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
188
Cicle
Bachelor (1st Cycle)
Degree
Bachelor
Access to other programs
Coordinator
Mauro Castelli
Opening date
September 2023
Vacancies
45
Fees
Schedule
Working Hours
Teaching language
English
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
Structure
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 |
Options | ||
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 |