97326 - DATA LITERACY

Course Unit Page

SDGs

This teaching activity contributes to the achievement of the Sustainable Development Goals of the UN 2030 Agenda.

Quality education

Academic Year 2021/2022

Learning outcomes

The main goals of this course are: • to understand what data are and what their role in society is; • to discriminate data based on specific use; • to synthesize, create visualizations and representations of data; • to understand the issues involved in the use of data; • to promote critical thinking based on information from data analysis • to recognize when data are tampered with, misrepresented and used in a misleading manner • to communicate information about the data to people who are not expert on the subject (data storytelling). After completing this course, students will be able to: • Provide examples of how people use data every day • Apply critical thinking skills when working with data • Identify data analysis tools and techniques appropriate for the analysis

Course contents

1. Turn data into information and information into insight.

  • Obtaining data: Data sources, data types, measurements
  • Analyzing data: Fundamentals of statistics
  • Data visualization: basic graphical representations 
  • Recognize misleading graphs and statistics

2. Communicate information obtained from data analysis

  • Infographics
  • Data storytelling

3. Empirical case studies from academic and grey literature.

Readings/Bibliography

Lecture notes, selected articles and book chapters will be provided through the on-line e-learning platform  and will be sufficient for preparing the exam.

For those students seeking more structured materials, the course is based on the following textbooks:

  • David Spiegelhalter, "The Art of Statistics: how to learn from data", Pelican, 2019
  • Alberto Cairo, "How Charts Lie: Getting Smarter about Visual Information", WW Norton & Co, 2019

Teaching methods

  • Lectures involve the presentation of theoretical and practical issues.  After each theoretical session a practical tutorial in the laboratory is devoted to applications on real case studies.
  • Students are invited to solve and discuss case studies. Home assignments will serve to reinforce class concepts and get familiarity with data analysis and interpretation. Students are allowed and encouraged to work together on home assignments. However, a separate write-up is expected from each student. Home assignments will  be ungraded. However, solutions (or  simply a  feedback)  will be provided for self-assessment.

 

Assessment methods

  • Oral examination
  • The exam aims at  testing the students knowledge of the theoretical topics and their ability to critically interpret data and turn data into information and into insigts in a decision-making process. 
  • Attending and non attending students will be required to write an essay on an empirical case study that will be discussed during the oral examination. Students are invited to submit it a couple of days before the oral examination.

Teaching tools

The UNIBO e-learning platform (VIRTUALE) will be used to share teaching materials and to assign periodical home assignments to students. The teaching material includes: 

  • Lecture notes summarising theoretical topics explained in class
  • Open data and lecture notes  to follow the practical sessions
  • Miscellanea: exercises, solutions to assignments, sample exams, follow-up materials

Office hours

See the website of Ida D'Attoma