In today's world, technological and social transformations are occurring at an ever-increasing pace, from the proliferation of devices connected to the Internet and their enormous capacity, to the data on individuals and interactions between individuals that are stored every second. All over the world, inside and outside traditional contexts, multiple academic and business organizations are being born, dedicated to the study of the problems associated with the proliferation of data and the search for ethical criteria that can guide their progress. In this context, there are European forecasts that anticipate a huge demand for professionals specialized in Data Science, which is an interdisciplinary area focused on the study and analysis of data, with the aim of extracting knowledge for possible decision making in various professional and knowledge areas.

With several courses in the area of social sciences, humanities and behaviour, and also resources in the area of computing, the Catholic University of Portugal in Braga provides the favourable environment for training in Applied Data Science, with research activities and links to the business sector, which in this city is particularly active and creative. This course originated among the teachers of Computation and Data Science, Mathematics and Statistics areas of the FFCS and works in collaboration with the School of Biotechnology, the School of Arts and the Porto Business School of the UCP, which has teachers with high prestige in the area of computer science and information systems.

This cycle of studies in Applied Data Science aims to equip students with the necessary skills to analyse large amounts of data, solving problems in various fields, including the social sciences, humanities and behavioural sciences. In this sense, the first two years are dedicated to the acquisition of the necessary skills in computing and data analysis. The third year aims to apply these skills to specific domains through curricular units of interest to the students.

General Objectives

• To provide a solid background in data science, in the field of data collection, processing, modeling and analysis, in organisational, business and academic contexts.
• To provide a solid background in the disciplines that serve as the foundation of data science. This basic training will stimulate problem-solving skills and provide graduates with the ability to keep up with scientific and technological developments in this area of knowledge.
• To provide future graduates with the versatility and skills necessary to apply the tools of data science to different areas of knowledge present in the FFCS (social sciences, humanities and behaviour sciences) and also in the broader context of UCP (medicine, management, law, biotechnology, etc.).
• To train professionals and citizens with critical thinking, teamwork skills, civic sense and high sense of ethics.

Learning objectives

• In-depth knowledge of mathematics and statistics oriented to data science.
• Knowledge in the area of computer science. Ability to design and implement efficient algorithms.
• Skills in collecting, processing, storing, visualising, and analysing large amounts of data; ability to infer, prescribe, and predict using the information extracted.
• Ability to apply machine learning methods. Knowledge of the theoretical foundations of artificial intelligence.
• Application of data knowledge extraction techniques to intelligent decision making based on the analysis of large data streams.
• Ability to design, develop and implement a project.
• Ability to work in a team and collaborate with professionals and researchers from different scientific fields and professional backgrounds.
• Keen ethical sense and knowledge of the anthropological, ethical and legal implications of data science.

Career Prospects

• Big Data Analyst
• Data Scientist
• Data Engineer
• Machine Learning Specialist
• Machine Learning Algorithm Development Specialist
• Artificial Intelligence Systems Specialist

These functions can be exercised in various sectors of activity, such as: Banking, Insurance, Telecommunications, Pharmaceuticals, Communication Companies, Public Administration, Industrial Companies, Sports, among others.

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