. The course will also cover the doctrine of fair use and the legal limits of these tools.
Data Visualization II is designed to give students who have taken and passed (and hopefully enjoyed) an introductory course on Statistics a more advanced treatment of the process of storytelling with data. This includes: Frameworks and tools for finding, accessing, manipulating and publishing data (APIs, various databases, and some techniques for data “cleaning”); simulation-based approaches to statistical inference when data have special designs (surveys, A/B testing); “models” for data and the stories they tell (regression, trees); and advanced tools for visualization (to explore both data, the effects of data processing, and models). Throughout we will emphasize best practices for documenting your code and analysis (“showing your work”).
In this course, students will learn the basics of video journalism: gathering sound and picture simultaneously; the fundamentals of exposure and composition; the grammar of video, writing to picture, selecting sound bites, the basic concepts of non-linear editing. By the end of the course students should have a foundational understanding of the basic skills involved in video storytelling and the ability to produce shortform video pieces. This class (or Video II, described below, for students with prior experience) is a prerequisite for those interested in pitching a video-hybrid master’s project or registering for any of the following spring classes: Video Storytelling; Nightly News and Multimedia Storytelling. (Please note that registration for this class is not a guarantee of admission into those spring classes or of faculty approval for a video hybrid project.) Students who enroll in this class will be charged a $175 lab fee.
Columbia University Graduate School of Journalism offers quite a few digital courses that are not taught by Tow Center faculty. In Spring 2013 these included:
This course is designed to give M.A. students an understanding of the ways that technology is transforming journalism— from the ramped-up news cycle to high-tech methods of story construction, from the evolving culture of reader engagement to radically changing business imperatives. We also want to create an opportunity for you to grapple with how these changes affect your work, and what means for the industry and for your careers.
During the semester, each student will develop a research blog analyzing the coverage of specific topic of professional interest. You will learn how to delineate the community that is engaged with a subject, map the central debates, and develop creative new approaches to news coverage.
We hope that by the end of the semester every student—even those who intend to work far outside of digital news—will develop a comfort with key technologies and, more importantly, an intellectual understanding of how the role of the journalist is changing. This should enable you to think more deeply and expansively about likely opportunities and challenges in the field.
Columbia University Graduate School of Journalism offers quite a few digital courses that are not taught by Tow Center faculty. In Fall 2012 these included:
A project-based hands-on course on data journalism and information visualization that covers data retrieval and analysis tools, in addition to current approaches to information visualization from a variety of disciplines. (Lore | Bit.ly)
Columbia University Graduate School of Journalism offers quite a few digital courses that are not taught by Tow Center faculty. In Spring 2012 these included: