Bachelor degree in Data Science

Education objectives

The objective of the bachelor’s degree in Data Science is to train future professionals who are able to understand, develop and use models, algorithms, and the most advanced data science techniques to analyze and extract knowledge from Big Data. In this course, students will be exposed to innovative topics that span multiple disciplines (machine learning, deep learning, and artificial intelligence) and that are indispensable in the rapidly evolving field of data science. In this way, the course will have a wide variety of cross-sectoral applications. Thus, the bachelor’s degree in Data Science aims to train future experts in data science, needed in a wide range of industrial applications and in favoring the digital transition of companies.

The data science graduate must:

  • Understand the theoretical foundations of statistics, machine learning and methods of artificial intelligence;
  • Identify and understand the most efficient algorithm for each specific problem;
  • Design and develop state-of-the-art data science algorithms;
  • Work closely with information technology experts to integrate science-based algorithms data in existing systems;
  • Identify the underlying patterns and extract useful information from the large volume of heterogeneous data in organizations;
  • Be proficient in the most used statistical procedures, tables, techniques and machine learning systems;
  • Stimulate interest in following scientific advances in the field of data science and artificial intelligence.

For more information about this degree, click here.

General characterization

DGES code



Bachelor (1st Cycle)



Access to other programs


Mauro Castelli

Opening date

September 2023





Working Hours

Teaching language


Degree pre-requisites

The study program is organized in 6 semesters for a total of 180 ECTS divided into five scientific areas (Informatics, Mathematics and Statistics, Business Sciences, Social and Behavioral Sciences, Informatics, or Mathematics and Statistics, or Business Sciences, or Social and Behavioral Sciences).

Out of the 180 ECTS required to complete the course, 150 correspond to mandatory subjects. Additionally, the student must choose five elective course units that will correspond to 30 ECTS. All elective courses can be taken at Universidade Nova de Lisboa or abroad (for example the Erasmus Program)-

Conditions of admittance

Calendar of the 2023 National Higher Education Access

1st Phase: from July 24th until August 7th 

2nd Phase: from August 28th until September 5th 

Establishment Code: 0906

Course Code: L188

Formula of Applicants Grade:

  • Secondary school grade: 65%
  • Entry Exam: 35%

Entrance Exam (One of the following):

  • 19 -  Mathematics A


  • 19 – Mathematics A
  • 04 - Economy


  • 19 – Mathematics A
  • 10 - Descriptive Geometry

Exams (One of the following):

  • 635- Mathematics A


  • 635 - Mathematics A 
  • 712 - Economy A


  • 635 - Mathematics A
  • 708 - Descriptive Geometry A

Minimum Grades:

  • Applicants Grade: 100
  • Entry exam grade: 95

Prerequesites: None

General Quota Last Entry Grade 2022-23

  • 1st phase: 16,88
  • 2nd phase: 17,26

Evaluation rules


1º year - Autumn semester
Code Name ECTS
100001 Linear Algebra 4.0
100152 Computers' Architecture 4.0
100151 Foundational aspects of data science 4.0
100150 Introduction to Programming 7.0
100094 Information Systems 6.0
1º year - Spring semester
Code Name ECTS
100008 Mathematical Analysis I 5.0
100010 Mathematical Analysis II 7.0
100035 Personal Development I 2.0
100156 Statistics and Probability Distributions 6.0
100159 Introduction to Artificial Intelligence 5.0
100158 Introduction to Computational Thinking and Data Science 4.0
100157 Programming for Data Science 6.0
2º year - Autumn semester
Code Name ECTS
100160 Algorithms and Data Structures 6.0
100163 Machine Learning I 6.0
100012 Databases 6.0
100161 Statistical Inference 6.0
100162 Data preprocessing and visualization 6.0
2º year - Spring semester
Code Name ECTS
100165 Optimization Algorithms 6.0
100164 Regression Analysis 6.0
100167 Machine Learning II 6.0
100166 Big Data Storage 6.0
100086 Forecasting Methods 6.0
3º year - Autumn semester
Code Name ECTS
100172 Big Data Analysis 6.0
100169 Deep Learning 6.0
100168 Ethical, Social and Legal Aspects of Artificial Intelligence 2.0
100040 Personal Development II 2.0
100171 Capstone Project 8.0
100170 Text Mining 6.0
3º year - Spring semester
Code Name ECTS
100153 Network Analysis 6.0
100048 Informatics and Information Law 4.0
100051 Entrepreneurship and Project Analysis 4.0
100148 Geospatial Analytics 4.0
100253 Innovation Management 4.0
100064 Risk Management 6.0
100155 Process Intelligence 4.0
100105 Web Marketing and E-business 4.0
100134 Digital Innovation Projects 4.0
100092 Information Systems Seminar 6.0
100103 Web Analytics 4.0