Teaching Methodologies
The teaching methods (ME) to be used are balanced between traditional and active and are as follows:
ME1 – Content exposure by the teacher (compatible with learning objectives 1, 2, 3, 4, 5, 6)
ME2 – Test the content learned by students (compatible with learning objectives 1, 2, 3, 4, 5, 6)
ME3 – Problem Solving by Students (Compatible with Learning Objectives 1, 2, 5, 6, 7)
ME4 – Interaction and sharing of ideas by students (compatible with learning objectives 1, 2, 5, 6, 7)
ME5 – Development of critical thinking by students (compatible with learning objectives 1, 2, 5, 6, 7)
ME6 – Research done by students (compatible with learning objectives 5, 7)
ME7 – Student-made creation (compatible with learning objectives 7)
The curricular unit is based on theoretical-practical classes. The teaching methods (ME) to be used are balanced between traditional and
active.
Classes include the presentation of concepts and methodologies and proceed with their discussion, as well as the demonstration of the
resolution of applied problems. In the classes concepts and methodologies are presented, contents are discussed and problem solving is
demonstrated. The content is taught and discussed in a classroom environment.
In addition to the traditional expository method, the methodology will include project-based learning (PBL). As the name implies, an active
learning methodology that aims to associate learning with doing. This method is based on the construction of knowledge collectively,
moving away from the conventional classroom model where the teacher teaches a subject and the students show how much they have
learned from a final evaluative activity. The project that is proposed to be developed, desirably carried out in a group, aims to cover the first
phases of a data science project, according to the data science life cycle proposal made by Hotz (2022) of the Data Science Process
Alliance. This project will include the problem definition phase and the investigation and data cleaning phase. It is expected that, at a more
advanced stage of the degree, the student will develop projects that cover the remaining phases of the data science life cycle.
Learning Results
The main learning objectives (AO) defined are the following:
LO1 Know the central role of data in an organizations value creation strategy
LO2 Understand how data-driven thinking is structured and streamlined
LO3 Know possible applications of data science projects in various sectors of activity
LO4 Know the main concepts and techniques related to data science
LO5 Become aware of some of the main sources of data at national and international level
LO6 Understand the main aspects related to the phases of a data science project
LO7 Know how to apply in a practical project some of the main concepts and approaches learned
The teaching methods (ME) to be used are balanced between traditional and active and are listed in Topic 8.
Program
1 Data, its transformation into value and data-driven thinking
2 Data science and applications across multiple domains
3 Those involved in a data science project
4 The main techniques of data science
5 National and international data sources
6 The data science lifecycle(s)
7 Natural Language Processing (NLP)
8 Analysis of practical cases
9 Tools used in data science
10 MainData Science Problems and Challenges
Internship(s)
NAO
Bibliography
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