Teaching plan for the course unit

 

 

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General information

 

Course unit name: Čtica per a la Cičncia de Dades

Course unit code: 574185

Academic year: 2020-2021

Coordinator: Jordi Vitria Marca

Department: Faculty of Mathematics and Computer Science

Credits: 3

Single program: S

 

 

Estimated learning time

Total number of hours 75

 

Face-to-face and/or online activities

30

 

-  Lecture with practical component

Face-to-face and online

 

30

 

(Blended Learning)

Supervised project

15

Independent learning

30

 

 

Competences to be gained during study

 

  • Knowledge of legislation on data protection and privacy, and on the ethical code in professional practice.
  • Capacity to communicate results using appropriate communication and display techniques.
  • Capacity to verify and quantify the validity of a hypothesis, using data analysis.

 

 

 

 

Learning objectives

 

Referring to knowledge

Data science has the potential to be both beneficial and detrimental to individuals and/or the wider public. To help minimize any adverse effects, we must seek to understand the potential impact of our work and consider any opportunities that may deliver benefits for the public. It is recognized that not all work will have a defined societal benefit, but we could strive to seek fairness or an overall increase to well being, within commercial applications.

In this course, we will explore the moral, social, and ethical ramifications of the choices we make at the different stages of the data analysis pipeline, from data collection and storage to understand feedback loops in the analysis. Through class discussions, case studies, and exercises, students will learn the basics of ethical thinking, understand some tools to check or mitigate undesired effects, and study the distinct challenges associated with ethics in modern data science.

 

 

Teaching blocks

 

1. A quick tour through the foundations of ethics.

2. Privacy. Tools for preserving privacy.

3. Transparency, Explainability and Causality.

4. Data Science and Society.

5. Fairness.

 

 

Teaching methods and general organization

 

Due to the health emergency of COVID-19, the subject will follow a model of blended teaching instead of face-to-face. The division of students into groups will be adapted to this situation. Teaching will be structured as follows:

  • Weekly, there is an asynchronous one-hour session where the students autonomously work in the subject.
  • Weekly, there is a face-to-face one-hour session corresponding to theoretical-practical activities.


If the health situation allows it, we will move to a face-to-face teaching style. In this case, the hourly load remains at the same values, but all classes are held in face-to-face mode, and the distribution of students in groups can be varied to suit the face-to-face mode.

 

 

Official assessment of learning outcomes

 

Problem Sets and exercises: 100%. 

 

Examination-based assessment

Problem Sets and exercises: 100%.