Data Science (DSC)

Entry points, 1st year, Common Courses

Academic year 2023/2024

KTH Royal Institute of Technology (KTH)

Programme website

Programme Lead: Henrik Boström, bostromh@kth.se

FIRST SEMESTER

Compulsory CoursesECTS
Machine Learning 7.5
Programming for Data Science7.5
Research Methodology and Scientific Writing7.5
Elective Courses 
Scalable Machine Learning and Deep Learning7.5
Visualisation7.5
Image Analysis and Computer Vision7.5
Machine Learning, Advanced Course7.5
Statistical Methods in Applied Computer Science6
I&E 
Open and User Innovation7.5
Internet Marketing7.5


SECOND SEMESTER

Compulsory CoursesECTS
Data Mining, Basic Course7.5
Elective Courses 
Learning Machines7.5
Distributed Computing, Peer-to-Peer and GRIDS7.5
Distributed Systems, Advanced Course7.5
Search Engines and Information Retrieval Systems9
Artificial Neural Networks and Deep Architectures7.5
Deep Learning in Data Science7.5
Language Engineering6
Artificial Intelligence6
I&E 
Technology-based Entrepreneurship7.5
e-Business Strategies7.5

Technical University of Madrid (UPM)

Programme website

Programme Lead: Marta Patiño

FIRST SEMESTER

Courses (30 ECTS)ECTS
I&E6
Cognitive systems4.5
Statistical data analysis4.5
Cloud Computing and Big Data Ecosystems Design4.5
Big Data6
Machine Learning4.5


SECOND SEMESTER

I&E (18 ECTS)ECTS
I&E18
Elective Courses (12 ECTS) 
Data Science Seminars4.5
Data acquisition4.5
Information retrieval, extraction and integration4.5
Graph analysis and social networks3
Deep learning3

Université Côte d’Azur (UCA)

Programme website

Programme Lead: Francoise Baude, francoise.baude@unice.fr

FIRST SEMESTER

Compulsory Courses
(all are taught in English except those with the * sign). Students need to get at least an average mark of 10/20 in a block in order to get the corresponding ECTS.
ECTS
Data science 1 (6 ECTS, each course inside is accounted for 3 coefficient) 
Modelisation and optimisation in machine learning3
Technologies for massive data3
Elective Courses 
Electives Data science 1 (15 ECTS, each course listed inside is accounted for 3 coefficient) 
Single subject or interdisciplinary project (personal project in group or individual)3
Students can choose courses belonging to some topics structured this way: 
Data processing supporting technologies:
- Computer networks
- Relational databases (taught in French)*
- BD vers big data (taught in French)*
- Algorithmic approach to distributed computing
- Parallelism
- Content distribution in wireless networks
- Evolving Internet
- Software architecture for the cloud (taught in French)*
- Large Scale Distributed Systems
- Middleware for the Internet of things
- Peer to Peer
- Blockchain and privacy
- Virtualised infrastructure in Cloud computing
3
Data modelling and analysis:
- Data Science (challenges and industrial experiences)
- Numerical interpolation (taught in French)*
- Partial differential equations (taught in French)*
- Stochastic processes (taught in French)*
- Problem solving introduction and AI Game
3
Application of data science:
- Computational Linguistics
- Data mining for networks
- Analysis and indexing of images and videos in big data systems: from shallow to deep learning
- Compression, analysis and visualisation of multimedia content
- Multimedia data management
- Web of data
- Semantic web
- Knowledge engineering
3
I&E 
Innovation and Entrepreneurship 1 (9 ECTS, each course inside is accounted for 3 coefficient) 
Entrepreneurship Introduction3
Basics in Innovation and Entrepreneurship3
Business Development Lab Introduction3


SECOND SEMESTER

Compulsory CoursesECTS
Elective Data science 2 (6 ECTS, each course inside is accounted for 3 coefficient)18
Personal project in group or individual (can be continuation of semester 1 project)3
Students can choose courses belonging to some topics structured this way: 
Data processing supporting technologies:
- Programmation parallèle (taught in French)*
- Réseaux avancés et Middleware (taught in French)*
- Communication and Concurrency
- BD vers Big Data avancé (taught in French)*
- Software engineering
- Security (taught in French)*
3
Data modelling and analysis:
- Augmented reality (taught in French)*
- Optimisation
- Graphs
3
Application of data science
- Winter school (on Complex networks)
3
I&E 
Innovation and Entrepreneurship 2 (6 ECTS) 
Digital Business3
Digital IP & Law3
Innovation and Entrepreneurship 3 (9 ECTS) 
Business Development Lab5
Summer School4

Polytechnic University of Milan (POLIMI)

Programme website

Programme Lead: Paolo Cremonesi, paolo.cremonesi@polimi.it

FIRST SEMESTER

Mandatory Courses (21 ECTS)ECTS
DATA BASES 25
SOFTWARE ENGINEERING 25
Elective Courses - 3 choices (15 ECTS) 
SOFT COMPUTING5
DISTRIBUTED SYSTEMS5
RECOMMENDER SYSTEMS5
PRINCIPLES OF PROGRAMMING LANGUAGES5


SECOND SEMESTER

Mandatory Courses (21 ECTS)ECTS
MACHINE LEARNING6
COMPUTING INFRASTRUCTURES5
Elective Courses (10 ECTS) 
DATA MINING AND TEXT MINING5
ADVANCED ALGORITHMS AND PARALLEL PROGRAMMING5


I&E MINOR, INNOVATION & ENTREPRENEURSHIP

Mandatory Courses (24 ECTS)ECTS
STRATEGY & MARKETING10
DESIGNING DIGITAL BUSINESS INNOVATION LAB10
I&E SUMMER SCHOOL4


Notes:
- The I&E Minor will be increasingly offered in a blended format, with integrative online modules and innovative teaching methods (blended I&E Education).
- In evaluating students’ previous BSc studies, the Admission Committee at POLIMI can provide minor directions on the individual study plans, regarding the choice of courses to be attended.

Aalto University (Aalto)

Programme website

Programme Lead: Wilhelmiina Hämäläinen; wilhelmiina.hamalainen@aalto.fi 

FIRST SEMESTER

Compulsory Major Courses (19 ECTS)ECTS
Introduction Course for Master's students: Academic Skills1
Language course: Compulsory degree requirement, both oral and written requirements3
Machine Learning: Supervised Methods5
Principles of Algorithmic Techniques5
Methods of Data Mining5
Compulsory I&E Courses (7 ECTS) 
Introduction to Digital Business and Venturing3
Digital Business Management4

Optional Major Courses (select at least 7 ECTS over the two semester)

 
Bayesian Data Analysis5
Computer Vision5
Cloud Software and Systems5
Complex Networks5
Special Course in Computer Science1-10
Special Assignment in Computer Science1-10
Speech Processing5
Speech Recognition5
Applied Microeconometrics I6


SECOND SEMESTER

Compulsory Major Courses (10 ECTS)ECTS
Artificial Intelligence5
Deep Learning5

Compulsory I&E Courses (17 ECTS)

 
Startup Experience9
Global Business in the Digital Age4
ICT Innovation Summer School4
Optional Major Courses (select at least 7 ECTS over the two semesters) 
Machine Learning: Advanced Probabilistic Methods5
Kernel Methods in Machine Learning5
Information Visualization5
Programming Parallel Computers5
Special Course in Computer Science1-10
Special Assignment in Computer Science1-10
Statistical Inference5
Statistical Natural Language Processing5
Data Science for Business6
Topics in Economic Theory and Policy6
Digital Marketing6


Total for the whole year: 60 ECTS

Note for exit year at partner university: According to Finnish legislation, a master's thesis is a public document and its contents cannot be confidential. Therefore, the material of the thesis must be chosen so that it does not include any information that could be classified as a business secret of the financing company. More information about Master's thesis process for Aalto entry students here.

Eötvös Loránd University (ELTE)

Programme website

Programme Lead: Tomáš Horváth, tomas.horvath@inf.elte.hu

FIRST SEMESTER

Compulsory CoursesECTS
Introduction to Data Science5
Foundations of Data Science4
Data Models and Databases4
Electives Courses 
Software Technology5
Design and Analysis of Algorithms 5
Computer Graphics5
Image and Signal Processing 5
Introduction to Vehicles and Sensors 5
I&E 
I&E Basics (Barbara Hegyi)5
Business Development Lab I. (Udo Bub)5


SECOND SEMESTER

Compulsory CoursesECTS
Machine Learning5
Optimization for Data Science4
Electives Courses 
Embedded and Real-Time Systems 5
Data Mining in Smart Systems 5
3D Computer Vision 5
Artificial Intelligence in Processes and Automation 5
I&E 
I&E Specialisation (Barbara Hegyi)5
Business Development Lab II. (Udo Bub)5

University of Rennes 1 (UR1)

Programme website

FIRST & SECOND SEMESTER

Mandatory and Elective Courses

ECTS
Innovation and Entrepreneurship Basics5
Business Development Laboratory 15
Basics of Data Analysis6
Advanced Databases4
Operations Research5
Object Oriented Analysis and Design5
Intangible assets management5
Business Development Laboratory 2     5
Summer School4
Machine Learning I5
Semantic Web Technologies5
Database Security5
Technological Watch5

University of Twente (UT)

Programme website

Programme Lead: Dr. Maurice van Keulen: https://people.utwente.nl/m.vankeulen

FIRST & SECOND SEMESTER

CoreECTS
Managing Big Data5
Data Science5
Basic Machine Learning5
Information Theory and Statistics5
Computer Ethics5
Core I&E courses (shared with other EIT masters) 
Innovation and Entrepreneurial Finance for EIT students5
Business Development Lab I5
Business Development Lab II5
Advanced (at least 4) 
Advanced Machine Learning5
Foundations of Information Retrieval5
Service-oriented Architecture with Web services5
Architectures of Information Systems5
Research Experiments in Databases and Information Retrieval5
Probabilistic programming5

University of Turku (UTU)

The University of Turku provides an entry point for the EIT Digital master program in Data Science starting from the academic year 2022-2023.

The entry year courses are the following:

FIRST & SECOND SEMESTER

Compulsory Major Courses (25 ECTS)ECTS
Statistical Data Analysis5
Data Analysis and Knowledge Discovery5
Evaluation of Machine Learning Methods5
Introduction to Deep Learning5
Machine Learning and Pattern Recognition5
Electives (select 10 ECTS) 
Statistical and Probabilistic Programming5
Machine Learning and Algorithmics Seminar5
Acquisition and Analysis of Biosignals5
In addition, a variety of other courses qualifying as electives are available annually. Electives are chosen individually for each student when their personal study plan is made. 
Minor: I&E - Innovation and Entrepreneurship (25 ECTS) 
Introduction to Innovation and Business5
Finding Innovative Business Ideas3
Turning Ideas into Innovative Business7
Enterprise Architecture6
EIT Digital Summer School (during the summer between the entry and exit years)4

Exit points, 2nd year, Specialisation

Academic year 2024/2025

KTH Royal Institute of Technology (KTH)

Programme website

Programme Lead: Henrik Boström, bostromh@kth.se

SPECIALISATION: Distributed Systems & Data Mining for Big Data

The Distributed Systems & Data Mining for Big Data specialisation focuses on providing students with analytical and programming skills to be able to build systems that efficiently process big data. After completing the specialisation at KTH, the students will be able to effectively design and implement systems to handle data at any stage in the data mining process, from batch-oriented to real-time stream processing. Students will be able to write efficient programs that extract useful information from big data. They will acquire deep skills in subfields of data mining such as mining graphs, text and streaming data as well as scalable learning algorithms.

The students work with well-known platforms such as Hadoop, Flink, Spark, GraphLab, Mahout, and H20. There are frequent guest lectures from the many companies that are active in data science in the Stockholm region (some of which are listed below).

For the 2nd semester, students are offered practical industrial experience in cooperation with Stockholm-based companies, such as Ericsson, Spotify AB, King.com Ltd, Scania and Oracle (MySQL), or research-oriented projects, e.g., at RISE SICS and Ericsson Research. These companies and organisations, as well as several fast-moving start-ups, already cooperate with KTH on data science projects and in the context of this master's programme.

THIRD & FOURTH SEMESTER

Compulsory CoursesECTS
Data-Intensive Computing7.5
Data Mining7.5
ICT Innovation Study Project6
Research Methodology and Scientific Writing7.5
Degree Project in Computer Science and Engineering, specialising in ICT Innovation, Second Cycle30
Degree Project in Electrical Engineering, specialising in ICT Innovation, Second Cycle30
Elective Courses 
Visualisation7.5
Artificial Intelligence6
Language Engineering6
Image Analysis and Computer Vision7.5
Deep Learning in Data Science7.5
Machine Learning, Advanced Course7.5
Artificial Neural Networks and Deep Architectures7.5
Statistical Methods in Applied Computer Science6
Search Engines and Information Retrieval Systems9
Distributed Systems, Advanced Course7.5
Distributed Computing, Peer-to-Peer and GRIDS7.5
Learning Machines7.5
Scalable Machine Learning and Deep Learning7.5


Henrik Boström is Professor of Computer Science - data science systems at KTH Royal Institute of Technology. His research focuses on machine learning algorithms and applications, in particular ensemble learning, prediction with confidence (conformal prediction) and interpretable and explainable machine learning. He is action editor of the journals Machine Learning and Data Mining and Knowledge Discovery. He regularly acts as senior programme committee member of some of the most prominent conferences in the area, including SIGKDD, IJCAI, AAAI.

Technical University of Madrid (UPM)

Programme website

Programme Lead: Marta Patiño

SPECIALISATION: Infrastructures for Large Scale Data Management and Analysis

The Infrastructures for Large Scale Data Management and Analysis specialisation focuses on how to use large scale data management and big data infrastructures for processing, storing and analysing huge amounts of data as well as building new applications on top of them.
Students will learn:

  • How to use data streaming systems, persistent queues, batch processing for large clusters, large distributed databases among other technologies.
  • How to combine these tools to build ecosystems in which applications will be able to deal with the large amount of data that is being produced today and that it is increasing at a high pace due to the number of connected devices that will be available and producing data.
  • How to gain experience with data analytics in order to get new insights and value from the produced data.

Students will be able to do internships and cooperate with large companies like Telefonica, Indra, Atos and also with startups from UPM in the area of Big Data such as Localidata and LeanXcale. Localidata focuses on the value data chain. LeanXcale provides a leading-edge Real-Time Big Data Analytics platform.

THIRD & FOURTH SEMESTER

Courses (24 ECTS)ECTS
Data Analysis4.5
Large Scale Systems Project3
Open Data and Knowledge Graphs6
Large Scale Data Management4.5
Deep Learning3
Massively Parallel Machine Learning3


Marta Patiño is professor at UPM. She is Distributed Systems co-director and co-founder of LeanXcale startup on Real-Time Big Data analytics. She is also funder member of the research center for Open Middleware. She is co-inventor of 3 patent applications. She has coordinated several national projects and the EU funded projects LeanBigData, CoherentPaaS, and CumuloNimbo. She is co-author of the book Database replication and published over 100 papers in international conferences and journals such as SIGMOD, VLDB Journal, ACM Trans. On Database Systems, ACM Trans. On Computer Systems, IEEE Trans. On Parallel and Distributed Systems, etc. Her research areas include scalable transactional processing, scalable complex event processing, online analytical processing, cloud computing, big data, fault-tolerance.

Université Côte d’Azur (UCA)

Programme website

Programme Lead: Francoise Baude, francoise.baude@unice.fr

SPECIALISATION: Multimedia and Web Science for Big Data

THIRD & FOURTH SEMESTER

All courses are taught in English despite the title in French.
Students need to get at least an average mark of 10/20 in a block (please see the * sign) in order to get the corresponding ECTS.

Compulsory Courses (24 ECTS)ECTS
UE 1 “Unité d’enseignement 1” (course block) Total 6 ECTS, each course inside has coefficient 2)  
Technologies for massive data2
Compression, analysis and visualization of multimedia content2
Analysis and indexing of images and videos in big data systems: from shallow to deep learning2
UE 3 “Unité d’enseignement Project”  
Personal project in data science6
UE I&E 
I&E study (belongs to the I&E minor)6
Elective Courses (12 ECTS) 
UE 2 “Unité d’enseignement Options” Elective course block (Total 12 ECTS, each course inside coefficient 2 or 4) 
Students can choose courses belonging to some topics structured this way: 
Data modelling and analysis:  
Statistical learning methods4
Statistical computational methods4
Data science (challenges and industrial experiences)2
Data mining2
Data processing supporting technologies:  
Large scale distributed systems2
Middleware for the Internet of Things2
Blockchain and privacy2
Peer to peer2
Virtualized infrastructure in cloud computing2
Application of data science, in particular on multimedia content and data on the web: 
Data mining for networks2
Web of data2
Semantic web2
Knowledge engineering2
Sécurité des applications web (in French)2
Ingénierie 3D* (taught in French)2
3D engineering* (taught in French)2
Multimedia content management2
Images advanced management* (taught in French)2
  • Frédéric Précioso
  • Lionel Fillatre
  • Françoise Baude
  • Fabien Gandon

Aalto University (Aalto)

Programme website

Programme Lead: Wilhelmiina Hämäläinen; wilhelmiina.hamalainen@aalto.fi 

SPECIALISATION: Machine Learning, Big Data Management and Business Analytics

THIRD SEMESTER

Compulsory Major Courses (9 ECTS)ECTS
Introduction course for Master's students: Career and working life skills1
Language course: Compulsory degree requirement, both oral and written requirements3
Select one of the following:5
Machine Learning: Supervised Methods5
Bayesian Data Analysis5
Methods of Data Mining 
Compulsory I&E Courses (6 ECTS) 
I&E Study Project6
Optional Major Courses (15 ECTS) 
Principles of Algorithmic Techniques5
Complex Networks5
Speech Recognition5
Capstone: DigitalSM Challenge6
Kernel Methods in Machine Learning5
Special Course in Computer Science1-10
Web Software Development6
Research Project in Machine Learning, Data Science and Artificial Intelligence5-10
Special Assignment in Computer Science1-10
Individual Studies in Computer Science1-10
Seminar in Computer Science5
Artificial Intelligence5
Information Visualization5
Programming Parallel Computers5
Digital Marketing6
Data Science for Business I6
Deep Learning5
Computer Vision5
Prediction and Time Series Analysis5
Reinforcement Learning5


FOURTH SEMESTER

CourseECTS
CS.thes - Master’s Thesis30


Total for the whole year: 60 ECTS

Eötvös Loránd University (ELTE)

Programme website

Programme Lead: Tomáš Horváth, eszterkiss@inf.elte.hu

SPECIALISATION: Real-time Data Analytics 

The Real-time Data Analytics specialisation focuses on state-of-the-art solutions for supporting real-time data driven decision making. The (research) areas covered by the specialisation concern stream mining, sensor data analytics, complex event processing and network analysis.

Real-time data analytics can be found in many application domains ranging from Industry 4.0 through business to healthcare, playing a crucial role in areas such as control systems of self-driving cars, prediction of assembly line malfunctions, fraud detection in financial transactions or early recognition of anomalies in health monitoring.
Students will be familiarised with:

  • Large scale and in-memory databases.
  • The ecosystem of distributed processing and its components with particular focus on open source development of these components.
  • Business analytics and reporting tools over aggregated data from multiple sources.
  • Visual analytics tools and their components.
  • The utilisation and adaptation of machine learning and data mining methods in real-time scenarios.

Students will learn to use and utilise open-source technologies such as Hadoop, Spark, Flink, ElasticSearch, Kibana or Tableau, just to name a few. In addition to good theoretical basics of clustering, prediction, pattern mining and pattern recognition techniques, students will have a chance to gain practical experiences during internships at our industrial partners with whom we have well-established cooperation.

THIRD & FOURTH SEMESTER

Courses (25 ECTS)ECTS
Stream Mining5
Sensor Data Analytics5
Network Science5
Advanced Machine Learning5
Open-source Technologies for Real-time Data Analytics5


Tomáš Horváth is the head of the Data Science and Engineering Department, established in September 2016 by Deutsche Telekom, of the Faculty of informatics of the Eötvös Loránd University in Budapest, Hungary. He received his MSc and PhD degrees at the Pavol Jozef Šafárik University in Košice, Slovak Republic, in 2002 and 2008, respectively. He was on a post-doc internship at the Information Systems and Machine Learning lab of the University in Hildesheim, Germany, from 2009 to 2012. From 2015 to 2016 he received a post-doc grant at the Department of Computer Science, University of São Paulo in São Carlos, Brazil. His research interests include relational learning, rule-based and monotone classification techniques, pattern mining, recommender systems and personalization. Recently, he is focusing his work on meta-learning techniques and automated machine learning approaches.

University of Trento (UNITN)

Programme website

Contact: eitmaster@unitn.it

SPECIALISATION: Big Data Variety and Veracity

Data Science has increasingly become an integrated approach where modern scientists analyse data collected from many different sources. Before this data can be leveraged by data analytics, it has to be cleaned, transformed and integrated. This kind of data preparation is challenging, laborious, time consuming and error-prone. It is estimated to cost data scientists 50% and 80% of their time and effort. The reason is the heterogeneity and quality issues that inherently exist in the data. It, thus, comes as no surprise that Variety and Veracity are considered two fundamental characteristics of Big Data (alongside Volume and Velocity).

The Big Data Variety and Veracity specialisation at the University of Trento aims at providing the students with all the necessary knowledge to be able to understand, use, and develop tools, techniques and methodologies for efficiently and effectively coping the variety and the veracity of big datasets. Throughout the courses the students will learn about the different kinds of challenges faced in real scenarios, the existing algorithmic approaches, the software solutions that are available, the commercial tools that one can use, and the evaluation methodologies that can be applied. At the end of the programme, they will be able to identify the data management challenges in real-world situations, select the best solution for the task at hand, and apply that solution successfully.

In the first semester, the students with take a number of technical courses that will provide them with the necessary specialisation foundations. The lectures are often enriched by external experts from industry and academia. In the second semester the students will obtain their industrial experience by performing their internship in a company and also materializing their thesis work, which may be on a separate topic than the internship or a related topic that can be seen as an extension of the internship work.

THIRD SEMESTER

CoursesECTS
I&E Studies6
Plus 3 courses from the following list (that have not already taken during the first year of studies): 
Quantum Machine Learning6
Data Mining6
Knowledge and Data Integration6
Web Architectures6
Affective computing6
Privacy and Intellectual Property Rights6


FOURTH SEMESTER

 ECTS
Thesis & Internship30

University of Rennes 1 (UR1)

SPECIALISATION:  Artificial Intelligence & Data Mining for Business Intelligence

Our exit year focuses on artificial intelligence methods in general, and data mining in particular, to address the challenges of business intelligence. Those challenges include numerous tasks that are covered by our courses, such as pre-processing and storing decisional data in data ware-houses, managing Big Data in the cloud, learning models and perform inferences, mining knowledge out of data, and visualizing both data and learned knowledge. The focus is put on the data science methodology rather on the arcane details of specific methods, although the courses will bring fundamental knowledge about the main AI & data mining methods.

The students will learn to analyse a business intelligence problems, and to make the appropriate choices among the numerous existing methods and tools. They will also learn to conduct the data science workflows, and to analyse the results in cooperation with domain experts. At Univ. Rennes 1, we have already built for decades a strong enterprise culture where students acquire competencies for communicating with non-IT domain experts, especially in business. Those competencies are highly valued and recognized by companies, who are also involved in the continuous improvement committee of the existing cursus on which this master is founded.

Our specialisation is backed by IRISA, one of the biggest computer science research lab in France with 800 people. Two research departments and 12 research groups are related to data science. Most teachers in the master are also researchers in one of those groups, ensuring quickly evolving course contents.

Mandatory and Elective CoursesECTS
I&E study6
Machine Learning II5
Data Mining5
Indexing and Visualization3
Datawarehouses3
Cloud and Big Data Management3
Case Study in Data Science5
Master Thesis30

University of Twente (UT)

Programme website

Programme Lead: Dr. Maurice van Meulen, https://people.utwente.nl/m.vankeulen

SPECIALISATION:  Data Science for Persona Information

Main topics in courses as well as in final projects are covered such as: health and sports, wellbeing, biometrics and privacy.

With this master program, the students will get acquainted with and work on the following topics: big data, data analytics, information inference, machine learning, context-aware applications, smart services. With data science, one learns how to dig for value in data by analysing various data sources. With service engineering, one learns how to design services that effectively use system capabilities to satisfy user needs and requirements. Information systems that can use the results of data science to get more value out of data and become context-aware may turn traditional services into smart services. Applications of this in various domains such as pervasive health, well-being, intelligent transportation, logistics, and business intelligence.

The variety of subjects arises from the fact the program has different flavours and hence allows the students to have an orientation towards each of them, being a mathematical, a computer science, an electrical engineering and a business information flavour. 

The exit point will provide, in-depth theoretical and technical skills for becoming:

  • a) specialist in specific kinds of data, such as natural language text, image data, geographic data, sensor data, networked data
  • b) designer of smart services
  • c) designer of data science algorithms
  • d) multi-disciplinary.

Apart from the final project, elective courses are available such as

Telemedicine and Data Analysis for Monitoring
Technology for Health
Remote Monitoring and coaching
Signals with Information
Introduction to Biometrics
Image Processing and Computer Vision
Advance Computer Vision & Pattern Recognition
Internet of Things
Remote Monitoring and Coaching
I&E minor thesis
Data Science group


Education and research in this setting of Data Science requires a fundamental inter-disciplinary approach bridging fields like computational statistics, machine learning, image and signal processing, information retrieval, and data processing and management. Therefor a group was formed to focus on this. The mission of the group is to work on explainable data science by developing methods for autonomous, reliable and robust gathering, preparation, and analysis of the data, to enable relevant, trustworthy and explainable results.

University of Turku (UTU)

The second-year at the University of Turku offers a graduation project (Final Degree Project). The graduation project includes an internship at a company or a research institute and results in a master’s thesis with a strong innovation and entrepreneurship dimension.

SPECIALISATION: Medical Data Science

The Medical Data Science specialization brings together skills and principles from computer science, data analytics, and machine learning as well as from medical sciences to build theoretical understanding and practical competences in the field. On the practical side, a special emphasis is put on both traditional biomedical foci on signal processing, and medical machinery and contemporary health-related internet-of-things technologies and gives tools to work with the near future ICT technologies. This track has a strong focus on data analytics and medical Internet-of-Things while leveraging from machine learning, software engineering, and embedded electronics that are strongly represented at the University of Turku.

The students graduating from this cross-disciplinary program will have acquired scientific and analytical skills, expertise in present theories, and up-to-date technologies as well as practical skills, including teamwork, leadership, and interpersonal skills in an international environment. The acquired methodological skills and advanced knowledge allow the students to continue their career paths in academia or industry.

At the University of Turku, the EIT students select 24 ECTS from the following set of courses, taking into account the content of the entry year studies:

THIRD & FOURTH SEMESTER

Compulsory Courses (15 ECTS)ECTS
Acquisition and Analysis of Biosignals5
Biosignal Analytics3
Analytics and Programming of Health Wearables5
Special Course on Health Technology2
Elective Courses (select at least 9 ECTS): 
Usability, User Experience and Analytics5
Mixed Reality and Metaverse5
Computer Vision and Sensor Fusion5
Statistical and Probabilistic Programming5
In addition, a large variety of other courses qualifying as electives are available annually. Especially the courses offered by the faculty of medicine for exchange students: https://opas.peppi.utu.fi/en/programme/94253 may fit very well for the medical technology studies. Electives are chosen individually for each student when their personal study plan is made. 
Compulsory I&E and thesis:  
I&E Study: a project based course in I&E minor6
Master’s Thesis and Internship30

Budapest University of Technology and Economics (BME)

Programme website

Programme Lead: Péter Antal, antal@mit.bme.hu

SPECIALISATION: Human-centred intelligent data analysis (HCIDA)

The declared focus of the European Union on ethical data analysis and ethical artificial intelligence (AI), exemplified by GDPR and the AI Act requires entrepreneurs to understand the moral, legal, and regulatory aspects, in addition to mastering the technology of a product. The one-year curriculum provides solid foundations for interdisciplinary dimensions and corresponding theoretical concepts and technologies. The HCIDA programme is part of the 'Data Science and Artificial Intelligent' (DS-AI) specialization in the Computer Science Engineering Master’s programme at the BME. The curriculum also overlaps* with the Human Centered Artificial Intelligence Masters (HCAIM) programme (https://hcaim.bme.hu/); thus, its accomplishment can grant the BME HCAIM certificate.

THIRD SEMESTER (fall, 20 ECTS):

Compulsory coursesECTS
Artificial intelligence and ethics*2
Artificial Intelligence and Law2
Innovation & Entrepreneurship Study6
Diploma Thesis Design 110

FOURTH SEMESTER (spring, 20 ECTS):

Compulsory courses 
Diploma Thesis Design 220
Elective courses (min. 5 ECTS) 
Privacy and Security in machine learning*5
Trustworthy AI and data analysis*5
Intelligent data analysis and decision support*5
Engineering Ethics*2
Complex Federated Models in Machine Learning*3
User-centered data-driven and AI-based systems*5
Artificial general intelligence*3

Total credits for the whole exit year: 60 ETCS

Péter Antal founded the Computational Biomedicine Laboratory (ComBineLab) in 2009, and has been the head of the Artificial Intelligence Group since 2019.  He has published around 150 papers in intelligent data analysis, Bayesian computation, causality research, decision support, bioinformatics, and chemoinformatics.

Scroll up

Co-Funded by the European Union