“Journalism” refers to the practice of creating and publishing stories and narratives in accessible ways while using publicly available data (structured and unstructured visual, textual, statistic, and numerical information from reliable sources). The course will focus on 1) Promoting critical data practice as a step beyond data collection and consumption, which consists in reflecting on, interpreting, and translating the relationship between society and data generator platforms; 2) The latest tools, expert advice, case studies, and repositories of data for public use, and 3) Building new storytelling projects rooted in real life datasets, either by the students themselves or in collaboration with other practitioners and institutions. 

This course uses an engaged research-led teaching approach. Projects will be structured around a “real-life” problem or questions involving social issues. The idea behind “learning by doing” is to undertake group projects with guidance from the module leader or guest speakers. These group projects constitute the assessment for this module. Students start working on these projects from the first weeks and continue to develop them through the end of the course, with input from convenors and ample opportunities for formative feedback and entrepreneurship. 

The course splits into theoretical and practical sessions. Ideally, collaboration with NGOs and stakeholders will take place during the project development phase.

By the end of the course, students will be able to: 

·       understand the importance of data and user-generated content to engage with social issues and opportunities

·       critique current narratives, social structures, political facts based on data extracted from public repositories

·       acquire technical competence to organize and create visualizations of data analyses

·       research and write their own data journalism stories

·       engage in teamwork focused on the retrieval and interpretation of data offering various levels of complexity

All students are expected to be active participants in their own and each other’s learning process. SIMPLY SHOWING UP IS NOT “ATTENDING” or “PARTICIPATING”. 

·       This course requires a significant amount of work to be completed outside of class hours. Given the nature of the course, unexcused absences will not be tolerated. 

·       Please make sure that all observations, comments, and criticisms are constructive, respectful, and spoken in a neutral tone.

·       Please silence all electronic devices for the consideration of others.  

·       Please do not use social media or email during class if it is not relevant to the topic/discussion at hand.  

·       Sleeping and side conversations in class are not permitted. Excessive occurrences will lead to consequences at the professor’s discretion.

·       Arriving late to class is extremely disruptive both for your peers and for me. Be on time. 

Students unwilling to comply with these policies will be asked to leave the class and will be marked absent for that class period.

Part 1 - The foundations of Data Journalism

Content: Introduction to conceptual perceptions of data in the networked society, the roots of data journalism as a discipline (From early scientific magazines to NYT’s Avalanche story); examples of data application to understand imbalances in social representation (e.g., race, gender, and class); Student workshops and an overview of key software to scrape, compile, and visualize data (publicdatalab.org); the rise of data-centered stories in the press: a collective reading of case studies (a selection of NY Times, Washington Post, FT stories); Interpreting data: How to engage with visual and numerical assets on social media (the case of Twitter, Facebook & Cambridge Analytica); An overview of reading skills and standards that guide data journalism: accuracy, accessibility, and pattern-chasing; Learning how to develop a case study to its implementation.

Week 1: 

What’s Data Journalism? – A brief history of data, ideology, and power

Week 2: 

Data journalism and social justice movements – Facing social issues

Week 3:

Best practices in data journalism – Doing your data shopping list

Week 4: 

In search of the perfect software – A round-up of the latest technology

Week 5: 

Merging data and story: The art of visual and numerical storytelling

Week 6:

Sourcing with safety: A roadmap to navigate a data-flooded world

Week 7:

Data and people: Getting personal (Sourcing personable identifiable data, ethics and legal issues) 

MIDTERM

Part 2: Practising Data Journalism

Content: Understanding the main dynamics behind successful data journalism projects: Picking up authors, researchers, curators, and designers within groups; A round-up of the best-suited technology from the publicdatalab.org; The importance of tailoring pitches and choosing editors: an overview of available and possible choices; First-hand accounts of publishing data journalism projects; The critical eye in the visuals and data: Talking numbers in a visual society (overview of recent reportages, speakers’ feedback); Recent case studies and ideas for student projects: Quick seminars; Portfolio reviews to get colleagues’ and professor’s feedback; Implementing feedback with input received, report making; Presentation skills for unveiling projects: Precision, good-will, and explanation techniques; Dissemination and reader feedback. 

Week 8:  

Defining priorities for your project

Week 9: 

First project presentation – Group priority definitions, task splitting, choice of data/institution

Week 10: 

Preparing the pitch

Week 11: 

New discoveries and why they matter

Week 12:

Defining platforms for publishing 

Week 13:

Finishing your project

Week 14:

Future paths for data journalism/Project presentation