Data journalism

Learning outcomes

Provide participants with theoretical notions and practical skills in the creation of data-driven products such as articles, surveys, press releases mainly focused on the analysis of statistical-qualitative information, to be achieved through a process of valorization of available information sources (offline and online at national and international level).

Expected learning outcomes

At the end of the course, students will have acquired the skills to construct a data-based journalistic output, through the creation of a personalized output that includes the following activities: selection and organization of data, analysis and basic statistical calculations (descriptive statistics), creation of graphs and accompanying journalistic articles.

Course contents

Contents
The course is developed in different training modules, each of which will have contents ranging from the notions of descriptive statistics, to computer science, to journalism, to the use of graphic visualization software.

Program

DESCRIPTION OF THE TRAINING MODULES

The context

What is meant by data journalism or, more generally, data driven products; what are the main experiences in Italy and abroad; what are the required skills and professional prospects; presentation of case histories on individual investigations/reports carried out in recent years.

FIND

Start with a good research question – Start working on the data having clear in advance what our goal is. Even if the database to work on is already available to us. Specific examples.
List of possible sources – Build a list of possible sources on which to search for the data that interests us.
The types of data that can be used – Structured data and unstructured data: what is the difference and why our goal is always to obtain structured data. The (simple) concept of machine-readable data.

TO ANALYZE

Structured data – Download a table and understand its formatting. A table can contain various types of data: what they can be and why accurate formatting is important.
Unstructured data – Scraping and structuring data. What is scraping and what is it for? A practical example to move from unstructured data to structured data.
Clean data – Is our data clean? A list of key controls that allow us to trust our data.
Analyze data – Filter, sort, group: preliminary data analyzes are often the most useful. How they are done and why they can be useful to us.

COMMUNICATION

Data Visualization – Key concepts and most popular tools. When to use a visualization type and why. The most popular viewing modes and main tools of the Dataninja School search catalog.
Use a visualization tool – Let's visualize the data collected with a tool and create our first graph.
Telling and disseminating – What is the best way to effectively tell and spread the contents we have produced.

Prerequisites

No prerequisites or preparatory courses are required.

Reading/Bibliography

Handout provided by the teacher.

Teaching methods

Inquiry based learning, flipped classroom, learning by doing project based. Students will be provided with teaching materials created by the teacher, exercise templates and continuous support throughout the duration of the course and online upon request.

Assessment methods

Examination methods
1/3 of the evaluation derives from active and relevant participation during the lesson and from the interaction with the teacher and colleagues during the discussion of the topics and the carrying out of exercises connected to them
1/3 of the evaluation comes from a final written test, which will be carried out step by step during the lessons in order to create a communicative output based on the data (analysis, visualization, narration).
1/3 of the evaluation comes from the presentation of the final written test, which will take place on the last day of class. Through this presentation, students will also be able to improve the public speaking skills previously acquired within their teaching plan.