DATA, INFORMATION AND SOCIETY 2: DATA JOURNALISM

Academic year
2022/2023 Syllabus of previous years
Official course title
DATA, INFORMATION AND SOCIETY 2: DATA JOURNALISM
Course code
ECC083 (AF:435051 AR:239824)
Modality
On campus classes
ECTS credits
6
Degree level
Corso Ordinario Primo Livello
Educational sector code
SECS-S/01
Period
2nd Semester
Course year
1
Where
VENEZIA
Moodle
Go to Moodle page
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.
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 ) although it is not formally required to have passed the exam. It is important that the students have a solid familiarity with the basic concepts of coding with R.
The course program includes presentation and discussion of the following topics:
1) Data wrangling
2) Data visualization
3) Data analysis
4) Case studies
- 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 (2016). R for data science. O’Reilly Media. https://r4ds.had.co.nz
1) active participation to the lectures (20% of the final mark)
2) intermediate assessment (30% of the final mark)
2) final project consisting in writing and presenting a data-driven article (50% of the final mark)
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)
written and oral
Definitive programme.
Last update of the programme: 19/12/2022