Course Objectives
Data science is becoming increasingly important to society and business as a strategic tool for making better decisions. This study cycle is highly technical and focuses on developing the quantitative and methodological skills needed to utilize the potential of data science. The plan is designed to provide students with substantial hands-on competence in data science and the ability to use it to
create value for organizations in a wide range of areas such as management, marketing, finance and economics.
Access Conditions
The information provided does not exempt the consultation to the page of the General Directorate of Higher Education (DGES)
Professional Outlets
Data Analyst
Big Data Specialists
Business Intelligence Specialist
AI and Machine Learning Specialists
Data Scientist
FinTech Expert
Learning Language
Portuguese Language.
Learning Objectives
Know the central role of data in an organizations value creation strategy;
Understand how data-driven thinking is structured and streamlined;
Know the main concepts and techniques of data science;
Acquire programming skills in Python and other machine learning tools to extract, organize and analyze data from various sources;
Knowing how to explore data sets of different dimensions, incorporating the quantification of uncertainty in the analysis and prediction of future results, making it possible to evaluate the impact of possible decisions;
Learn to extract, transform, load and visualize data and analysis;
Understand the main phases of a data science project, being able to apply them in practical implementations;
Gain knowledge in management support, from accounting, business management, fundamental economics and finance.
Access to Superior Studies
The graduate degree allows the students to apply for Post-graduate.
Course Coordinators
Common Field
Curricular Unit | Code | ECTS | Period |
Data Analytics | 51001826 | 4 | 1st S |
Financial Accounting for Management | 51001764 | 6 | 1st S |
Macroeconomic Analysis | 51001770 | 4 | 1st S |
Mathematical Analysis I | 51001792 | 6 | 1st S |
Programming | 51001815 | 10 | 1st S |
Applied statistics | 51001747 | 4 | 2nd S |
Data Science Topics | 51001736 | 11 | 2nd S |
Introduction to Management | 51001753 | 4 | 2nd S |
Mathematical Analysis II | 51001809 | 6 | 2nd S |
Microeconomics | 51001781 | 5 | 2nd S |
Curricular Unit | Code | ECTS | Period |
Artificial Intelligence | 51001874 | 6 | 1st S |
Databases | 51001852 | 6 | 1st S |
Management Accounting | 51001880 | 6 | 1st S |
Operations Research | 51001891 | 6 | 1st S |
Programming for Data Science | 51001905 | 6 | 1st S |
Corporate Finance | 51001916 | 5 | 2nd S |
Extraction, Transformation, Loading and Visualization | 51001848 | 9 | 2nd S |
Marketing | 51001863 | 4 | 2nd S |
Multivariate Analysis | 51001927 | 8 | 2nd S |
Strategic Management | 51001837 | 4 | 2nd S |
Curricular Unit | Code | ECTS | Period |
Development for the WEB | 51001951 | 4 | 1st S |
Financial and Operational Reporting | 51002013 | 6 | 1st S |
Machine Learning | 51001990 | 9 | 1st S |
Organization Behavior | 51001962 | 5 | 1st S |
Time Series | 51001973 | 6 | 1st S |
Computer Law | 51001938 | 4 | 2nd S |
FinTech – Financial Technology | 51002002 | 6 | 2nd S |
Project in Data Science | 51001949 | 15 | 2nd S |
Web Marketing and E-Commerce | 51001984 | 5 | 2nd S |