Beatriz Alves Damião Correia Paulino

Martim Hernandez dos Santos

On successful completion of the course, students should be able to:
LG1. Explain what are the aplication domains and what skills are necessary to analyse various data.
LG2. Define concepts such as exploratory data analysis, statistical inference and modelling, machine learning and high-dimensional data analysis.
LG3. Justify the need for reproducible research, the ethical and normative issues behind data-driven decision-making, and its potential bias.
LG4. Explain the underlying techniques of visualization and communication of results.

Syllabus contents (SC):
SC1. Introduction to Data Science: main concepts and methodologies.
SC2. Data to support to decision making: privacy and ethics.
SC3. Presentation of case studies that include the complete data cycle from different areas.
SC4. Collection and treatment of unstructured data.
SC5. Concepts and techniques for data visualization and visual perception for the communication of knowledge.
SC6. Structured data preparation elementary techniques.
SC7. Construction of inference models based on the data.

Since the UC is mostly of practical experimentation and critical reasoning, there will be no fully written examination component. The final grade is calculated under the category ""assessement along the semester"" by assessing:
Individual online quizzes (MT): 30%.
Online forum participation(F): 20%
Report and presentation of the group project assignent results in a case study: (P) 70%.
The oral discussion in P individually grades each student in the group.