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Presentation
Presentation
This course unit provides students with their first contact with the main dimensions of Data Science, introducing fundamental concepts, methods and tools that will be further developed throughout the degree programme. Students will follow the complete workflow of a Data Science project, from problem definition to the communication of results. The course unit aims to develop skills in data collection, preparation, exploration and analysis through the resolution of real-world problems in different contexts, particularly social and business contexts. A critical and evidence-based approach will also be promoted, including reflection on the ethical implications of data use.
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Class from course
Class from course
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Degree | Semesters | ECTS
Degree | Semesters | ECTS
Bachelor | Semestral | 6
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Year | Nature | Language
Year | Nature | Language
1 | Mandatory | Português
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Code
Code
ULHT6638-23084
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Prerequisites and corequisites
Prerequisites and corequisites
Not applicable
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Professional Internship
Professional Internship
Não
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Syllabus
Syllabus
1. Introduction to Data Science Importance, concepts, and applications Data Science project workflow Data, data sources, and data quality 2. Programming for Data Science Python NumPy Pandas 3. Data Manipulation and Preparation DataFrames Data cleaning and transformation 4. Data Exploration and Visualization Descriptive statistics Univariate, bivariate, and multivariate analysis Data visualization Identification and interpretation of patterns 5. Introduction to Machine Learning Fundamental concepts Supervised vs. unsupervised learning 6. Regression and Classification Models Linear regression Logistic regression Application to real-world problems 7. Model Evaluation and Interpretation Evaluation metrics Model comparison Interpretation of results and limitations 8. Ethics and Responsible Use of Data Data quality and bias Privacy and data protection Ethical implications of Data Science
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Objectives
Objectives
Upon successful completion of this course unit, students should have achieved the following learning outcomes (LO): LO1. Understand different types and sources of data. LO2. Understand the role of Data Science in solving real-world problems in different contexts. LO3. Distinguish Data Science from related disciplines. LO4. Apply fundamental Python programming, data preparation and data analysis techniques. LO5. Apply the Data Science workflow, from problem definition to the communication of results. LO6. Understand and apply basic machine learning methods, namely regression and classification. LO7. Evaluate and interpret results, recognising model limitations and the ethical implications of data use.
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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.
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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
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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%.
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Mobility
Mobility
No
