Entry points, 1st year, Common Courses
Academic year 2023/2024
KTH Royal Institute of Technology (KTH)
Programme Lead: Henrik Boström, bostromh@kth.se
FIRST SEMESTER
Compulsory Courses | ECTS |
Machine Learning | 7.5 |
Programming for Data Science | 7.5 |
Research Methodology and Scientific Writing | 7.5 |
Elective Courses | |
Scalable Machine Learning and Deep Learning | 7.5 |
Visualisation | 7.5 |
Image Analysis and Computer Vision | 7.5 |
Machine Learning, Advanced Course | 7.5 |
Statistical Methods in Applied Computer Science | 6 |
I&E | |
Open and User Innovation | 7.5 |
Internet Marketing | 7.5 |
SECOND SEMESTER
Compulsory Courses | ECTS |
Data Mining, Basic Course | 7.5 |
Elective Courses | |
Learning Machines | 7.5 |
Distributed Computing, Peer-to-Peer and GRIDS | 7.5 |
Distributed Systems, Advanced Course | 7.5 |
Search Engines and Information Retrieval Systems | 9 |
Artificial Neural Networks and Deep Architectures | 7.5 |
Deep Learning in Data Science | 7.5 |
Language Engineering | 6 |
Artificial Intelligence | 6 |
I&E | |
Technology-based Entrepreneurship | 7.5 |
e-Business Strategies | 7.5 |
Technical University of Madrid (UPM)
Programme Lead: Marta Patiño
FIRST SEMESTER
Courses (30 ECTS) | ECTS |
I&E | 6 |
Cognitive systems | 4.5 |
Statistical data analysis | 4.5 |
Cloud Computing and Big Data Ecosystems Design | 4.5 |
Big Data | 6 |
Machine Learning | 4.5 |
SECOND SEMESTER
I&E (18 ECTS) | ECTS |
I&E | 18 |
Elective Courses (12 ECTS) | |
Data Science Seminars | 4.5 |
Data acquisition | 4.5 |
Information retrieval, extraction and integration | 4.5 |
Graph analysis and social networks | 3 |
Deep learning | 3 |
Université Côte d’Azur (UCA)
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 learning | 3 |
Technologies for massive data | 3 |
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 Introduction | 3 |
Basics in Innovation and Entrepreneurship | 3 |
Business Development Lab Introduction | 3 |
SECOND SEMESTER
Compulsory Courses | ECTS |
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 Business | 3 |
Digital IP & Law | 3 |
Innovation and Entrepreneurship 3 (9 ECTS) | |
Business Development Lab | 5 |
Summer School | 4 |
Polytechnic University of Milan (POLIMI)
Programme Lead: Paolo Cremonesi, paolo.cremonesi@polimi.it
FIRST SEMESTER
Mandatory Courses (21 ECTS) | ECTS |
DATA BASES 2 | 5 |
SOFTWARE ENGINEERING 2 | 5 |
Elective Courses - 3 choices (15 ECTS) | |
SOFT COMPUTING | 5 |
DISTRIBUTED SYSTEMS | 5 |
RECOMMENDER SYSTEMS | 5 |
PRINCIPLES OF PROGRAMMING LANGUAGES | 5 |
SECOND SEMESTER
Mandatory Courses (21 ECTS) | ECTS |
MACHINE LEARNING | 6 |
COMPUTING INFRASTRUCTURES | 5 |
Elective Courses (10 ECTS) | |
DATA MINING AND TEXT MINING | 5 |
ADVANCED ALGORITHMS AND PARALLEL PROGRAMMING | 5 |
I&E MINOR, INNOVATION & ENTREPRENEURSHIP
Mandatory Courses (24 ECTS) | ECTS |
STRATEGY & MARKETING | 10 |
DESIGNING DIGITAL BUSINESS INNOVATION LAB | 10 |
I&E SUMMER SCHOOL | 4 |
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 Lead: Wilhelmiina Hämäläinen; wilhelmiina.hamalainen@aalto.fi
FIRST SEMESTER
Compulsory Major Courses (19 ECTS) | ECTS |
Introduction Course for Master's students: Academic Skills | 1 |
Language course: Compulsory degree requirement, both oral and written requirements | 3 |
Machine Learning: Supervised Methods | 5 |
Principles of Algorithmic Techniques | 5 |
Methods of Data Mining | 5 |
Compulsory I&E Courses (7 ECTS) | |
Introduction to Digital Business and Venturing | 3 |
Digital Business Management | 4 |
Optional Major Courses (select at least 7 ECTS over the two semester) | |
Bayesian Data Analysis | 5 |
Computer Vision | 5 |
Cloud Software and Systems | 5 |
Complex Networks | 5 |
Special Course in Computer Science | 1-10 |
Special Assignment in Computer Science | 1-10 |
Speech Processing | 5 |
Speech Recognition | 5 |
Applied Microeconometrics I | 6 |
SECOND SEMESTER
Compulsory Major Courses (10 ECTS) | ECTS |
Artificial Intelligence | 5 |
Deep Learning | 5 |
Compulsory I&E Courses (17 ECTS) | |
Startup Experience | 9 |
Global Business in the Digital Age | 4 |
ICT Innovation Summer School | 4 |
Optional Major Courses (select at least 7 ECTS over the two semesters) | |
Machine Learning: Advanced Probabilistic Methods | 5 |
Kernel Methods in Machine Learning | 5 |
Information Visualization | 5 |
Programming Parallel Computers | 5 |
Special Course in Computer Science | 1-10 |
Special Assignment in Computer Science | 1-10 |
Statistical Inference | 5 |
Statistical Natural Language Processing | 5 |
Data Science for Business | 6 |
Topics in Economic Theory and Policy | 6 |
Digital Marketing | 6 |
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 Lead: Tomáš Horváth, tomas.horvath@inf.elte.hu
FIRST SEMESTER
Compulsory Courses | ECTS |
Introduction to Data Science | 5 |
Foundations of Data Science | 4 |
Data Models and Databases | 4 |
Electives Courses | |
Software Technology | 5 |
Design and Analysis of Algorithms | 5 |
Computer Graphics | 5 |
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 Courses | ECTS |
Machine Learning | 5 |
Optimization for Data Science | 4 |
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)
FIRST & SECOND SEMESTER
Mandatory and Elective Courses | ECTS |
Innovation and Entrepreneurship Basics | 5 |
Business Development Laboratory 1 | 5 |
Basics of Data Analysis | 6 |
Advanced Databases | 4 |
Operations Research | 5 |
Object Oriented Analysis and Design | 5 |
Intangible assets management | 5 |
Business Development Laboratory 2 | 5 |
Summer School | 4 |
Machine Learning I | 5 |
Semantic Web Technologies | 5 |
Database Security | 5 |
Technological Watch | 5 |
University of Twente (UT)
Programme Lead: Dr. Maurice van Keulen: https://people.utwente.nl/m.vankeulen
FIRST & SECOND SEMESTER
Core | ECTS |
Managing Big Data | 5 |
Data Science | 5 |
Basic Machine Learning | 5 |
Information Theory and Statistics | 5 |
Computer Ethics | 5 |
Core I&E courses (shared with other EIT masters) | |
Innovation and Entrepreneurial Finance for EIT students | 5 |
Business Development Lab I | 5 |
Business Development Lab II | 5 |
Advanced (at least 4) | |
Advanced Machine Learning | 5 |
Foundations of Information Retrieval | 5 |
Service-oriented Architecture with Web services | 5 |
Architectures of Information Systems | 5 |
Research Experiments in Databases and Information Retrieval | 5 |
Probabilistic programming | 5 |
University of Turku (UTU)
- Lead of the EIT Digital DM Programme at UTU: Tapio Pahikkala tapio.pahikkala@utu.fi
- Programme Coordinator: Päivi Rastas, pairit@utu.fi
- Coordinator for Innovation and Entrepreneurship (I&E) minor: Pasi Malinen, pasi.malinen@utu.fi
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 Analysis | 5 |
Data Analysis and Knowledge Discovery | 5 |
Evaluation of Machine Learning Methods | 5 |
Introduction to Deep Learning | 5 |
Machine Learning and Pattern Recognition | 5 |
Electives (select 10 ECTS) | |
Statistical and Probabilistic Programming | 5 |
Machine Learning and Algorithmics Seminar | 5 |
Acquisition and Analysis of Biosignals | 5 |
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 Business | 5 |
Finding Innovative Business Ideas | 3 |
Turning Ideas into Innovative Business | 7 |
Enterprise Architecture | 6 |
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 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 Courses | ECTS |
Data-Intensive Computing | 7.5 |
Data Mining | 7.5 |
ICT Innovation Study Project | 6 |
Research Methodology and Scientific Writing | 7.5 |
Degree Project in Computer Science and Engineering, specialising in ICT Innovation, Second Cycle | 30 |
Degree Project in Electrical Engineering, specialising in ICT Innovation, Second Cycle | 30 |
Elective Courses | |
Visualisation | 7.5 |
Artificial Intelligence | 6 |
Language Engineering | 6 |
Image Analysis and Computer Vision | 7.5 |
Deep Learning in Data Science | 7.5 |
Machine Learning, Advanced Course | 7.5 |
Artificial Neural Networks and Deep Architectures | 7.5 |
Statistical Methods in Applied Computer Science | 6 |
Search Engines and Information Retrieval Systems | 9 |
Distributed Systems, Advanced Course | 7.5 |
Distributed Computing, Peer-to-Peer and GRIDS | 7.5 |
Learning Machines | 7.5 |
Scalable Machine Learning and Deep Learning | 7.5 |
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 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 Analysis | 4.5 |
Large Scale Systems Project | 3 |
Open Data and Knowledge Graphs | 6 |
Large Scale Data Management | 4.5 |
Deep Learning | 3 |
Massively Parallel Machine Learning | 3 |
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 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 data | 2 |
Compression, analysis and visualization of multimedia content | 2 |
Analysis and indexing of images and videos in big data systems: from shallow to deep learning | 2 |
UE 3 “Unité d’enseignement Project” | |
Personal project in data science | 6 |
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 methods | 4 |
Statistical computational methods | 4 |
Data science (challenges and industrial experiences) | 2 |
Data mining | 2 |
Data processing supporting technologies: | |
Large scale distributed systems | 2 |
Middleware for the Internet of Things | 2 |
Blockchain and privacy | 2 |
Peer to peer | 2 |
Virtualized infrastructure in cloud computing | 2 |
Application of data science, in particular on multimedia content and data on the web: | |
Data mining for networks | 2 |
Web of data | 2 |
Semantic web | 2 |
Knowledge engineering | 2 |
Sécurité des applications web (in French) | 2 |
Ingénierie 3D* (taught in French) | 2 |
3D engineering* (taught in French) | 2 |
Multimedia content management | 2 |
Images advanced management* (taught in French) | 2 |
- Frédéric Précioso
- Lionel Fillatre
- Françoise Baude
- Fabien Gandon
Aalto University (Aalto)
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 skills | 1 |
Language course: Compulsory degree requirement, both oral and written requirements | 3 |
Select one of the following: | 5 |
Machine Learning: Supervised Methods | 5 |
Bayesian Data Analysis | 5 |
Methods of Data Mining | |
Compulsory I&E Courses (6 ECTS) | |
I&E Study Project | 6 |
Optional Major Courses (15 ECTS) | |
Principles of Algorithmic Techniques | 5 |
Complex Networks | 5 |
Speech Recognition | 5 |
Capstone: DigitalSM Challenge | 6 |
Kernel Methods in Machine Learning | 5 |
Special Course in Computer Science | 1-10 |
Web Software Development | 6 |
Research Project in Machine Learning, Data Science and Artificial Intelligence | 5-10 |
Special Assignment in Computer Science | 1-10 |
Individual Studies in Computer Science | 1-10 |
Seminar in Computer Science | 5 |
Artificial Intelligence | 5 |
Information Visualization | 5 |
Programming Parallel Computers | 5 |
Digital Marketing | 6 |
Data Science for Business I | 6 |
Deep Learning | 5 |
Computer Vision | 5 |
Prediction and Time Series Analysis | 5 |
Reinforcement Learning | 5 |
FOURTH SEMESTER
Course | ECTS |
CS.thes - Master’s Thesis | 30 |
Total for the whole year: 60 ECTS
Eötvös Loránd University (ELTE)
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 Mining | 5 |
Sensor Data Analytics | 5 |
Network Science | 5 |
Advanced Machine Learning | 5 |
Open-source Technologies for Real-time Data Analytics | 5 |
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)
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
Courses | ECTS |
I&E Studies | 6 |
Plus 3 courses from the following list (that have not already taken during the first year of studies): | |
Quantum Machine Learning | 6 |
Data Mining | 6 |
Knowledge and Data Integration | 6 |
Web Architectures | 6 |
Affective computing | 6 |
Privacy and Intellectual Property Rights | 6 |
FOURTH SEMESTER
ECTS | |
Thesis & Internship | 30 |
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 Courses | ECTS |
I&E study | 6 |
Machine Learning II | 5 |
Data Mining | 5 |
Indexing and Visualization | 3 |
Datawarehouses | 3 |
Cloud and Big Data Management | 3 |
Case Study in Data Science | 5 |
Master Thesis | 30 |
University of Twente (UT)
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)
- Lead of the EIT Digital DM Programme at UTU: Tapio Pahikkala tapio.pahikkala@utu.fi
- Programme Coordinator: Päivi Rastas, pairit@utu.fi
- Coordinator for Innovation and Entrepreneurship (I&E) minor: Pasi Malinen, pasi.malinen@utu.fi
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 Biosignals | 5 |
Biosignal Analytics | 3 |
Analytics and Programming of Health Wearables | 5 |
Special Course on Health Technology | 2 |
Elective Courses (select at least 9 ECTS): | |
Usability, User Experience and Analytics | 5 |
Mixed Reality and Metaverse | 5 |
Computer Vision and Sensor Fusion | 5 |
Statistical and Probabilistic Programming | 5 |
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 minor | 6 |
Master’s Thesis and Internship | 30 |
Budapest University of Technology and Economics (BME)
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 courses | ECTS |
Artificial intelligence and ethics* | 2 |
Artificial Intelligence and Law | 2 |
Innovation & Entrepreneurship Study | 6 |
Diploma Thesis Design 1 | 10 |
FOURTH SEMESTER (spring, 20 ECTS):
Compulsory courses | |
Diploma Thesis Design 2 | 20 |
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.