DATA, INFORMATION AND SOCIETY 2: DATA JOURNALISM

The course is an introduction to data storytelling, the art of blending data science and narratives. Students will learn essential skills for analyzing, visualizing and summarizing data, and how to use such skills to tell stories. A fundamental part of the course involves the critical discussion of articles published in various newspapers, magazines, and websites.

Regular and active participation in the teaching activities offered by the course and in independent research activities will enable students to:
1. (knowledge and understanding)
- know and understand basic data science instruments for journalism
2. (applying knowledge and understanding)
autonomously apply basic data science instruments to extract and synthetize information
3. (making judgements)
make autonomous judgements about the validity and feasibility of basic data science instruments and understand their impact in data journalism

Students are assumed to have reached the learning objectives of the course Introduction to Coding (https://www.unive.it/data/insegnamento/435050 ). It is important that the students have a solid familiarity with the basic concepts of coding with R.

The course program will present and discuss the following topics:
1) Data wrangling
2) Data visualization
3) Data summary
4) Case studies in data journalism

- Readings and materials distributed during the course through Moodle
- A. B. Tran (2018). R for Journalism. Online course. https://learn.r-journalism.com
- H. Wickham and G. Grolemund (2023). R for data science. O’Reilly Media, 2nd edition. https://r4ds.hadley.nz

1) active participation to the lectures (30% of the final mark)
2) final project consisting in writing and presenting a data-driven article (70% of the final mark)

The evaluation of the final project (70% of the final grade) is in turn divided into:
- evaluation of the quality, originality and technical correctness of the article (40%)
- evaluation of the quality of the presentation and its oral discussion (30%)

oral

The exam result is graded as follows:
- sufficient (18-22 points), if the student demonstrates a sufficient knowledge and understanding of the course methods, is able to apply and interpret them adequately, and uses technical terminology correctly;
- fair (23-25 points), if the student shows a good knowledge and understanding of the course methods, applies and interprets them convincingly, and uses technical terminology with fair accuracy;
- good (26-28 points), if the student possesses a solid knowledge and understanding of the course methods, applies and interprets them in a thoroughly convincing manner, and employs technical terminology accurately;
- excellent (29-30 points), if the student demonstrates an excellent knowledge and understanding of the course methods, applies and interprets them brilliantly, and uses technical terminology with extreme accuracy.

Distinction (lode) is reserved for students who, in addition to having achieved an excellent result, demonstrate an exceptional commitment in the execution and presentation of the project, providing original contributions or insights.

Conventional theoretical lectures complemented by exercise classes, discussion of case studies and computer labs. Teaching material prepared by the lecturer will be distributed during the course through the Moodle platform. The statistical software used in the course is R (www.r-project.org)