-
Presentation
Presentation
This curricular unit is the first touchpoint of the students with the various dimensions of Data Science. It is a bird's eye view of Data Science, covering topics at a high level, to be developed in detail throughout the Data Science undergraduate curriculum. In this unit we aim to provide the student with the perspectives of how data science is viewed and used in the academic, business and societal domains. Basic principles, its beginnings and historical development, interactions and relations with other disciplines, its tools and ground theoretical principles will be discussed, as well as some of the ethical issues that its usage may imply.
-
Class from course
Class from course
-
Degree | Semesters | ECTS
Degree | Semesters | ECTS
Bachelor | Semestral | 6
-
Year | Nature | Language
Year | Nature | Language
1 | Mandatory | Português
-
Code
Code
ULHT6634-23084
-
Prerequisites and corequisites
Prerequisites and corequisites
Not applicable
-
Professional Internship
Professional Internship
Não
-
Syllabus
Syllabus
S1 - Introduction to the Course S2 - Data, Data Types and Data Manipulation Metadata Bias Data Types and representations Processing and Storage S3 - Exploratory Data Analysis Centrality and dispersion measures Visualization S4 - Data Modeling, Databases, Data Extraction, Integration, and Processing Data origin Data sources Extraction Data vs. Information Processing S5 - Machine Learning Supervised models Unsupervised models S6 - Case Study S7 - Ethics in Data Science S8 - Data Science Projects - Methodologies
-
Objectives
Objectives
After successfully completing this curricular unit, the student should have achieved the following learning objectives (LO): LO1. Understand and explain what is data. Distinguish between different types of data. Identify and classify data sources. LO2. Be able to defend the need, usefulness and value of applying data science to scientific, management and social problems, describing, predicting and pescribing actions to address them. LO3. Distinguish data science from related disciplines, identifying similarities and differences. LO4. Examine the implications of data collection in science, business and society, and its ethical framework. LO5. Understand the contextual needs of a data analysis model and be able to draw and create a simple analytical model.
-
Teaching methodologies
Teaching methodologies
The classes are accompanied by practical examples and individual tutorials, to be developed in the classroom under the supervision of the professor, but also independently during out-of-class study time. In addition, two group projects are planned to be developed during out-of-class study time.
-
References
References
Adhikari, A., Denero, J., Wagner, D. - Computational and inferential thinking: The foundations of data science. University of California, Berkeley. 2019. Disponível em: https://inferentialthinking.com O'Neil, C., & Schutt, R. (2013). . O'Reilly Media, Doing data science: Straight talk from the frontline Inc. ISBN: 9781449358655
-
Assessment
Assessment
A cadeira é avaliada nos componentes Teórico e Prático.
Componente Teórico:
- 2 Testes (80%)
- Exercícios e participação (20%)
Componente Prático:
- 2 projetos (80%)
- Exercícios e participação (20%)
Condições para aprovação:
- Pelo menos 50% de aproveitamento no componente Teórico.
- Pelo menos 50% de aproveitamento no componente Prático.
- Média simples dos componentes Téorico e Prático maior ou igual a 50%.
Condições para realização de Exame:
- O exame será dividido em componente prático e teórico. O aluno deve realizar o exame no(s) componente(s) em que tiver obtido aproveitamento menor que 50%.
-
Mobility
Mobility
No
