Data Science (DSC)

Data Science (DSC)

120 ECTS

Field of Study:
Computer Science and Information Technology

2 years, full-time

Hold a Bachelor of Science or be in the final year of studies of... (read more).

Tuition fees & scholarships:
For EU and non-EU citizens.
More information.

Language of Instruction:
More information.

Data Science (DSC)

Why study Data Science at EIT Digital?

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.

The Data Science Master's 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.

Who can apply?

If you wish to apply to this programme you must have a Bachelor of Science in, or be in your final year of studies of:

  • Computer Science
  • Information Systems
  • Mathematics
  • Statistics
  • Electrical Engineering/Electronics

The studies should include at least 60 ECTS courses in computer science, computer architecture, or programming, and mathematics including calculus, algebra and mathematical statistics.

Kindly note that relevant work experience can compensate a non-strictly matching bachelor degree. Please justify your work experience in your motivation letter or resume. Once your papers are received, the selection committee will make the final decision on whether your bachelor's and work experience are sufficient as prerequisites for the track you have applied for.

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 graduation 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 I&E minor please click here.

Where can I study Data Science?

What can I study at the entry and exit points?

Entry - 1st year, common courses

Aalto University (Aalto), Finland

Link to the university:


Contact: Wilhelmiina Hämäläinen; 


Compulsory courses (19 ECTS)

CS-E3210 - Machine Learning: Basic Principles - 5 ECTS
CS-E3190 - Principles of Algorithmic Techniques - 5 ECTS
CS-E4640 - Big Data Platform - 5 ECTS
LC-xxxx - Language course: Compulsory degree requirement, both oral and written requirement - 3 ECTS
SCI-E1010 - Introduction course for Master's students: Academic Skills - 1 ECTS

Compulsory I&E Courses (7 ECTS)

CS-E5120 - Introduction to Digital Business and Venturing - 3 ECTS
CS-E5130 - Digital Business Management - 4 ECTS

Elective courses (Select at least 4 ECTS)

CS-E5710 - Bayesian Data Analysis - 5 ECTS
CS-E4600 - Algorithmic Methods of Data Mining - 5 ECTS
CS-E4850 - Computer Vision - 5 ECTS
CS-E4100 - Mobile Cloud Computing - 5 ECTS
CS-E5740 - Complex Networks - 5 ECTS
CS-E4002 - Special Course in Computer Science - 1-10 ECTS
CS-E4003 - Special Assignment in Computer Science - 1-10 ECTS
ELEC-E5422 - Convex Optimization I - 5 ECTS
ELEC-E5500 - Speech Processing - 5 ECTS
ELEC-E5510 - Speech recognition - 5 ECTS
31E00910 - Applied Microeconometrics I - 6 ECTS
23E47000 - Digital Marketing - 6 ECTS


Compulsory courses (10 ECTS)

CS-E4800 - Artificial Intelligence - 5 ECTS
CS-E4890 - Deep Learning - 5 ECTS

Compulsory I&E Courses (17 ECTS)

TU-E4100 - Startup Experience - 9 ECTS
CS-E5140 - Global Business in the Digital Age - 4 ECTS
CS-E5430 - ICT Innovation Summer School - 4 ECTS

Elective courses (Select at least 3 ECTS)

CS-E4820 - Machine Learning: Advanced Probabilistic Methods - 5 ECTS
CS-E4830 - Kernel Methods in Machine Learning - 5 ECTS
CS-E4840 - Information Visualization - 5 ECTS
CS-E4580 - Programming Parallel Computers - 5 ECTS
CS-E4002 - Special Course in Computer Science - 1-10 ECTS
CS-E4003 - Special Assignment in Computer Science - 1-10 ECTS
MS-C1620 - Statistical Inference - 5 ECTS
ELEC-E5550 - Statistical Natural Language Processing - 5 ECTS
30E03000 - Data Science for Business - 6 ECTS
31C01000 - Economics of Strategy for Online and Digital Markets - 6 ECTS

Technical University Eindhoven (TUE), The Netherlands

Link to the university:
Contact: Dr. Renata de Carvalho;

(Each course with 5 ECTS)

Technical Common Base

Core Electives (2 out of 5)

  • 2IMA15 Geometric algorithms
  • 2IMV10 Visual computing project
  • 2IMG15 Algorithms for geographic data
  • 2DI70 Statistical learning theory
  • 2IMD15 Data Engineering

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
  • 2IMM15 Web information retrieval and data mining
  • 2IMD10 Database technology
  • 2IMV15 Simulation in computer graphics
  • 2DD23 Time-series analysis & forecasting

Technical University of Madrid (UPM), Spain

Link to the university:
Contact: Marta Patiño;

First Semester (30 ECTS):

  • I&E 6 ECTS
  • Cognitive systems 4.5 ECTS
  • Statistical data analysis 4.5 ECTS
  • Cloud Computing and Big Data Ecosystems Design 4.5 ECTS
  • Big Data 6 ECTS
  • Machine Learning 4.5 ECTS

Second Semester (30 ECTS):

  • I&E 18 ECTS

Electives (12 ECTS)

  • Data Science Seminars 4.5 ECTS
  • Data acquisition 4.5 ECTS
  • Information retrieval, extraction and integration 4.5 ECTS
  • Graph analysis and social networks 3 ECTS
  • Deep learning 3 ECTS

Université Côte d’Azur (UCA), France

Link to the university:
Contact: Francoise Baude;


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.

Data science 1 (6 ECTS, each course inside is accounted for 3 coefficient)

  • Modelisation and optimisation in machine learning
  • Technologies for massive data

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)
  • 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
    • Virtualized infrastructure in Cloud computing
  • Data modeling 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
  • 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


Innovation and Entrepreneurship 1 (9 ECTS, each course inside is accounted for 3 coefficient)

  • Entrepreneurship Introduction
  • Basics in Innovation and Entrepreneurship
  • Business Development Lab Introduction


Compulsory courses

Data science 2 (9 ECTS, each course inside is accounted for 3 coefficient)

  • Temporal series
  • Data valorization
  • Computer vision & machine learning

Electives courses

Elective Data science 2 (6 ECTS, each course inside is accounted for 3 coefficient)

  • Personal project in group or individual (can be continuation of semester 1 project)

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)*
  • Data modeling and analysis:
    • Augmented reality (taught in French)*
    • Optimisation
    • Graphs
  • Application of data science
    • Winter school (on Complex networks)


Innovation and Entrepreneurship 2 (6 ECTS)

  • Digital Business
  • Digital IP & Law

Innovation and Entrepreneurship 3 (9 ECTS)

  • Business Development Lab (coeff 5) and summer school (coeff 4)

Polytechnic University of Milan (POLIMI), Italy

Link to the university:

Contact: Paolo Cremonesi;




  • 089183 DATA BASES 2 - 5 ECTS





  • 052536 SOFT COMPUTING - 5 ECTS




  • 099356 I&E SUMMER SCHOOL - 4 ECTS


  • 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.

Université Paris-Saclay (UPS) - formerly Université Paris Sud, France

Link to the university:


Contact: Alexandre Allauzen;


Technical Mandatory Common Base

  • Data computing (5 ECTS)
  • Machine Learning (5 ECTS)
  • Data Mining (5 ECTS)
  • Big Data Project (4 ECTS)
  • Scientific programming (2,5 ECTS)
  • Interactive Information visualization (2.5 ECTS)

Technical Electives (Choose 4 out of the following list) (2,5 ECTS each):

  • Fairness in DataSciences
  • Deep-learning
  • Probalistic Inference
  • Signal Processing Basics
  • NPL: information extraction
  • Dialog systems
  • Information Retrieval
  • Web data-model,
  • Graph data management

NON Technical Mandatory courses:

  • Innovation & Entrepreneurship Basics ( 6 ECTS)
  • Business Development Lab 1 (3 ECTS)
  • Business Development Lab 2 (6 ECTS)
  • Innovation & Entrepreneurship Advanced (5 ECTS)
  • Summer School (4 ECTS)
  • French Language and Culture (2 ECTS)

Eötvös Loránd University (ELTE), Hungary

Link to the university:


Contact: Tomáš Horváth,


Compulsory courses

Introduction to Data Science (5 ECTS, 1st semester)

The course navigates through the basic concepts and principles behind the main data science models and techniques. Descriptive techniques such as clustering and frequent pattern mining are explained in more details while, in case of predictive techniques, the focus is put mainly on the concepts of a model, its parameters and hyper-parameters as well as the quality and validation of models including overfitting-underfitting and the bias-variance trade-offs. Data quality and pre-processing issues related to various data types and modeling problems are also tackled. Finally, basic recommendation techniques and the CRISP-DM methodology are contained in the course as well.

Foundations of Data Science (4 ECTS, 1st semester)

The course focuses on mathematical foundations of data science including basic univariate and multivariate statistics, basic concepts of probability theory, basic concepts from geometry, basic concepts from linear algebra and basic concepts from information theory. The purpose of the course is that students with different backgrounds in the above mentioned disciplines of mathematics receive a compact knowledge necessary for understanding the basic principles and methods in data science.

Data Models and Databases (4 ECTS, 1st semester)

The course is devoted to main concepts, models and principles behind databases. Besides a good knowledge in machine learning, familiarity with various data models and hands-on skills and experience related to database technologies are inevitable in the toolbox of a data scientist. The main topics discussed within the lecture are non-relational data models and database technologies; operational database management systems such that data lakes, data marts and data warehouses; data analytics and on-line analytical processing tools and techniques. Topics related to the efficiency of various models and technologies w.r.t. use-case applications will be also discussed during the course.

Electives courses:

Software Technology (5 ECTS, 1st semester)

Introduction to security analysis methods, including threat modelling and attack modelling both for cyber attacks, e.g. attacks on software or communication and for physical (or field) attacks, e.g. manipulation of sensor data, GPS signals etc. in autonomous systems. Cryptographic primitives, mathematical background and scope. Use of appropriate cryptographic tools in protocols. Overview of most important protocols used in networking, especially in the automotive industry. Overview of existing standards and recommendations on safety and security of autonomous driving systems.

Design and Analysis of Algorithms (5 ECTS, 1st semester)

Stable matching. Gale-Shapley algorithm. Divide and conquer algorithms. Mergesort. Counting inversions. Closest pair of points. Dynamic programming. Sequence alignment. Knapsack, subset sum and change-making problems. Greedy algorithms. Scheduling problems. Clustering. Approximation algorithms. Load balancing problem. Center selection problem. Randomized algorithms. Quicksort. Quickselect. Karger’s global minimum cut algorithm.

Computer Graphics (5 ECTS, 1st semester)

The basic principles and techniques for computer graphics on modern hardware, with special focus on real time applications.

Image and Signal Processing (5 ECTS, 1st semester)

Signals and systems. Fourie-, Laplace-transform. AD conversion: sampling, quantization. DA conversion, Shannon’s formulae. Windowing. Analog and discrete filters, Signal processing in time domain, in frequency domain. Basic concepts and methods of image processing. Edge detection, segmentation. Image reconstruction. Noise reduction.

Introduction to Vehicles and Sensors (5 ECTS, 1st semester)

Principles of autonomous vehicles, and self-driving cars. Hardware and software architectures. Sensors, interconnect networks, actuators, processing elements. Radars, LIDAR’s, cameras, ultrasonic, GPS, and other sensors. CAN, LIN, MOST, FlexRay vehicle interconnect networks and architectures. Intelligent transportation systems.


  • I&E Basics (Barbara Hegyi) – 5 ECTS
  • Business Development Lab I. (Udo Bub) – 5 ECTS


Compulsory  courses

Machine Learning (5 ECTS, 2nd semester)

The course is concerned with deeper explanation of machine learning models and algorithms. Particular interest, besides the main principles of these algorithms and their theoretical background, will be devoted to the hyper-parameters of various algorithms such as their meaning and tuning. The pros and cons of these algorithms w.r.t. various application domains and prediction tasks will be discussed, too. Main topics of the course include decision trees, support vector machines and kernel methods, graphical and probabilistic models, neural networks, factorization techniques, semi-supervised learning, ensemble techniques, bagging, boosting, time-series and text mining.

Optimization for Data Science (4 ECTS, 2nd semester)

The course focuses on basic concepts from optimization and graph theory as well as stochastic processes. The concepts discussed in the lecture form the basis for machine learning techniques since tuning the parameters of a machine learning model is an optimization task. The purpose of the course is that students with different backgrounds in the above mentioned disciplines of mathematics receive a compact knowledge necessary for understanding the basic principles behind various machine learning techniques and algorithms.

Electives courses

Embedded and Real-Time Systems (5 ECTS, 2nd semester)

Nowadays the usage of a real-time system more and more frequently is needed. Today all of the modern operating systems contain real-time kernel. We shall overview the features of real-time systems, the scheduling types, RT signals and timers. We shall examine its modern appearance in an operating and in an embedded system.

Data Mining in Smart Systems (5 ECTS, 2nd semester)

Data pre-processing, preparation (missing value imputation, noise handling and outlier detection, data transformation); clustering techniques (k-means, hierarchical, density-based); frequent pattern and association rule mining (Apriori, Eclat, FP-Growth); prediction models (linear and logistic regression, decision trees, SVM, Bayes models, kernels, matrix factorization); building model ensembles (ensembles, bagging, boosting); model evaluation (overfitting, bias-variance, cross-validation).

3D Computer Vision (5 ECTS, 2nd semester)

Camera models. Feature detection in images. Pattern matching. Camera calibration. Stereo vision. Monocular vision. Simultaneous localization and mapping. Case studies in several application fields of computer vision.

Artificial Intelligence in Processes and Automation (5 ECTS, 2nd semester)

After this course, the student will (i) understand the connection between low level, i.e., network based AI and high level, i.e., rule based AI (ii) be able to develop stochastic and deterministic models from data (iii) understand control principles and (iv) understand the principles of designing optimal autonomous systems that can learn with state-of-the-art learning algorithms matching or overcoming human performance in industrial environments. Novel AI related software libraries will be introduced.


  • I&E Specialisation (Barbara Hegyi) – 5 ECTS
  • Business Development Lab II. (Udo Bub) – 5 ECTS

University of Twente (UT)

Link to the university:

Contact: Dr. Maurice van Keulen:

As entry year, the following courses are offered (5 EC each):


201200044 - Managing Big Data

201400174 - Data Science (could also be 10 EC, with additional topics and project 201500363 )

201600070 - Basic Machine Learning

201700080 - Information Theory and Statistics

191612680 - Computer Ethics

Core I&E courses (shared with other EIT masters):

201700180 - Innovation and Entrepreneurial Finance for EIT students

201700119 - Business Development Lab I

201700120 - Business Development Lab II

Advanced (at least 4):

201600071 - Advanced Machine Learning

201600076 - Foundations of Information Retrieval

192652150 - Service-oriented Architecture with Web services

192320111 - Architectures of Information Systems

201300074 - Research Experiments in Databases and Information Retrieval

201700081 - Probabilistic programming

University of Rennes 1 (UR1), France

Entry Year

Mandatory and elective courses

  • Innovation and Entrepreneurship Basics (5 ECTS)                            
  • Business Development Laboratory 1 (5 ECTS)                    
  • Data handling and Analysis (6 ECTS)
  • Advanced Databases (4 ECTS)   
  • Operational Research (5 ECTS)
  • Object Oriented Analysis and Design (5 ECTS)
  • Intangible assets management (5 ECTS)               
  • Business Development Laboratory 2      (5 ECTS)                              
  • Summer School (4 ECTS)                             
  • Machine Learning I (5 ECTS)      
  • Semantic Web Technologies (5 ECTS)    
  • Database Security (5 ECTS)
  • Technological Watch (5 ECTS)

Exit - 2nd year, specialisation

Technical University Eindhoven (TUE), The Netherlands

Link to the university:
Contact: Dr. Renata de Carvalho;

Specialisation: Business Process Intelligence

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 start-ups.

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.


  • 2IMI35 Introduction to process mining (5 ECTS)
  • 2IMS25 Principles of Data Protection (5 ECTS)
  • 2IMV20 Visualization (5 ECTS)
  • 2IMI20 Advanced process mining (5 ECTS)
  • 2IMI00 Seminar analytics for information systems (5 ECTS) 1
  • 2IMD00 Seminar databases (5 ECTS) 1
  • 2IMM00 Seminar data mining (5 ECTS) 1
  • 1ZS30 Innovation and entrepreneurship study (6 ECTS)
  • 2IMC00 Master project (30 ECTS)

1 One of these three seminars has to be chosen

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.

Technical University of Berlin (TUB), Germany

Link to the university:

Contact: Prof. Volker Markl; Dr. Ralf D. Kutsche

Specialisation: Design, Implementation, and Usage of Data Science Instruments
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
  • Applications

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.

Compulsary courses

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 ECTS)
Database Technology - DBT(6 ECTS)
Database Technology Lab: Implementation of a Database Engine - IDB-PRA(6 ECTS)
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)

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).

KTH Royal Institute of Technology (KTH), Sweden

Link to the university:

Contact: Henrik Boström; or

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 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, 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.


Compulsory courses:

ID2221 Data-Intensive Computing7.5 ECTS
ID2222 Data Mining7.5 ECTS
ME2096 ICT Innovation Study Project 6.0 ETCS
AK2036 Theory and Methodology of Science with Applications (Natural and Technological Science)
One of II2202, AK2036 shall be chosen
7.5 ECTS
II2202 Research Methodology and Scientific Writing
One of II2202, AK2036 shall be chosen
7.5 ECTS

Elective courses:

DD2257 Visualization

7.5 ECTS

DD2380 Artificial Intelligence

6.0 ECTS

DD2418 Language Engineering

6.0 ECTS

DD2423 Image Analysis and Computer Vision

7.5 ECTS

DD2424 Deep Learning in Data Science

7.5 ECTS

DD2434 Machine Learning, Advanced Course

7.5 ECTS

DD2437 Artificial Neural Networks and Deep Architectures

7.5 ECTS

DD2447 Statistical Methods in Applied Computer Science

6.0 ECTS

DD2476 Search Engines and Information Retrieval Systems

9.0 ECTS

ID2203 Distributed Systems, Advanced Course

7.5 ECTS

ID2210 Distributed Computing, Peer-to-Peer and GRIDS

7.5 ECTS

ID2225 Learning Machines

7.5 ECTS

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. 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 machine learning, 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 has been a frequent senior programme committee member of some of the most prominent conferences in the area, including SIGKDD, IJCAI, AAAI.

Technical University of Madrid (UPM), Spain

Link to the university:
Contact: 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.

(24 ECTS)

  • Data Analysis (4.5 ECTS)
  • Large Scale Systems Project (3 ECTS)
  • Open Data and Knowledge Graphs (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.

Aalto University (Aalto), Finland

Link to the university:


Contact: Wilhelmiina Hämäläinen;

Specialization: Machine Learning, Big Data Management, and Business Analytics

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.


Compulsory major courses (14 ECTS)

  • CI-E1010 Introduction course for Master's students: Career and working life
    skills - 1 ECTS
  • LC-xxxx Language course: Compulsory degree requirement, both oral and written
    requirements - 3 ECTS

Select at least two courses of the following:

  • CS-E3210 Machine Learning Basic Principles - 5 ECTS
  • CS-E5710 Bayesian Data Analysis - 5 ECTS
  • CS-E4600 Algorithmic Methods of Data Mining - 5 ECTS
  • CS-E4640 Big Data Platforms - 5 ECTS

Compulsory I&E courses (6 ECTS)

  • CS-E5425 I&E Study Project - 6 ECTS

Optional major courses (10 ECTS)

  • CS-E3190 Principles of Algorithmic Techniques - 5 ECTS
  • CS-E5740 Complex Networks - 5 ECTS
  • ELEC-E5510 Speech Recognition - 5 ECTS
  • CS-E4830 Kernel Methods in Machine Learning - 5 ECTS
  • CS-E4002 Special Course in Computer Science - 1-10 ECTS
  • CS-C3170 Web Software Development - 6 ECTS
  • CS-E4870 Research Project in Machine Learning and Data Science - 5-10 ECTS
  • CS-E4003 Special Assignment in Computer Science - 1-10 ECTS
  • CS-E4004 Individual Studies in Computer Science - 1-10 ECTS
  • CS-E4000 Seminar in Computer Science - 5 ECTS
  • CS-E4800 Artificial Intelligence - 5 ECTS
  • CS-E4840 Information Visualization - 5 ECTS
  • 37E01600 Data Resources Management - 6 ECTS
  • CS-E4640 Big Data Platforms - 5 ECTS
  • CS-E4580 Programming Parallel Computers - 5 ECTS
  • 23E47000 Digital Marketing - 6 ECTS
  • 30E03000 Data Science for Business - 6 ECTS
  • CS-E4890 Deep Learning - 5 ECTS
  • 57E00700 Capstone: DigitalSM Challenge -6 ECTS
  • CS-E4850 Computer Vision -5 ECTS autumn
  • MS-C2128 Prediction and Time Series Analysis -5 ECTS autumn
  • ELEC-E8125 Reinforcement Learning -5 ECTS autumn

Université Côte d’Azur (UCA), France

Link to the university:
Contact: Francoise Baude;

Specialisation: Multimedia and Web Science for Big Data

LIST OF COURSES: (all 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


  • UE 1 “Unité d’enseignement 1” (course block) Total 6 ECTS, each course inside has coefficient 2)
    • Technologies for massive data
    • Compression, analysis and visualization of multimedia content
    • Analysis and indexing of images and videos in big data systems: from shallow to deep learning
  • UE 3 “Unité d’enseignement Project”
    • Personal project in data science (6 ECTS)
  • UE I&E (Total 6 ECTS)
    • I&E study (belongs to the I&E minor) (6 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:

Frédéric Précioso
Full Professor at University Nice-Sophia Antipolis

From September 2011, with University Nice-Sophia Antipolis (UNS) at Ecole Polytech Nice-Sophia, Head of MinD (Mining Data) research group in Machine Learning and Data Mining.
Research Lab: I3S Laboratory, Team SPARKS.

Research interests: kernel-based SVM, Boosting, Ensemble Learning, Random Forest, Artificial Neural Networks, Deep Learning, (Inter-)Active learning, Long-term learning, Large Scale learning, Machine Learning. Hybridization, Pattern Recognition.

Research contexts: Multimedia Indexing/Classification, Content-Based Multimedia Retrieval, Text mining, Applications to Bio-Medical data, Application to environmental data, Application to sensor networks.

From September 2018, scientific programme leader at the the French National Research Agency (ANR), in the digital and mathematics department, responsible for programmes in particular, in AI.

Lionel Fillatre
Full professor at the University Nice Sophia Antipolis in the I3S laboratory ("Laboratoire d'Informatique, Signaux et Systèmes de Sophia Antipolis"), Team SIS (Signal, Images et Systèmes). His current research interests include statistical decision theory, machine learning, signal and image processing, and bio-inspired data processing.

At the Polytech Nice Sophia-Antipolis engineering school, he leads the Data Science option affiliated with both Mathématiques Appliquées et Modélisation and Sciences Informatiques departments.

Françoise Baude
Full professor at the University Nice Sophia Antipolis in the I3S laboratory ("Laboratoire d'Informatique, Signaux et Systèmes de Sophia Antipolis"), Team COMRED, SCALE group. Her research interests include distributed systems in general, parallel and distributed programming and data stream processing systems. Since 2016, she is an elected member of the academic council and research commission of the UNS.
At the Polytech Nice Sophia-Antipolis engineering school, she leads the Data Science Entry and Exit point tracks of the EIT Digital Master School.

Fabien Gandon
Research Leader
Université Côte d’Azur, Inria, CNRS, I3S, France

Senior Researcher and Research Director at Inria
Research topics: Artificial Intelligence, Knowledge Representation, Semantic Web, Linked Data, Ontologies
Leader of the Wimmics research team at Inria in the Research Center of Sophia-Antipolis and I3S CNRS,
University Côte d’Azur, Nice - Sophia Antipolis, France.
Director joint research Laboratory QWANT-Inria
Advisory Committee representative of Inria at the World-Wide Web Consortium (W3C)
Representative of Inria in the Web Science Trust Network
Leader research convention French Ministry of Culture-Inria

  • Data modeling 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)
    • Multimedia content management
    • Images advanced management* (taught in French)

Eötvös Loránd University (ELTE), Hungary

Link to the university:

Contact: Tomáš Horváth;

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 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.

Université Paris-Saclay (UPS) - formerly Université Paris Sud, France

Link to the university:


Contact: Alexandre Allauzen;

Specialisation: Natural Language Processing

Our exit point will provide in-depth theoretical and technical skills in NLP and large-scale machine learning. With specific courses, students will choose to deepen her/his knowledge in big-data topics or in NLP, including for instance : deep learning for NLP, speech recognition for natural interaction, dialogue systems, multilingual models.

With this master's program, the students will acquire on the one hand in-depth theoretical and technical skills in scalable data engineering tools, statistical analysis methods along with machine learning models. On the other hand, with specific courses, students will learn how to exploit spoken and textual data in order to imagine new applications of Natural language processing (NLP) for data mining, innovating services and new user-interactions.

Warm up project (To assure homogeneous skills on basics) (2 ECTS)

Technical Mandatory Common Base

  • Data-warehouse (2,5 ECTS)
  • NPL: information extraction (2,5 ECTS)
  • Deep-learning (2,5 ECTS)
  • Probalistic Inference (2,5 ECTS)
  • Signal Processing Basics (2,5 ECTS)

Technical Electives (Choose 4 out of the following list) (2,5 ECTS each):

  • Fairness in Data Sciences
  • Dialog systems
  • Information Retrieval
  • Web data-model,
  • Graph data management
  • Graphical models
  • Multilingual NLP
  • Deep Learning for structured data
  • Speech recognition and interaction
  • Data-science project (shared with University of Nice)

NON Technical Mandatory courses:

  • Innovation & Entrepreneurship Study (6 ECTS)
  • Career Seminar (5 ECTS)
  • Internship (25 ECTS)

Alexandre Allaunzen is Professor at the Université Paris-Sud and a researcher at LIMSI-CNRS in the Spoken Language Processing group.

His research activities are dedicated to "machine learning for natural language processing", with a strong focus neural network models for machine translation and language modeling. Beyond deep-learning, he has also explored other kind of approaches like for instance Bayesian modeling (parametric and non-parametric) for natural language documentation along with Conditional Random Fields (w/o hidden structures) for machine translation and bilingual alignment.

University of Trento (UNITN), Italy

Link to the university:

Contact: Prof. Yannis Velegrakis;

Specialisation: Big Data Variety and Veracity

Data Science has increasingly become an integrated approach where modern scientists analyze 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.


1st Semester:

I&E Studies


Knowledge and Data Integration


Plus 3 courses from the following list (that have not already taken during the first year of studies):

Web Architectures


Machine Learning


Data Mining


Big Data and Social Applications


Human Computer Interaction


Real Time Operating Systems and Middleware


Introduction to Computer and Network Security


2nd Semester:

Thesis & Internship


Prof. Yannis Velegrakis is a faculty member in the Department of Information Engineering and Computer Science of the University of Trento, director of the Data Management Group, head of the Data and Knowledge Management Research Program, and coordinator of its EIT Digital Master. His research area of expertise includes Big Data Management, Analytics, Data Exploration, Knowledge Discovery, Highly Heterogeneous Information Integration, User-centered Querying, Personalization, Recommendation, Graph Management, and Data Quality. Before joining the University of Trento, he was a researcher at the AT\&T Research Labs (USA). He has spent time as a visitor at the IBM Almaden Research Centre (USA), the IBM Toronto Lab (Canada), the University of California, Santa-Cruz (USA), the University of Paris-Saclay (France), and the Huawei Research Center (Germany). His work has also been recognized through a Marie Curie and a Université Paris-Saclay Jean D’Alembert fellowship. He has been an active member of the database community. He has served as the General Chair for VLDB'13, PC Area chair for ICDE'18, and PC chair in a number of other conferences/workshops.

University of Twente (UT)

Link to the university:

Contact: Dr. Maurice van Keulen:

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 analyzing 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 flavors 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 flavor. 

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:

201600028 - Telemedicine and Data Analysis for Monitoring

201500222 - Technology for Health

201500132 - Remote Monitoring and coaching

201400353 - Signals with Information

201500040 - Introduction to Biometrics

191210910 - Image Processing and Computer Vision

201100254 - Adv. Comp. Vision & Pattern Recognition

201700075 - 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 Rennes 1 (UR1), France

Exit Year

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 preprocessing and storing decisional data in dataware-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 analyze 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 analyze 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          

  • I&E study (6ECTS)                          
  • Machine Learning II (5 ECTS)     
  • Data Mining (5 ECTS)    
  • Indexing and Visualization (3 ECTS)        
  • Datawarehouses (3 ECTS)           
  • Cloud and Big Data Management (3 ECTS)          
  • Case Study in Data Science (5 ECTS)       
  • Master Thesis (30 ECTS)              

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!

Application period for 2020 has ended.

Have questions? We are here to help!

Data Science (DSC)
120 ECTS
Field of Study:
Computer Science and Information Technology
2 years, full-time
Hold a Backelor of Science or be in the final year of studies of... (read more).
Tuition fees & scholarships:
For EU and non-EU citizens.
More information.
Language of Instruction:
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