BD (2025 - 2027)
Competences
The competence framework states the academic capabilities this programme is designed to build. Each entry keeps its linked learning outcomes visible so the structure can be followed without leaving the page.
Key competences
5 entries
CC1
Multilingual competences
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Multilingual competences
CC2
Competences in science, technology, engineering and mathematics
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Competences in science, technology, engineering and mathematics
CC3
Personal, social and learning-to-learn competences
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Personal, social and learning-to-learn competences
CC4
Entrepreneurial competences
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Entrepreneurial competences
CC5
Cultural awareness and expression competences
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Cultural awareness and expression competences
Professional competences
6 entries
CP1
operate with fundamental concepts in the field of mathematical modelling and statistical analysis, as well as the use them in practical contexts
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operate with fundamental concepts in the field of mathematical modelling and statistical analysis, as well as the use them in practical contexts
CP2
identify, implement and use algorithms for extracting patterns from data using statistical methods and machine learning techniques
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identify, implement and use algorithms for extracting patterns from data using statistical methods and machine learning techniques
CP3
understand and apply the principles of distributed data processing and to use high-performance computing architectures
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understand and apply the principles of distributed data processing and to use high-performance computing architectures
CP4
use platforms and technologies specific to the processing of large volumes of data
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use platforms and technologies specific to the processing of large volumes of data
CP5
design and implement scalable applications
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design and implement scalable applications
CP6
adapt solutions based on data-driven approaches for problems specific to a particular field of application
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adapt solutions based on data-driven approaches for problems specific to a particular field of application
Transversal personal competences
3 entries
CT1
plan and organize work efficiently while respecting deadlines
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plan and organize work efficiently while respecting deadlines
CT2
synthesize, interpret and critically analyze obtained results
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synthesize, interpret and critically analyze obtained results
CT3
Compliance with ethical norms specific to the field of activity and with data security and confidentiality rules
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Compliance with ethical norms specific to the field of activity and with data security and confidentiality rules
Transversal interpersonal competences
2 entries
CT4
Identifying the role in an interdisciplinary team and taking on responsibilities corresponding to the professional and personal profile
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Identifying the role in an interdisciplinary team and taking on responsibilities corresponding to the professional and personal profile
CT5
communicate and transfer knowledge between specialists belonging to different fields
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communicate and transfer knowledge between specialists belonging to different fields
Transversal global citizenship competences
1 entries
CT6
Engaging in activities for diverse social groups and using professional expertise to initiate/carry out projects and activities that support the process of digitization and education for a digitized society
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Engaging in activities for diverse social groups and using professional expertise to initiate/carry out projects and activities that support the process of digitization and education for a digitized society
Learning outcomes
Learning outcomes translate the competence framework into observable academic results. Each entry keeps its connected competences and subject anchors visible so the curriculum can be followed in both directions.
Knowledge
6 entries
C1
Knowledge, understanding and use in a practical context of concepts of probabilistic modeling (probability distributions, Markov models), statistics (descriptive analysis, inference techniques, statistical tests, regression) and linear and nonlinear optimization techniques;
Taxonomy not set
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Knowledge, understanding and use in a practical context of concepts of probabilistic modeling (probability distributions, Markov models), statistics (descriptive analysis, inference techniques, statistical tests, regression) and linear and nonlinear optimization techniques;
C2
Knowledge of statistical methods specific to different types of processing and understanding of how machine learning algorithms can be used;
Taxonomy not set
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Knowledge of statistical methods specific to different types of processing and understanding of how machine learning algorithms can be used;
C3
Understanding how large volumes of data can be processed in a distributed manner and the principles underlying high-performance computing;
Taxonomy not set
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Understanding how large volumes of data can be processed in a distributed manner and the principles underlying high-performance computing;
C4
Understanding how platforms specific to the processing of large volumes of data work;
Taxonomy not set
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Understanding how platforms specific to the processing of large volumes of data work;
C5
Understanding how the computational complexity of an algorithm is determined and the specific requirements for scalability;
Taxonomy not set
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Understanding how the computational complexity of an algorithm is determined and the specific requirements for scalability;
C6
Understanding how decision models can be designed from data.
Taxonomy not set
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Understanding how decision models can be designed from data.
Skills
5 entries
A1
Using concepts from computer science, mathematics and statistics in defining models and designing data analysis strategies and interpreting results;
Taxonomy not set
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Using concepts from computer science, mathematics and statistics in defining models and designing data analysis strategies and interpreting results;
A2
Identifying statistical and machine learning techniques as well as appropriate IT tools for data processing and building decision models;
Taxonomy not set
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Identifying statistical and machine learning techniques as well as appropriate IT tools for data processing and building decision models;
A3
Designing, implementing and testing software modules suitable for processing and analyzing large volumes of data;
Taxonomy not set
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Designing, implementing and testing software modules suitable for processing and analyzing large volumes of data;
A4
Using distributed parallel processing principles in designing scalable applications;
Taxonomy not set
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Using distributed parallel processing principles in designing scalable applications;
A5
Using knowledge regarding the construction of data-driven models to develop decision support systems specific to different application domains.
Taxonomy not set
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Using knowledge regarding the construction of data-driven models to develop decision support systems specific to different application domains.
Responsibility
8 entries
R1
Responsibility to act in accordance with the interest of users;
Taxonomy not set
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Responsibility to act in accordance with the interest of users;
R2
Respecting the confidentiality of the employer and clients, but also protecting their intellectual property;
Taxonomy not set
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Respecting the confidentiality of the employer and clients, but also protecting their intellectual property;
R3
Correctly representing the level of competence and accepting tasks within its limits;
Taxonomy not set
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Correctly representing the level of competence and accepting tasks within its limits;
R4
Responsibility to respect the highest professional standards in data processing;
Taxonomy not set
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Responsibility to respect the highest professional standards in data processing;
R5
Maintaining autonomy, integrity and independence in professional opinions
Taxonomy not set
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Maintaining autonomy, integrity and independence in professional opinions
R6
Promoting the integrity and reputation of the profession, in accordance with the public interest;
Taxonomy not set
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Promoting the integrity and reputation of the profession, in accordance with the public interest;
R7
Continuous improvement in the practice of the profession;
Taxonomy not set
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Continuous improvement in the practice of the profession;
R8
Ethical, honest and collegial behavior in the practice of the profession.
Taxonomy not set
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Ethical, honest and collegial behavior in the practice of the profession.
Curriculum
The semester structure keeps the curriculum grouped by mandatory, elective, and optional subjects. Expand a row to inspect hours, subject type, linked learning outcomes, and any public syllabus.
Semester 1
Mandatory subjects
Elective subjects
Optional subjects
Volunteering I
2 credits
Databases
5 credits
Programming I
6 credits
Programming III
5 credits
Semester 2
Mandatory subjects
Elective subjects
Optional subjects
Semester 3
Mandatory subjects
Machine Learning
6 credits
Big Data Applications
6 credits
Data Science Industry Project
6 credits
Elective subjects
Predictive Models and Analytics
Text Mining
Introduction To Quantum Computing
Metaheuristic Algorithms
Statistical Methods For Clinical Studies
Computer Vision
Optional subjects
Volunteering III
2 credits
Semester 4
Mandatory subjects
Professional Practice
8 credits
Msc Thesis Preparation
15 credits
Scientific Seminar
7 credits
Elective subjects
(no elective groups)