DATA, INFORMATION AND SOCIETY 2: DATA JOURNALISM

Academic year

2024/2025 Syllabus of previous years

Official course title

DATA, INFORMATION AND SOCIETY 2: DATA JOURNALISM

Course code

ECC083 (AF:529197 AR:310724)

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

Contribution of the course to the overall degree programme goals

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.

Expected learning outcomes

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

Pre-requirements

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.

Contents

The course program includes presentation and discussion of the following topics:
1) Data wrangling
2) Data visualization
3) Data analysis
4) Case studies

Referral texts

- 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

Assessment methods

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%)

Teaching methods

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)

Type of exam

written and oral

2030 Agenda for Sustainable Development Goals

This subject deals with topics related to the macro-area "Poverty and inequalities" and contributes to the achievement of one or more goals of U. N. Agenda for Sustainable Development

Definitive programme.

Last update of the programme: 13/10/2024