Data Science (DSC)

Programme website

Programme Lead: Henrik Boström, bostromh@kth.se

FIRST SEMESTER

SECOND SEMESTER

Programme website

Programme Lead: Marta Patiño

FIRST SEMESTER

SECOND SEMESTER

Programme website

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

FIRST SEMESTER

SECOND SEMESTER

Programme website

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

FIRST SEMESTER

SECOND SEMESTER

I&E MINOR, INNOVATION & ENTREPRENEURSHIP

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.

Programme website

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

FIRST SEMESTER

SECOND SEMESTER

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.

Programme website

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

FIRST SEMESTER

SECOND SEMESTER

Programme website

FIRST & SECOND SEMESTER

Programme website

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

FIRST & SECOND SEMESTER

• 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

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

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.

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

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.

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.

• Frédéric Précioso
• Lionel Fillatre
• Françoise Baude
• Fabien Gandon

Programme website

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

SPECIALISATION: Machine Learning, Big Data Management and Business Analytics

THIRD SEMESTER

FOURTH SEMESTER

Total for the whole year: 60 ECTS

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

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.

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

FOURTH SEMESTER

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.

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

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.

• 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

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):

FOURTH SEMESTER (spring, 20 ECTS):

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.