Data Science (DSC)
Meet our students: Andreas Kaas Johansen
Contact us to find out more!
Why study Data Science?
Data abounds. Social media, manufacturing systems, medical devices, logistic services, and countless others generate petabytes of data on a daily basis. With a wealth of data available, we are at a point in history, where we can conduct analyses to detect, discover, and, ultimately, better understand the world around us.
What are the career opportunities?
Data science is a highly innovative area. The profession was described as "Sexiest Job of the 21st Century," by Harvard Business Review in October 2012. The data scientist simultaneously masters scalable data management, data analysis and domain area expertise to extract key knowledge and solve real-world problems. Data scientists’ skills are highly valued across all fields within the private sector, government and non-profit organisations. This is an opportune time to pursue training in both a challenging and rewarding area of expertise. So join us and embark on a journey of a lifetime!
Why choose Data Science at EIT Digital?
The Data Science Masters offers a unique two-year academic programme, whereby students study data science, innovation and entrepreneurship at two different leading European universities.
Students acquire in-depth technical skills in scalable data collection techniques and data analysis methods. They learn how to use and develop a suite of tools and technologies that address data capture, processing, storage, transfer, analysis, visualisation, and related concepts (e.g., data access, data pricing, and data privacy).
At the same time, they also acquire extensive business skills by learning how to bring an innovation to the market and developing a successful business model. These additional entrepreneurial skills will give students their ticket to a successful career.
How is the programme structured?
All EIT Digital Master School programmes follow the same scheme:
- Students study one year at an ‘entry’ university and one year at an ‘exit’ university in two of EIT Digital’s hot spots around Europe.
- Upon completion, graduates receive degrees from the two universities and a certificate awarded by the European Institute of Innovation and Technology.
- The first year is similar at all entry points with basic courses to lay the foundation for the chosen technical programme focus. Some elective courses may also be chosen. At the same time, students are introduced to business and management. During the second semester, a design project is combined with business development exercises. These teach how to turn technology into business and how to present a convincing business plan.
- In between the first year and the second year, a summer school addresses business opportunities within a socially relevant theme.
- The second year offers a specialisation and a graduation project. The gradation project includes an internship at a company or a research institute and results in a Master thesis with a strong innovation and entrepreneurship dimension.
To learn more about the structure of the programme, please click here.
To learn more about the Innovation & Entrepreneurship minor, please click here.
"Getting value, meaning and answering big questions, are the ultimate goals of Learning Data Science at EIT Digital."
Data Science Master student
Eindhoven University of Technology
"Study Data Science at EIT Digital, where education is provided by renowned universities and where entrepreneurial data scientists of the future are come to being!"
Dr. Renata de Carvalho
Data Science Coordinator
Where can I study Data Science?
Entry - 1st year, common courses
- Eindhoven University of Technology (TU/e), The Netherlands
- Royal Institute of Technology (KTH)
- Universidad Politecnica de Madrid (UPM), Spain
- Universite Nice Sophia Antipolis (UNS), France
- Politecnico di Milano (POLIMI), Italy
Exit - 2nd year, specialisation
- Infrastructures for Large Scale Data Management and Analysis at Universidad Politecnica de Madrid (UMP), Spain
- Multimedia and Web Science for Big Data at Université de Nice Sophia Antipolis (UNS), France
- Business Process Intelligence at Eindhoven University of Technology (TU/e), The Netherlands
- Distributed Systems and Data Mining for Big Data at The Royal Institute of Technology (KTH), Sweden
- Design, Implementation, and Usage of Data Science Instruments at Technische Universität Berlin (TUB), Germany
- Machine Learning, Big Data Management, and Business Analytics at Aalto University, Finland
- Real-time Data Analytics at Eötvös Lorand University (ELTE), Hungary
What can I study at the entry and exit points?
Entry - 1st year, common courses
Technical University Eindhoven (TUE)
Entry Program (each course with 5 ECTS)
Suggested Electives (on Top of Program)
2MMS10 Probability and stochastics 1
2IMV25 Interactive virtual environments
2IMS25 Principles of Data Protection
2MMS30 Probability and stochastics 2
2IMI30 Business process simulation
2IMW30 Foundations of data mining
2IMW10 Data engineering
2IMV15 Simulation in Computer Graphics
2IMW20 Database technology
2DD23 Time-series analysis & forecasting
Royal Institute of Technology (KTH)
- Visualization (DD2257) 7.5 credits
- Machine Learning (DD2421) 7.5 credits
- Programming for data science (ID2XXX) 7.5 credits [no link available yet]
- Data Mining, basic course (ID2XXX) 7.5 credits [no link available yet]
- Entrepreneurship for Engineers (ME2072) 6.0 credits
- Business Development Lab of Entrepreneurship Engineers (ME2073) 9.0 credits
- Summer Course- Entrepreneurship for Engineers (ME2078) 4.0 credits
Conditionally elective courses (one course should be chosen)
- Open and User Innovation (ME1033) 7.5 credits
- Technology-based Entrepreneurship (ME2062) 7.5 credits
- Internet Marketing (ME2094) 7.5 credits
- e-Business Strategies (ME2095) 7.5 credits
- Deep Learning in Data Science (DD2424) 7.5 credits
- Machine Learning, Advanced Course (DD2434) 7.5 credits
- Artificial Neural Networks and Deep Architectures (DD2437) 7.5 credits
- Statistical Methods in Applied Computer Science (DD2447) 6.0 credits
- Search Engines and Information Retrieval Systems (DD2476) 9.0 credits
- Distributed Systems, Advanced Course (ID2203) 7.5 credits
- Distributed Computing, Peer-to-Peer and GRIDS (ID2210) 7.5 credits
Technical University of Madrid (UPM)
First Semester 30 ECTS
- I&E 6 ECTS
- Cognitive systems 4.5 ECTS
- Intelligent Data Analysis 4.5 ECTS
- Cloud Computing and Big Data Ecosystems Design 4.5 ECTS
- Big Data 6 ECTS
- Intelligent Systems 4.5 ECTS
Second Semester 30 ECTS
- I&E 18 ECTS
- Information Retrieval, Extraction and Integration 4.5 ECTS
- Deep learning 3 ECTS
- Data Science Seminars 4.5 ECTS
University of Nice Sophia Antipolis (UNS)
Program Coordinator: Francoise Baude
Technical Courses (34-36 ECTS)
Big data handling (6 ECTS)
- Technologies for big data
- Data mining
Data Transmission (choose from 4 up to 6 ECTS)
- Networking and traffic analysis
- Virtualized cloud computing
- Internet & network programming
- Wireless networking
- Cryptography & security
Data Analysis (choose from 4 up to 6 ECTS)
- Problem solving
- Analysis&indexing of images&videos in big size systems
- Graph & linear programming
- Probability & Statistics
- Web semantic & reasoning
Elective (choose from 0 up to 6)
- Group project in Data Science
- Winter School on Complex networks
Parallelism and Big Data distributed Systems (choose from 2 up to 6)
- Concepts of concurrency
- Concepts of parallelism
- Distributed systems and databases
Intelligent data analysis
- Statistical machine learning
- Data Valorization
Elective (choose from 1 up to 5)
- Web Science seminar
- Machine learning for computer vision
- Advanced programming (C++)
Innovation and Entrepreneurial courses (24-26 ECTS)
- Specific Scientific Writing (2 ECTS)
- French as Foreign Language (2 ECTS)
- Innovation & Entrepreneurship = mini BDL (2 ECTS)
- Basic concepts in I&E (2 ECTS)
- EIT Digital summer school (4 ECTS)
- Shared on-line business courses on Data Science (5 ECTS)
- Business Dev. Lab (7 ECTS)
Politecnico di Milano (POLIMI)
Program Coordinator: Paolo Cremonesi (email@example.com)
|Technical Common Base||Sem||ECTS|
|89183||DATA BASES 2||1||5|
|89184||SOFTWARE ENGINEERING 2||1||5|
|Core Electives (3 out of 6)||Sem||ECTS|
|95943||PRINCIPLES OF PROGRAMMING LANGUAGES||2||5|
|89167||DATA MINING AND TEXT MINING||2||5|
|95946||ADVANCED ALGORITHMS AND PARALLEL PROGRAMMING||2||5|
|Suggested Electives (on Top of Program)||Sem||ECTS|
|88983||FOUNDATIONS OF OPERATIONS RESEARCH||1||5|
|89202||TECHNOLOGIES FOR INFORMATION SYSTEMS||1||5|
|95944||BIOINFORMATICS AND COMPUTATIONAL BIOLOGY||1||5|
|94743||DATA MANAGEMENT FOR THE WEB||1||5|
|88949||ADVANCED COMPUTER ARCHITECTURES||2||5|
|97688||ECONOMICS AND COMPUTATION||2||5|
|Innovation and Entrepreneurship Part I (1 out of 2)||Sem||ECTS|
|96078||ACCOUNTING, FINANCE & CONTROL||1||10|
|96080||STRATEGY & MARKETING||1||10|
|Innovation and Entrepreneurship Part II (1 out of 2)||Sem||ECTS|
|96076||LEADERSHIP & INNOVATION||2||10|
|97406||DESIGNING DIGITAL BUSINESS INNOVATION LAB||2||10|
Exit - 2nd year, specialisation
Business Process Intelligence at TUE
The Business Process Intelligence specialisation focuses on technologies for business analysis and prediction. It considers process mining and its application in the domain of high tech systems, healthcare, visual analytics, spatial data handling, and software analytics (Big Software).
Process mining is a relatively young research discipline that sits between computational intelligence and data mining on the one hand, and process modeling and analysis on the other hand. The idea of process mining is to discover, monitor and improve real processes (as opposed to assumed processes) by extracting knowledge from event logs readily available in today's information systems. Process mining includes automated process discovery, conformance checking, social network and organisational mining, automated construction of simulation models, model extension, model repair, case prediction, and history-based recommendations.
In this specialisation students will acquire breadth and depth in the design, implementation, and use of data science instruments, with the emphasis on business problem solving in the context of business processes.
TU/e has excellent research groups in the data-science area, which have joint forces in the Data Science Center Eindhoven (DSC/e). A central task of the DSC/e is to educate new generations of data scientists. The Netherlands, and specifically the Brainport region, offers a number of leading technology companies. Our graduates have participated in numerous internship opportunities at leading technology companies like ASML, Philips Healthcare, NXP, FEI Company, TomTom, DAF Trucks and also at innovative startups.
The mandatory courses listed below offer students business analytics and predictive modeling techniques. They enable them to detect structures and relationships in large data sets and to build predictive models. Common techniques are discussed to apply these in fields such as applied statistics, data mining and artificial intelligence and multi-objective optimisation of operational processes through nature-inspired meta-heuristics.
This includes evolutionary computation techniques such as genetic/memetic algorithms, particle swarm optimisation, ant-colony optimisation. Process mining techniques will not be limited to control-flow and will also include other perspectives in bottleneck analysis, social network analysis, and decision mining.
The elective courses enable students to acquire greater depth in high tech systems, healthcare applications, spatial data handling, visual analytics, or software evolution.
Besides learning theoretical concepts, students will be exposed to event data from a variety of domains, including hospitals, insurance companies, governments and high-tech systems. Assignments will focus on the analysis of such data sets and on focusing on a particular process mining problem. Application areas include but are not limited to hospital logistics optimisation, software repository mining, predictive maintenance of healthcare equipment, visualisation of genomics data and visual analytics for epidemiologists. Upon completion of this programme, graduates possess a sound foundation to begin a career as a data scientist with a specialisation in process mining.
Programme coordinator: Renata Carvalho
Additional contact: Wil van der Aalst
Further information on EIT Digital Master Programme Data Science at TU/e can be found here.
Mandatory courses (5 ECTS each)
- 2IMI35 Introduction to process mining (if needed)
- 2MMS10 Probability and stochastics 1 (if needed)
- 2IMS25 Principles of Data Protection
- 2IMI20 Advanced process mining
- 2IMI00 Seminar architecture of information systems, OR
- 2IMW00 Seminar web engineering
- 1ZS30 Innovation and entrepreneurship thesis
- 2IMC00 Master project
Electives at TU/e
- 2IMV10 Visual computing project
- 2IMA20 Algorithms for geographic data
- 2DI70 Statistical learning theory
- 2IMV25 Interactive virtual environments
- 2MMS30 Probability and stochastics 2
- 2IMI30 Business process simulation
- 2IMW30 Foundations of data mining
- 2IMW10 Data engineering
- 2IMV15 Simulation in Computer Graphics
- 2IMW20 Database technology
- 2DD23 Time-series analysis & forecasting
Dr ir Boudewijn van Dongen is an associate professor at the Architecture of Information Systems (AIS) research group at Eindhoven University of Technology (TU/e). His research focuses on Process Mining and specifically on conformance checking, aiming to develop techniques and tools to analyse databases and logs of large-scale information systems for the purpose of detecting, isolating, diagnosing and predicting misconformance in the business processes supported by these systems. The notion of alignments plays a seminal role in conformance checking and the AIS group is world-leading in the definition of alignments for various types of observed behavior and for various modelling languages. Besides, the AIS group investigates methods, techniques and tools for the design and analysis of Process-Aware Information Systems (PAIS), i.e., systems that support business processes (workflows) inside and between organizations. The AIS group is generally seen as one of the strongest Business Process Management (BPM) groups in the world. This is also reflected by the widespread use of its open sourced tools like ProM, CPN Tools and YAWL.
Design, Implementation, and Usage of Data Science Instruments at TUB
The Design, Implementation, and Usage of Data Science Instruments specialisation offers students training in three key areas, namely:
- Scalable data management
- Data analysis and machine learning
Students are provided solid data science instruments knowledge (i.e., involving methods, technologies, and systems) to empower them to tackle data science problems in science or business. The specialisation focuses on important application domains such as Industrie 4.0 (ICT based manufacturing), healthcare, energy, smart cities, smart spaces and logistics. For example, in the context of information marketplaces, students learn how to contribute to information economies by provisioning, transforming, analyzing, augmenting and reselling data along data value chains. This can refer to text, speech or video data analytics in the media sector or sensor data for Industry 4.0 and other data-driven business intelligence use-cases. These instruments include Hadoop, Flink, Spark, and GraphLab.
Students learn how to use and enhance these open-source technologies, in addition to working with various closed source technologies, in varying settings, such as for graph mining or text mining. The curriculum is technology-focused, but also addresses other data science dimensions, such as business models, legal issues, and societal aspects.
Nowadays, graduates with data science skills are in great demand. We are routinely asked to recommend our graduates for immediate employment. Many of them have conducted internships at leading technology companies, such as Google, IBM, Oracle, SAP, and Twitter. Our strong ties to industry offer students numerous opportunities to pursue rewarding internships, where they acquire hands-on experience and put their knowledge and skills into practice. These opportunities include DFKI (the German Research Center for Artificial Intelligence), Deutsche Telekom, SAP, Siemens, Trumpf, and innovative start-ups, such as Blue Yonder, Data Artisans, DataMarket, Internet Memory Research, Parstream, and Vico Research.
The mandatory courses listed below offer students the opportunity to obtain training in data management systems and big data (analytics) technologies. Furthermore, elective courses enable them to acquire greater depth in machine intelligence, database technology, speech processing, or cloud operations and attend seminars in machine learning, parallel data processing, signal processing, or big data analytics. Upon completion of this programme, graduates will possess a sound foundation to begin a career as data scientists.
Programme coordinator: Volker Markl
For questions related to coordination of the programme at TUB, please contact Ralf-Detlef Kutsche.
Further information on EIT Digital Master Programme Data Science at TU Berlin can be found here.
- Scalable Data Science: Systems and Methods, AIM-3 SDSSM (6 ECTS - previously called: Large Scale Data Processing and Analytics)
- BDAPRO - Big Data Analytics Project (9 ECTS)
- I&E Study (6 ECTS)
Elective (9 ECTS)
- Cloud Computing - CC (6 ETCS)
- Database Technology - DBT (6 ETCS)
- Database Technology Lab: Implementation of a Database Engine - IDB-PRA (6 ETCS)
- Heterogeneous and Distributed Information Systems - AIM-1 HDIS (6 ECTS)
- Machine Learning I & Classical Topics in Machine Learning - ML I (9 ECTS)
- Management of Data Streams - AIM-2 MDS (6 ECTS)
- Speech Signal Processing and Speech Technology - SSPSC (6 ECTS)
- Big Data Analytics Seminar - BDASEM (3 ECTS)
Details about the classes and all rules and regulations you can find here.
Volker Markl is a Full Professor and Chair of the Database Systems and Information Management (DIMA) Group at TUB, as well as an adjunct status-only professor at the University of Toronto. His research interests include IaaS, new hardware architectures for information management, information integration, big data analytics, query processing, query optimization, data warehousing, electronic commerce, and pervasive computing. He has presented over 100 invited talks worldwide, authored over 50 research papers, has seven patent awards, & has transferred technology into several commercial products, and advises several companies and startups. He is the Speaker and Principal Investigator of the Stratosphere Research Project that resulted in the Apache Flink Big Data Analytics System. He is also Speaker of the Berlin Big Data Center (BBDC), one of the first competence centers in Europe researching innovative technologies and applications around big data, with strong ties to industry and startups. Additionally, he currently serves as the Secretary of the VLDB Endowment and was recently elected as one of Germany's leading “Digital Minds” (Digitale Köpfe) by the German Informatics Society (GI).
Distributed Systems and Data Mining for Big Data at KTH
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 manipulate and process big data. After completing the courses at KTH, graduates are able to effectively design and implement systems to parse data at any stage in the pipeline, from batch-oriented to real-time stream processing. Students are able to write efficient programmes that extract useful information from big data. They acquire deep skills in subfields of data-mining such as mining graphs, text and streaming data as well as scalable learning algorithms.
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 (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, 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 future cooperation is envisaged in the context of this master's programme.
Programme coordinator: Henrik Boström
- Data-Intensive Computing (ID2221) 7.5 credits
- Data Mining (ID2222) 7.5 credits
- ICT Innovation Study Project (ME2096) 6 credits (no link available yet)
Conditionally elective courses (one course should be chosen)
- Theory and Methodology of Science with Applications (Natural and Technological Science) (AK2036) 7.5 credits
- Theory and Methodology of Science in Human-Computer Interaction (DH2610) 7.5 credits
- Research Methodology and Scientific Writing (II2202) 7.5 credits
- Artificial Intelligence (DD2380) 6.0 credits
- Language Engineering (DD2418) 6.0 credits
- Image Analysis and Computer Vision (DD2423) 7.5 credits
- Scalable Machine Learning and Deep Learning (ID2223) 7.5 credits
Henrik Boström is professor of computer science - data science systems at KTH Royal Institute of Technology, since Oct. 1, 2017. Before joining KTH, he was professor of computer and systems sciences at Stockholm University and prior to that professor of computer science with specialisation in information fusion at University of Skövde. He obtained his PhD from Stockholm University in 1993 and became docent (associate professor) in 1999 at Stockholm University. His research focuses on machine learning algorithms and applications, in particular ensemble learning and interpretable models, including decision trees and rules. He is editor of the Machine Learning journal and at the editorial boards of Data Mining and Knowledge Discovery, Journal of Machine Learning Research and Intelligent Data Analysis. He is frequently senior program committee member of some of the most prominent conferences in the area, including SIGKDD, IJCAI, AAAI.
Infrastructures for Large Scale Data Management and Analysis at UPM
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.
Programme coordinator: Marta Patino
Further information on EIT Digital Master Programme in Data Science at UPM can be found here.
- Data Analysis (4.5 ECTS)
- Large Scale Systems Project (3 ECTS)
- Open Linked Big Data (6 ECTS)
- Large Scale Data Management (4.5 ECTS)
- Deep Learning (3 ECTS)
- Massively Parallel Machine Learning (3 ECTS)
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.
Machine Learning, Big Data Management, and Business Analytics at Aalto University
The Aalto specialization aims to provide students a versatile and diverse set of skills for managing very big data, extracting knowledge from data, learning models and making inferences, creating meaningful visualizations to interact with data, and using data-driven methods in business analytics and intelligence, as well as in other applications. These are all necessary skills to becoming a successful data scientist, one of the top professional careers world-wide. An ideal candidate to the Aalto specialization is mathematically inclined, technically proficient, has entrepreneurial spirit, and interest in solving real-life problems.
Teaching Machine Learning and Data Mining in Aalto has long tradition and is supported by several leading research groups in the area. Students get to choose among several courses on Machine Learning, Neural Networks, Bayesian Data Analysis, Data Mining, and more. In the module related to the Management of Big Data the students get to know about advanced Scalable Cloud Computing technologies, programming Parallel Computers, and Modern Database Systems, in all cases with hands-on experience on dealing with new technologies, such as Hadoop, Spark, Storm, GraphLab, as well as computers with multicore CPUs and GPUs. Finally, the students learn about the use of Data Science in business settings, with courses on Business Intelligence, Digital Marketing, and Data Science for Business. In the second semester, the students have the opportunity to work on their MS thesis projects supervised by relevant professors in the School of Science, School of Engineering, or Business School. Additionally, Helsinki has a vibrant entrepreneurship scene (e.g., see Slush conference every November), and there is a plethora of opportunities for MS thesis projects in cooperation with local Data Science companies.
Programme coordinator: Aristides Gionis
Madatory courses (18 ETCS)
Select one course from each block.
- CS-E4600 Introduction to Analytics and Data Science (2 ETCS) autumn
- CS-E3210 Machine Learning: Basic Principles (5 ETCS) autumn
- CS-E4810 Machine Learning and Neural Networks (5 ETCS) autumn
- CS-E5710 Bayesian Data Analysis (5 ETCS) autumn
- CS-E4600 Algorithmic Methods of Data Mining (5 ETCS) autumn
- CS-E4120 Scalable Cloud Computing (5 ETCS) autumn
- CS-E4580 Programming Parallel Computers (5 ETCS) spring
- CS-E4610 Modern Database Systems (5 ETCS) spring
- 57E00500 Business Intelligence (6 ETCS) autumn
- 23E47000 Digital Marketing (6 ETCS) aut/sum
- 30E03000 Data Science for Business (6 ETCS) spring
Elective courses (12 ETCS)
Select from the following list, or the previous mandatory courses.
- CS-E3190 Principles of Algorithmic Techniques (5 ETCS) autumn
- CS-E5740 Complex Networks (5 ETCS) autumn
- ELEC-E5510 Speech Recognition (5 ETCS) autumn
- CS-E4830 Kernel Methods in Machine Learning (5 ETCS) autumn
- CS-C3170 Web Software Development (6 ETCS) aut/spr
- CS-E4870 Research Project in Machine Learning and Data Science (5-10 ETCS) aut/spr
- CS-E4800 Artificial Intelligence (5 ETCS) spring
- CS-E4840 Information Visualization (5 ETCS) spring
- 37E01600 Data Resources Management (6 ETCS) spring
- 31E00920 Applied Microeconomics II (6 ETCS) spring
I&E Minor thesis
- CS-E5420 ICT Innovation I&E Minor Thesis (6 ETCS) autumn
- CS-E5420 MS thesis (30 ETCS) spring
Aristides Gionis is an associate professor in the department of Computer Science in Aalto University. His previous appointment was in Yahoo! Research, where he was leading the Web Mining group. His research interests include several areas of data science, such as graph mining, social-media analysis, analysis of temporal networks, team coordination and opinion formation in social networks, and more. He has published over 100 research papers and made several contributions in data mining, databases, and algorithms research. His research has been funded by FP7, H2020, and Academy of Finland.
Multimedia and Web Science for Big Data at UNS
The Multimedia and Web Science for Big Data specialisation targets data analysts in multimedia. The spelisation focuses on the huge amount of multimedia data available on the web, and in particular on social media.
The explosion of multimedia data (image, video, 3D) from mobile image captures, social sharing, the web, TV shows and movies, as well as the availability of large amount of metadata have created unprecedented opportunities and fundamental challenges to multimedia analytics. They are not just big in volume, but also unstructured and multi-modal.
Students will acquire high technical skills to design and implement new approaches and algorithms to process and make sense of the volumes of information that people and organisations need to deal with. A particular focus will be given to multimedia content available on the web, making the web platform the place where to mine for relevant information. Consequently, the specialisation courses also pertain to advanced web technologies allowing the future data scientist to access, understand, categorize and reason upon the web of data, including non-multimedia ones whenever relevant.
Several international clusters based in the neighborhood of UNS such as Secured Communicating Solutions (SCS) Cluster bring together players in the field of microelectronics, software, telecommunications, services and uses of Information and Communications Technologies. A new workgroup including some international companies like HP and SAP has been created within the SCS Cluster in 2013 to deal with big data including data science opportunities, in which UNS is contributing. Students benefit from UNS relevant industrial collaborators, either located in the Europe’s leading Sophia-Antipolis technology park or even abroad. This includes corporations very active in web and multimedia-content delivery and applications such as Orange and its research branch Orange Labs, Amadeus, Akamai and Thales. It also counts SMEs proposing data analytics supporting technologies such as ActiveEon, Alcméon, Mnemotix.
Programme coordinator: Françoise Baude
Innovation & Entrepreneurship local coordinator : Cedric Ulmer
Further information on EIT Digital Master Programme in Data Science at UNS can be found here.
- Web of Data & Semantic Web (4 ECTS)
- Multimedia Data Processing (4 ECTS)
- Analysis, Mining and Indexing of Structured or Semi-structured Web Data, Images, and Videos in Big Multimedia Systems (2 ECTS)
- Programmable Web: Client and Server Side (4 ECTS)
Elective Courses (10 ECTS in total)
- Knowledge Engineering (2 ECTS)
- Security of Web Applications (2 ECTS)
- Security and Privacy 3.0 (2 ECTS)
- An algorithmic approach to truly distributed systems (2 ECTS)
- Middleware of internet of things (2 ECTS)
- Large scale distributed systems (2 ECTS)
- Software architecture for the cloud (2 ECTS)
- Virtualized infrastructures in cloud computing (2 ECTS)
- Group project in multimedia data management (6 ECTS)
Françoise Baude is a Full Professor (since 2010) at the Engineering school Polytech’Nice-Sophia, part of University of Nice-Sophia Antipolis, and belongs to joint research group with Inria, CNRS I3S since 1995. She is the official contact person for UNS in EIT Digital since 2010. She has been involved in numerous research activities encompassing large-scale distributed systems and middleware, focusing recently on big data analytics (recently EU funded PLAY play-project.eu FP7 project for instance). She has published more than 70 peer-reviewed contributions (in international journals and conferences).
Real-time Data Analytics at Eötvös Lorand University (ELTE)
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 cooperations.
Courses (25 ECTS)
Stream Mining (5 ECTS)
The course is devoted to processing and mining data streams in which data, arriving at high speed, are processed under various space and time constraints. Typically, data are processed with one pass by the algorithm taking into account that data may evolve over time. The course will cover topics of data stream clustering and classification, frequent pattern mining from data streams, change detection and forecasting in data streams, and, indexing and distributed mining of data streams.
Sensor Data Analytics (5 ECTS)
The course is concerned with analysis of data originating of sensors of various types under the presence of uncertainty due to errors and noise in data collection and transmission. Often, in-network processing is required such that the data are processed in the network of sensors itself instead of utilising a centralised solution. Main topics of the course include noise reduction and data cleaning, object detection and recognition, pattern mining, prediction and forecasting, and, in-network computing while considering various types of data such as time series, audio and video.
Network Science (5 ECTS)
The course is devoted to investigation of complex structures such as, among others, computing, communication, transportation, social, biological or spatial networks, with particular interest in analysis, mining and visualization of networks.
Advanced Machine Learning (6 ECTS)
The course focuses on advanced machine learning approaches and their application in various areas with particular interest in deep learning architectures, kernel methods, graphical models, bayesian techniques, reinforcement learning, scalable latent models, semi-supervised learning, ensemble techniques, transfer learning and other state-of-the-art approaches.
Open-source Technologies for Real-time Data Analytics (4 ECTS)
During the course, students will be familiarised with state-of-the-art open-source technologies suitable for real-time data analytics with particular interests in thorough analysis of their advantages and disadvantages with relation to various use-case domains and applications. After completing the course students will be able to assemble complex workflows covering tasks from large-scale data collection and storage to big data analytics.
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.
Any questions about the technical content of the programme? Contact the programme lead: Prof Renata de Carvalho, firstname.lastname@example.org
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Are you interested to apply? Check out or application guidelines. Deadlines for a start in September 2018 are February 1 for non-EU citizens and April 15 EU/EEA/CH citizens.
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