Análise e Tratamento de Dados

The course adopts teaching and learning methodologies designed to ensure the integrated acquisition of the competencies defined in the learning objectives, fully aligned with a student-centred pedagogical model focused on problem-solving and practical application of knowledge.
The theoretical component is delivered through a flipped classroom approach, where students access preparatory materials, instructional videos, and recommended readings in advance. This allows in-class sessions to focus on discussion, clarification of concepts, and practical application, fostering active engagement, autonomous reflection, and better preparation for hands-on activities.
Theoretical-practical sessions make extensive use of computational tools such as Excel, R, and Python, enabling students to apply statistical and data analysis concepts to real datasets. The practical component also incorporates project-based learning (PBL), in which students work in teams to identify problems, design analysis strategies, implement solutions, and critically interpret results. This methodology promotes autonomy, complex problem-solving skills, critical thinking, and effective teamwork in multidisciplinary settings.
Collaborative activities in small groups encourage idea exchange, mutual validation of procedures, and the development of scientific and technical communication skills. Formative assessment includes practical assignments, project presentations, and analysis reports, ensuring continuous monitoring of student progress and reflecting both theoretical mastery and practical competence.
The balanced integration of flipped classroom, laboratory sessions, problem-solving exercises, project-based learning, and applied assessment ensures coherence between the pedagogical methods and the intended learning outcomes, preparing students thoroughly for academic and professional challenges in the field of data analysis and processing.

The course is designed to provide students with the conceptual, methodological, and procedural foundations necessary for conducting data analysis using computational tools such as Excel, R, or Python. The main competencies to be developed by the students:
– Acquisition of the core analytical “language” associated with Data Analysis and Data Processing, enabling students to design, implement, and evaluate data-driven projects and to contribute effectively within multidisciplinary teams involving technical specialists and stakeholders.
– Development of the ability to write and apply code in R or Python to real datasets, as well as to critically interpret and communicate the resulting analytical outputs.
The emphasis on practical exercises, problem-solving tasks, and guided computational activities ensures coherence between the intended competencies and the pedagogical approach.

1. Introduction to Data Analysis
Experimental design. The nature of data. The importance of Statistics. Stages of a statistical study.
2. Descriptive Statistics
Organization and presentation of data. Measures of central tendency and dispersion. Measures of skewness and kurtosis. Correlation and independence.
3. Inferential Statistics
Estimation and hypothesis testing. Inference for parameters of normal and other populations. Goodness-of-fit and independence tests.
4. Reliability
Basic concepts. Important parametric models. Applications.
5. Regression Models
Simple and multiple linear regression. Assumption validation. Nonlinear models.
6. Classification Methods.

• - Project or Exam - 100.0%

NAO