Advanced Topics in Data Engineering I

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Credits
6
Types
Compulsory
Requirements
This subject has not requirements, but it has got previous capacities
Department
EIO;TSC;EEL;CS;DAC
En aquesta assignatura s'impartiran seminaris sobre diferents temes relacionats amb l'Enginyeria de Dades. Serà una assignatura activa que anirà evolucionant al llarg dels anys segons les prioritats tecnològiques, la presència d'experts en temes estratègics i la disponibilitat de recursos per realitzar activitats dins d'un àmbit particular.Aquest curs l'assignatura se centrarà en l'Ètica de Dades.

Teachers

Person in charge

  • Eva Vidal Lopez ( )

Others

  • Climent Nadeu Camprubi ( )
  • Jordi Domingo Pascual ( )

Weekly hours

Theory
2
Problems
0
Laboratory
2
Guided learning
0
Autonomous learning
6

Competences

Transversal Competences

Transversals

  • CT2 [Avaluable] - Sustainability and Social Commitment. To know and understand the complexity of economic and social phenomena typical of the welfare society; Be able to relate well-being to globalization and sustainability; Achieve skills to use in a balanced and compatible way the technique, the technology, the economy and the sustainability.
  • CT3 - Efficient oral and written communication. Communicate in an oral and written way with other people about the results of learning, thinking and decision making; Participate in debates on topics of the specialty itself.
  • CT4 - Teamwork. Be able to work as a member of an interdisciplinary team, either as a member or conducting management tasks, with the aim of contributing to develop projects with pragmatism and a sense of responsibility, taking commitments taking into account available resources.
  • CT7 - Third language. Know a third language, preferably English, with an adequate oral and written level and in line with the needs of graduates.
  • CT8 - Gender perspective. An awareness and understanding of sexual and gender inequalities in society in relation to the field of the degree, and the incorporation of different needs and preferences due to sex and gender when designing solutions and solving problems.

Basic

  • CB2 - That the students know how to apply their knowledge to their work or vocation in a professional way and possess the skills that are usually demonstrated through the elaboration and defense of arguments and problem solving within their area of ??study.
  • CB3 - That students have the ability to gather and interpret relevant data (usually within their area of ??study) to make judgments that include a reflection on relevant social, scientific or ethical issues.
  • CB4 - That the students can transmit information, ideas, problems and solutions to a specialized and non-specialized public.
  • CB5 - That the students have developed those learning skills necessary to undertake later studies with a high degree of autonomy

Generic Technical Competences

Generic

  • CG3 - Work in multidisciplinary teams and projects related to the processing and exploitation of complex data, interacting fluently with engineers and professionals from other disciplines.
  • CG4 - Identify opportunities for innovative data-driven applications in evolving technological environments.
  • CG5 - To be able to draw on fundamental knowledge and sound work methodologies acquired during the studies to adapt to the new technological scenarios of the future.

Objectives

  1. Recognize and understand the social and environmental impact of data science and engineering, and the ethical issues involved in their applications.
    Related competences: CB2, CB3, CB4, CB5, CT2, CT3, CT4, CT7, CT8, CG3, CG4, CG5,
    Subcompetences:
    • Practice critical thinking and develop argumentation and communication skills through dialogue and debate in the field of data science and engineering.
    • Learn to use relevant ethical and legal concepts and terms

Contents

  1. Data ethics. Introduction
    Ethics and morals. Values. Ethical conflict. Engineering profession. Responsibility.
  2. Laws, rules and codes
    Codes of ethics.
    Normative compliance.
    Ethics self-assessment in research,
    Broader impact statement on research articles.
    National and international regulations.
  3. Current Case Studies in Data Science and Engineering
    Privacy, data origin, biases, virtual world, whistleblowers, environmental impact, ODS, manipulation, education, health, gender, neurorights, etc.

Activities

Activity Evaluation act


Ethics in Data Science and Engineering. Introduction


Objectives: 1
Contents:
Theory
20h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
30h

Case study in data science and engineering.

Study, reflection, exposition, dialogue and conclusions of each case presented.
Objectives: 1
Contents:
Theory
10h
Problems
0h
Laboratory
30h
Guided learning
0h
Autonomous learning
60h

Teaching methodology

Las primeras sesiones serán de introducción a la ética en el área de ciencia e ingeniería de datos. Las siguientes sesiones serán trabajadas por el estudiantado.
Cada semana tiene un tema asociado y un grupo de estudiantes asignado. Cada grupo se encarga de investigar el tema asignado en mayor profundidad. El grupo presenta el tema y realiza propuestas para el diálogo: argumentos a favor y en contra. El grupo organiza actividades para los compañeros y compañeras para que todos puedan adentrarse en la problemática ética que el tema representa.

Evaluation methodology

The subject will be evaluated as follows:
Class participation: 25 %
Development of a theme and presentation: 75 %

In case of reassessment, an exam and additional work will be done.

Bibliography

Basic:

Complementary:

Previous capacities

those obtained in the previous subjects