Below are links and descriptions to syllabi from computer assisted and data journalism courses (In alphabetical order of university, then professor’s name).
Arizona State: Evan Wyloge, Telling Stories with Data
Techniques and tools of reporting using data and public records as well as how to use the Internet and other online sources to find information and sources for stories. Students get experience with computer spreadsheets, database software and statistical techniques as they develop their advanced reporting skills.
Arizona State: Evan Wyloge, Graduate Data Journalism
Uses data to tell stories, negotiates with government officials for public records and uses the Internet and other online sources to find information and sources for stories. Uses computer spreadsheets, database software, mapping programs and statistical techniques and explores how these tools can be used across a wide variety of beats and stories.
Birmingham City University: Paul Bradshaw, Data Journalism
Birmingham City University: Paul Bradshaw, Specialist Journalism, Investigations and Coding
Part of the MA in Data Journalism, this module is designed to get students using computational journalism techniques for investigations and specialist reporting, including scraping, network analysis, data cleaning, using company accounts, statistical techniques and immersive storytelling.
Brussels University and IUT Lannion: Laurence Dierickx, Data Journalism
This comprehensive reader includes a history of data journalism and introductions to analyzing, cleaning and gathering data.
Cardiff University: Dr. Aidan O’Donnell, Data Journalism
This is the material and weekly structure for the Data Journalism module taught at Cardiff University’s School of Journalism in the Autumn 2020 semester (MSc in Computational & Data Journalism). The course ran for 12 weeks with a weekly four-hour class and, some weeks, an additional three-hour workshop. Questions and observations welcome at firstname.lastname@example.org or @aodhanlutetiae.
Carleton University: David McKie, Data Journalism Research Methods
The goal of the Data Journalism Storytelling course is to teach students how to find and negotiate for data that is already publicly available, or must be obtained formally or informally through access to information. We will analyse the information using the following: Excel; the data-visualization tools, Tableau Public, Google’s Fusion Tables; the document-annotation software called DocumentCloud; and the mapping program called ArcMap.
Taken together, these skills comprise computer-assisted reporting, also known as data journalism. Our textbook will be, “Computer-Assisted Reporting: A Comprehensive Primer”.
At the end of this term, students will become adept searching for information, analyzing the material for story ideas, or for questions that could lead to stories. They’ll learn how to use Excel to spot trends, and employ data-visualization and mapping programs to show the patterns and allow readers to interact with the material, such as being able to identify the income level or levels of crime in their neighbourhood.
Columbia University: Jonathan Soma, Foundations in Computing
An introduction to the ways in which the computer and data technologies can be partners in creative practices. We will emphasize writing code over point-and-click interfaces, presenting the computer as a programmable object. Through a series of projects, students will move from exploratory sessions, to writing small programs, to sharing code with others in class. They will learn by making, and in the process master a “scripting language” like Python or Ruby. Projects will examine and extend existing technologies in the digital humanities, computational journalism, architecture, and design; and will likely deal in text and images, in human relationships as exhibited through social networks, in map-making, and in reporting.
Columbia University: Allison Parrish and Matthew Jones, Data and Databases
Consideration of both the scientific and social implications of counting, of turning the world into bits. Through the process of gaining fluency in the use of Python, students will spend some time thinking through representations of core “data types” like time, location, text, image, sound and relationships (or networks), and the computational “affordances” associated with each. Students will study several common metaphors for organizing and storing data – from structureless key-value stores, to document collections like MongoDB, to a single table or spreadsheet, to the “multiple tables” of a relational database. We will also discuss ideas behind publishing or sharing data, moving from HTML documents and Web 1.0 to data services and APIs in Web 2.0, to semantics in Web 3.0. These efforts will be project-driven, with students using and building modern data services with a scripting language. Their projects will underscore the reality that data are plentiful and circulate and interact in a kind of informational ecosystem. As researchers, our students will be called on both to access and to publish data products.
Columbia University: Richard A. Dunks, Algorithms
This course presents an overview of algorithms as they relate to journalistic tradecraft, with particular emphasis on algorithms that relate to the discovery, cleaning, and analysis of data. This course intends to provide literacy in the common types of data algorithms, while providing practice in the design, development, and testing of algorithms to support news reporting and analysis, including the basic concepts of algorithm reverse engineering in support of investigative news reporting. The emphasis in this class will be on practical applications and critical awareness of the impact algorithms have in modern life.
Columbia University: Gregory Eirich, Social Network Analysis
The course is designed to teach students the foundations of network analysis including how to manipulate, analyze and visualize network data themselves using statistical software. We will focus on using the statistical program R for most of the work. Topics will include measures of network size, density, and tie strength, measures of network diversity, sampling issues, making ego-nets from whole networks, distance, dyads, homophily, balance and transitivity, structural holes, brokerage, measures of centrality (degree, betweenness, closeness, eigenvector, beta/Bonacich), statistical inference using network data, community detection, affiliation/bipartite networks, clustering and small worlds; positions, roles and equivalence; visualization, simulation, and network evolution over time.
Columbia University: The Lede Program, a data and computing program
The Lede Program offers an intensive summer program in data and computation, or a comprehensive two-semester program for students interested in pursuing more advanced work. The program was designed to help students rapidly elevate their skills in these areas, especially if they were considering applying for Columbia’s highly demanding dual-degree program in journalism and computer science.
Data-N: Kuang Keng Kuek Ser, Foundation Course in Data Journalism
We believe learning by doing is the best way to gain new skills. That is why our course is highly interactive and hands-on. It is conducted through a series of engaging sessions including quiz game, design workshop, group discussion, and step-by-step hands-on exercise on participants’ own laptops using real datasets.
Indiana University: Assistant Professor Gerry Lanosga, Data Analysis for Journalists
J502 is concerned with the collection, analysis and interpretation of data in the pursuit of news. As such, this is not just a skills course. Certainly, you will get an overview of lots of digital tools to find stories in data. But more importantly, this course emphasizes how to think about, contextualize and write about the data we encounter all around us. You will generate your own quantitative analyses of data we obtain, but you will also learn to critically evaluate data analysis by others, such as scientists and interest groups. You will gain an understanding of important statistical concepts, perspective on how social science techniques can serve a journalists, and working knowledge of how to obtain data and conduct analyses using tools such as spreadsheets and databases. While this course is aimed at establishing a working journalist’s competency with data, the skills and concepts you learn will be useful in practically any branch of mass communication you choose.
Kadir Has University: Pinar Dag, Data Journalism: The Basics
Course Objectives: This data journalism course will present fundamentals of open data collecting, gathering, cleaning, analysis, visualization and understanding. Data skills are getting more important to affect data-driven works for the media industry. In the developing internet-based world; to understand and figure out how database journalism turns to data journalism; and as the future of journalism, to teach with open data disciplines how data became a strong role in transferring more efficient growing online resources, tools, and techniques. This course will also address common pitfalls in misinterpreting data.
Kent State University: Prof. Karl Idsvoog, Computer-Assisted Reporting
Course Outcomes: By the time you finish this course, you’ll know how to do basic data calculations using Microsoft Excel and how to do basic data queries on Microsoft Access. Far more important, you will understand that for nearly every story of significance it will be important to request the data.
By the time you finish this course, you will know how to properly frame a public records request when asking for either printed or electronic data. By the time you finish this course, you will improve your ability to negotiate for public records over the phone.
A primary goal, one totally up to you, is to get you thinking and acting like a journalist, not a human microphone stand.
National University of Ireland and University College Dublin: Dr. Bahareh Heravi, Data Journalism
This course is part of MA in Journalism at the National University of Ireland, Galway, and will introduce journalism students to the practise of data journalism and showcase a set of tools and techniques for data driven analysis, investigation and storytelling. Students will learn:
- What data journalism is and where it comes from
- Why is data journalism important
- How to find stories in data
- Where and how to find data
- Investigative Journalism using data
- Cleaning data and making sense of messy data
- Visualising the data and to effectively communicate stories
- Newsroom Math and Statistic
NYU: Prof. Meredith Broussard, Advanced Reporting: Data Journalism
This is the Capstone course. Subject matter varies from section to section, but the basic skeleton of the course is the same across sections: the emphasis is on development of the ability to produce writing and reporting within a sophisticated longform story structure. The course involves query writing, topic research and reading, interviewing, and repeated drafts and rewrites, leading to a full-length piece of writing aimed at a publishable level and the ability of the student to present the reporting orally.
In this class, students will learn the fundamentals of reporting with data and will learn how to find stories in numbers. The final reported project will be a longform piece that incorporates data analysis.
NYU: Prof. Meredith Broussard, Data Journalism
In this class, students will learn to collect, analyze and present data in an immersive, hands-on course. A portion of each class is devoted to real-life examples, emphasizing the skills newsrooms want. How can web scripting help a reporter track down runners who may have witnessed explosions in the Boston Marathon bombings? How can a map illustrate the challenges in developing gun policy? Why is data cleaning required to uncover the influence of money in visits to the White House? More than ever, these new ways of telling stories require data skills.
Fluency with data and the ability to ask and answer questions from structured information sources can help any journalist, whether she’s a radio producer, magazine writer or digital producer. While the course’s main goal is journalistic, not technical, students will acquire a variety of new skills in data visualization, data analysis, and digital production. In the process, students will learn how to use data to strengthen and improve their reporting process.
Student-created projects will influence the path of the class: in a given semester, we may create infographics, investigate current political or economic issues, or develop innovative newsapps.
Prior experience is not assumed, nor is it necessary. Students are expected to be comfortable with learning software beyond basic social media or word processing, and should be willing to learn new technology skills.
Northwestern University: Associate Professor Darnell Little, Enterprise Reporting with Data
Journalists increasingly rely on data to understand the topics they are covering, to find news and to present information to their audience. ThiThis course is part of MA in Journalism at the National University of Ireland, Galway, and will introduce journalism students to the practise of data journalism and showcase a set of tools and techniques for data driven analysis, investigation and storytelling. Students will learn:s class provides a foundation in finding, acquiring, analyzing and presenting data in a journalistic context. Students will learn how to identify data sources, evaluate the value and accuracy of data, find newsworthy information in data. They will also learn to differentiate good and bad uses of data in journalism.
Northwestern University: Associate Professor Darnell Little, Advanced Data and Mapping
This course will teach students the basics of investigative and database reporting, starting with learning the software skills needed to use the main tools of CAR – spreadsheets and database managers. Students will also learn the basics of statistical analysis before moving on to developing the craft of turning data into compelling stories. Along the way, students will learn to rely less on hunches, theories and guesses and more on hard, quantifiable facts and rigorous aThis course is part of MA in Journalism at the National University of Ireland, Galway, and will introduce journalism students to the practise of data journalism and showcase a set of tools and techniques for data driven analysis, investigation and storytelling. Students will learn:nalysis. Students will study and analyze real government databases and documents that have recently been used in actual stories.
Point Loma Nazarene University: Prof. Danielle Cervantes Stephens, Computer Assisted Reporting
Introduces students to investigative journalism through hands-on laboratory work, including advanced Web research, public records requests, statistical analysis, databases, mapping, visual aids and data interactives.
CAR practitioners have borrowed tools and skills from business, social science, engineering, demographics and computer science— including database management, GIS mapping, statistical analysis and the development of web applications—to do better journalism.
News organizations now demand journalists work increasingly with data, yet very few journalists understand what data is, does, or means to reporting and news production. Best practices with data and journalism evolve from a specialty called computer-assisted reporting (CAR), which originated in investigative and watchdog journalism.
CAR gives a journalist a unique and powerful toolkit that can provide a distinct news market advantage, increasing salary and compensation and making the reporter an indispensable resource in today’s shrinking newsrooms. More importantly, good storytelling and backgrounding coupled with CAR help journalists tell more compelling stories and bring change to communities.
Stanford University: Dan Nguyen, Public Affairs and Data Journalism
Our primary goal is to learn how to argue with and against data. To understand the business of our government, including the power it wields over – or yields to – our institutions, then we must understand data, the byproduct of that business, and often, its fuel.
We see data as a means of understanding and, when necessary, critiquing the “data-driven decisions” in public affairs. Our focus is on concepts rather than technology and mathematical problem solving over statistical methods. The core of our work is ultimately the same core of traditional reporting: the initiative to question, the independent research of facts, and the desire to inform the public.
Stanford University: Dan Nguyen, Computational Methods in the Public Sphere
Computational Methods in the Civic Sphere (COMM 113/213) examines why some information problems are computational – and why others are not – in the context of journalistic enterprise and its wide variety of information problems: research, data collection, data cleaning, statistical analysis, information design, information retrieval, verification, publication, and mass distribution.
We will study real-world problems in journalism and data science, and we will also attempt to solve them. We will study programming, because many of these problems can be substantially solved through programming. But we will also learn why not every problem can or should be fitted to a mechanical algorithm.
Students who successfully complete this class will inevitably learn a wide array of tools and techniques. But gaining a useful skillset is only a coincidental outcome. Our main goal is to learn how to think, and to understand how a computer can complement, but not replace our ability to make decisions.
Stanford University: Dan Nguyen, Computational Journalism
Focuses on using data and algorithms to lower the cost of discovering stories or telling stories in more engaging and personalized ways. Project based assignments based on real-world challenges faced in newsrooms. Prior experience in journalism or computational thinking helpful. Prerequisite: Comm 273D, COMM 113/213, or the consent of instructor.
Stanford University: Cheryl Phillips, Data = Stories
Public Affairs Data Journalism Two is designed to help the students in the Graduate Program in Journalism at Stanford University to go deep on mining data for stories, reporting, writing and visualizing those stories. Students will publish their work on the Peninsula Press news site.
The instructor, Cheryl Phillips, is a Hearst Professional in Residence at the Graduate Program in Journalism at Stanford University where she is teaching data journalism and helping to build the Stanford Computational Journalism Lab. Previously, Phillips was the data innovation editor at The Seattle Times, where she had worked since 2002. Read more in her full bio.
Southern Illinois University: Prof. William Freivogel, Future of Journalism
Truth is, no one is sure what the future hold for journalism. Journalism’s face today bears little resemblance to that of a decade ago. Ten years ago, people didn’t have cell phones with cameras ready to record almost everything – political candidates making ill-considered but candid comments to donors, police shooting black suspects, police behavior at protest demonstrations. Citizen journalism was coming alive along with this new technology. Facebook and Twitter began to take over as the main source of news and information. Newsrooms of metropolitan newspapers lost a third or more of their reporters and photographers. Buzzfeed, Huffington Post, Reddit were born.
Fidler and Freivogel don’t have crystal balls, but will try to get you as prepared as possible for this new future. Every Tuesday, we’ll talk about the impact of new media on news events – like the shooting of Michael Brown and the coverage of the presidential campaign. We’ll also talk about the changes in the ethics and law of new journalism. Then, on Thursdays, we’ll work on computer assisted reporting techniques. Fidler will lead that part of the course. The goal of this CAR portion of the class is to teach you to be adept enough that you can remember how to use CAR techniques after the class ends. And we want to create publishable stories – most aimed for publication in the Daily Egyptian.
University of Arkansas: Rob Wells, Data Journalism
The class provides an introduction to the basic data reporting skills, but it is much more than learning software and pressing buttons. This class will describe how to use data to guide and inform your reporting, and how this will change your relationship with sources and the people you cover. At each step, this class describes real-world examples of ethical issues and best practices in data reporting. The end goal is to use data to advance important journalism that helps us tell stories that better serve the public.
- University of Arkansas: Rob Wells, Advanced Reporting
Students will learn basic data analysis and use these insights to help report on aspects of the homeless population in Northwest Arkansas. Students will produce multimedia stories with data visualizations on assigned topics and may generate their own story ideas.
UC Berkeley: Prof. Bill Drummond, Police Violence and Media Narratives
Police Violence and Media Narratives will turn 15 years of raw homicide data obtained exclusively from the Oakland police department into a richly textured and visualized web site. The goal of the class is to document the ripple effect of homicide deaths and the ensuing disruptions in the lives of victims, perpetrators and their families, through the voices and experiences of people in the community. It will provide a web-based framework that can be augmented and enhanced in future courses.
UC Berkeley: Peter Aldhous, Introduction to Data Visualization
This is a course in finding and telling visual stories from data. We will cover fundamental principles of data analysis and visual presentation, chart types and when to use them, and how to acquire, process and “interview” data. We will make interactive and static charts and maps using free software. There will be some coding, but no prior experience is required. The emphasis is on gaining practical skills that students can apply in a newsroom setting.
UC Berkeley: Peter Aldhous, Introduction to Data Journalism
Over four weeks, these classes will provide an introduction to data journalism. We will cover principles of data analysis, acquiring and cleaning data, basic spreadsheet skills, plus mapping and other forms of visualization. The emphasis will be on finding and telling stories from data.
University Diego Portales, Santiago, Chile: Jeff Kelly Lowenstein, Data Journalism (Periodismo de Datos)
Original course description (Spanish):
Este curso entrega herramientas digitales para optimizar la búsqueda de datos en internet y además capacita al alumno para cotejar correctamente esos datos, de tal manera de transformarlos en información integrada. El alumno aprenderá métodos para vincular información contenida en bases de datos con diseños gráficos simples y complejos. Los estudiantes que hagan un proyecto final tendrán la posibilidad de publicarlo en el diario Hoy en Chicago.
This course provides students with digital tools that optimize online data searching and that help them to properly compare and collate such data to transform it into integrated information. Students will learn methods to make information from databases available through simple as well as complex graphical designs. Students who do a final project will have the possibility to publish in Hoy, a Spanish language newspaper based in Chicago.
University of Florida: Associate Professor Norm Lewis, Data Journalism, Data Literacy and Data Visualization
Data Journalism Course Description:
This course equips you to acquire and analyze data using spreadsheets and databases to shape everyday journalism.
The day is coming when “data journalism” will be redundant. Once upon a time, reporters could rely mostly on interviews and anecdotes. But today’s digital-savvy audiences expect better evidence. They demand data to know whether the community is safe or what colors are in fashion. Thus, employers now expect journalists to find and use data as a matter of routine. So the purpose of this course is to enable you to be a journalist proficient in data.
University of Maryland: Derek Willis, Building Systems for Reporting
This course will teach students how to use data and technology to craft a systematic approach to beat reporting, or to build what you could call a reporter’s exoskeleton. Such a system would make it easier for a journalist to place news in context or spot interesting and potentially newsworthy events.
As an example of this, consider the Supreme Court: each ruling is in itself a potential story and also one part of the life of a changing institution that draws on previous cases and is influenced by judges’ past and present actions. Being able to place a ruling in context — is this out of character for this court, or for any Supreme Court in history? — is a valuable skill for a journalist. Collecting the information needed to provide this context can also help the journalist produce better questions and ideas. That collection effort is rarely simple, but advances in technology make it possible in a variety of circumstances.
Students will work with some of the tools for building system for reporting: spreadsheets, databases, pattern matching and some programming, including web scraping and building useful but simple sites for reporting. Students will work in small teams to choose a beat, research data sources around it and develop a web-based system to surface useful and unusual aspects of the data. You will be doing some work that qualifies as “computer programming” in this course, but it is not a programming course. This is a course about using computers and software to make you a better journalist.
University of Massachusetts at Amherst: Prof. Rodrigo Zamith, Special Topics: Data-driven Storytelling
How can journalists use data to find stories? How can they tell stories through data? This hands-on course provides students with the knowledge and skills necessary to begin gathering, analyzing and visualizing interactive, data-driven stories. Students will work in pairs to tackle questions pertaining to ethical data sourcing, data analysis and making data meaningful for the public. They will also produce their own exciting and thought-provoking digital news story. Prior experience with advanced statistics, web design or computer programming is neither assumed nor necessary, and course content will adapt to the collective skills of the students in the classroom. However, a willingness to experiment, learn new technologies and embrace iteration in a cooperative environment is a must.
In order to free up more time during class for hands-on exercises, professor Zamith uploads lecture videos to a Vimeo channel, which can be accessed here.
University of Minnesota: MaryJo Webster, Database Reporting
JOUR 5155 (Database Reporting) is a skills-based, capstone course designed to enhance reporting skills, primarily by identifying and analyzing electronic data to look for patterns and trends that can lead to in-depth news stories. Students will obtain and analyze digital data for reporting that can be published on various media platforms They will use spreadsheets and databases to manage information, find news stories, and produce visualizations that complement those stories.
University of Missouri: Associate Professor David Herzog, Computer-Assisted Reporting
Computer-assisted reporting (CAR) is a form of data journalism that focuses on the analysis of public records that are stored electronically.
This is largely a skills course with a heavy hands-on component. By successfully completing this course, you will be able to identify, obtain, evaluate, clean, analyze and visualize data. You will be expected to think like a journalist by evaluating data critically and applying what you learn to news stories, information graphics or web applications.
You’ll learn how to use software, such as spreadsheets, database managers, text editors, data-cleaning programs and visualization tools.
University of Nebraska-Lincoln: Prof. Matt Waite, Data Journalism
Every day, more of our lives is being stored in a database somewhere. With that
explosion of data, journalists now more than ever need the skills to analyze and
understand data to then produce the stories hidden in the information. In this
class, we’ll use brainpower and software to look at raw data — not summarized
and already reported information — to do investigative reporting. We’re going to
get our hands dirty with spreadsheets, databases, maps and some basic stats.
And we’re going to do journalism. So buckle up and hold on.
University of Neuchâtel: Nicolas Kayser-Bril, Data Journalism (Journalisme de Données)
Le cours est très pratique. Les étudiants apprendront à utiliser une série d’outils directement utilisables dans leur travail de journaliste.
– Présentation et historique du journalime de données
– Trouver un angle dans un jeu de données et le visualiser rapidement (en utilisant des outils comme Datawrapper, Chartbuilder ou Highcharts)
– Analyser des données avec OpenRefine
– Visualiser des données géographiques avec CartoDB
– Analyse de réseau et enquête avec Detective.io et Gephi
– Connaître les principaux pièges tendus par les données et les déjouer
– Protéger ses données : Introduction à la sécurité informatique
– Mettre en forme ses données : Quelques concepts de programmation
This course is very much practice-oriented. The students learn to use a series of tools that they can use immediately in their journalistic work.
-Presentation and history of data journalism
-Finding an angle in a dataset and visualizing it fast (by using tools like Datawrapper, Chartbuilder or Highcharts)
-Analyzing data with OpenRefine
-Visualizing geographical data with CartoCB
-Network analyses and investigation with Detective.io and Gephi
-Knowing the main traps in data and finding a way around them
-Protecting your data: Introduction to information security
-Formatting data: Some concepts in programming
University of North Carolina at Chapel Hill: Prof. Ryan Thornburg, Data-Driven Journalism
In this skills-based course students learn how to acquire, clean, analyze and present data in a journalistic setting. Your decision to take this course indicates that you are interested in learning the skills and concepts of data-driven reporting. The class starts from the assumption that you’ve never or rarely used even a basic spreadsheet to aid either your reporting or storytelling. That’s where the semester will begin. Along the road to data literacy we will also go over some basic statistics and basic data visualization concepts.
University of Southern California: Dana Chinn, Data Journalism
Overview of the basic data journalism techniques and tools for statistical analysis; understanding of numbers and basic statistics as they relate to journalism. Proficiency with gathering, analyzing and visualizing data.
University of Texas, Austin: Andrew Chavez, Intro to Coding for Journalists
Learning how to code opens all kinds of doors for journalists. It offers the ability to tell stories in new ways – from simple things like interactive news graphics to more sophisticated tools like machine learning and necessary business functions like the newsroom content management system and ad delivery.
University of Texas, Austin: Christian McDonald, Data-Driven Reporting
This course will cover the basics of computer-assisted reporting, using electronic records for the basis of news reporting. Students will learn how to request data from public and governmental sources, to clean up and analyze that data using tools such as Excel and SQL, and use simple statistical models to accurately report based on the data.
University of Texas, Austin; Dhiraj Murthy, Data, Privacy and You
This course provides approaches to understanding what some have termed ‘datafication.’ Specific weeks will explore what it means to think about data today, different types of data including administrative trace data, user-produced social media data, and Internet of Things (IoT) data. The course provides students with literacy of these types of data as well as the ways in which these data are transmitted, stored, compiled, aggregated, analyzed, and used in predictive analytics.
Virginia Commonwealth University: Associate Prof. Jeff South, Computer Assisted Reporting
In MASC 644, you will learn how to become a better, faster and smarter reporter. You will learn how to use the Internet and other digital technology to find ideas, information and sources for your stories. In particular, you will learn how to obtain and analyze data – about health, crime, education, demographics and other topics. You will learn how to find patterns and trends in the data – to identify what the data mean. You will learn how to pull from the data examples to support the trends you have identified. You will learn how to weave this information into your stories, so that your stories aren’t just anecdotal (based on what people have told you) but also analytical (based on reliable data that can’t be disputed). With computer-assisted reporting, you will be able to find stories that have gone unreported; rebut false and misleading statements from news sources; monitor the performance of government and other institutions; and become more marketable journalists.
Samples of students’ published work of the Fall 2015 semester:
Xianmen University: Jeff South, Data Journalism and Visualization
In our Data Journalism and Visualization course, you will learn how to use the Internet and other digital technology to find ideas, information and sources for your stories. In particular, you will learn how to obtain and analyze data – about health, crime, education, economics and other topics. You will learn how to weave this information into news reports, so that your stories aren’t just anecdotal (based on what people have told you) but also analytical (based on reliable data that can’t be disputed).