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Student Guide
Joint International Master's Programme in Communications and Data Science
Programme main page
Curriculum 2024–2026
About the programme
Programme covers a range of timely, industry topics relevant to modern fields of engineering, comprising competences from electrical engineering, automation, programming, communications, data science, artificial intelligence and machine learning, network security and Internet of Things.
Students will acquire competences in a range of techniques covering the broad areas of mathematics, modelling and analysis of signals and systems, electronics, data science, artificial intelligence, security, networks and distributed systems.
During their studies, students will further acquire expertise in other areas, such as project planning and management, teamwork and coordination, entrepreneurship, critical thinking and sustainability. The Programme will empower students to solve industry-relevant problems employing cutting-edge tools of artificial intelligence, automation and control theory, data analytics, network security, wireless systems, distributed systems and signal processing. Students will be trained in the design and analysis of machine learning models and communication systems, advance their knowledge in the broad field of computer networks and cybersecurity, and gain professional experience through tailored industry-relevant projects and entrepreneurship trainings.
The expertise gained through the Programme features diverse programming skills, good mathematical background, goal-oriented problem solving, as well as efficient project planning and management.
In particular, students will boast competences in:
- emerging communication technologies: students will learn from experts in the domain of communication and networking;
- cutting-edge automation competences: the Programme boasts theoretical skills and practical expertise at the interconnection between communications and data science;
- emerging information technologies: students will learn from experts in evolving information technologies that define the future in industry and society;
- network security: security is the weak spot in many contemporary technologies, so students will learn to lead the transition of the industry towards security and privacy by design and by default in all types of networks, and more specifically in IoT;
- distributed computer networks: distributed systems have become the norm and are ubiquitously deployed through IoT, so students will learn to command the tools to manage future distributed networks of an unprecedented scale;
- artificial intelligence in networking: data science has found its place in many areas of engineering in recent years and is increasingly dominating also networking domains, students being trained to possess a broad range of tools to structure and analyse huge data sets and to extract meaning from patterns, as well as to appreciate the value created by collecting, communicating, coordinating and leveraging the data from connected devices;
- programming skills: students will gather practical programming expertise in a range of industry-relevant languages;
- project and team working: students will collect practical experience through projects in relation with industry.
The Programme will be structured into 2 years:
- Year 1: basic studies common in student’s home university:
- General studies (languages, soft skills, …)
- Mathematics/Programming/Security
- Communications
- Data Science
- Year 2: specialization studies, specific to all Partners, Project and Thesis
- Communications, Data Science, and Security (Grenoble INP)
- Communications and Data Science (Técnico Lisboa)
- Communications, Automation and Machine Learning (Aalto University)
- Communications, Machine Learning, Medical Informatics & Biomedicine (TU Braunschweig)
- Communications, 5G/6G and Internet of Things, and Data Science (UPC)
All Consortium Universities can be either Home or Host University. In Year 1, students complete courses with 60 ECTS at Home University. In Year 2, students either:
- Option A: Student completes courses with 30 ECTS and the thesis with 30 ECTS in one Host University
- Option B: Student completes courses with 30 ECTS in Host University 1 in the autumn semester and the thesis with 30 ECTS in Host University 2 in the spring semester
Option B is targeted only for the students who are residents in the Home or Host University country at the enrolment to the programme (Erasmus Mundus regulation).
Total
120
ECTS
Major *
90
ECTS
Thesis
30
ECTS
* Including
- Studies in Aalto University 55 ECTS (entry year in Aalto) / 35 ECTS (exit year in Aalto)
- Studies in partner university 35 ECTS (exit in partner university) / 55 ECTS (entry in partner university)
Major: Communications Engineering and Data Science
Code: ELEC3067
Credits: 90 ECTS
Responsible professor: Stephan Sigg
Other professors involved: Stephan Sigg, Ville Kyrki, Alexander Jung
Entry year in Aalto University
| Code | Course name | ECTS | Period / Year |
|---|---|---|---|
| General studies 6 ECTS | |||
| ELEC-E0110 | Academic skills in MSc studies | 3 | I-III / 1 |
| Compulsory Language course | 3 | ||
| Communications 10 ECTS | |||
| ELEC-E7120 | Wireless Systems | 5 | I / 1 |
| ELEC-E7230 | Mobile communication Systems | 5 | II / 1 |
| Data Science 10 ECTS | |||
| CS-C3240 | Machine learning | 5 | I / 1 |
| CS-E4800 | Artificial Intelligence D | 5 | III-IV / 1 |
| Mathematics and Programming 5 ECTS | |||
| MS-C2111 | Stochastic processes | 5 | II / 1 |
|
Specialization 5-6 ECTS Track specific specialization course from exit year university |
|||
| Project 6 ECTS | |||
| ELEC-E7633 | Project Course | 6 | III-V / 1 |
| Electives – fulfill 60 credits | |||
| Student chooses from the list below: Elective studies | I-V / 1 | ||
Exit year in Aalto University
Track specific specialization course to complete online during the first year of studiesThe track specific specialization course is completed online during the first year of studies. It cannot be counted into the 60 ECTS completed during the second year of studies. |
|||
| ELEC-C8201 | Control and Automation | 5 | III-IV / 1 |
| Code | Course name | ECTS | Period / Year |
|---|---|---|---|
| General studies 3 ECTS | |||
| Compulsory Language course (if not completed in Entry University) | 3 | ||
| Communications 5 ECTS | |||
| ELEC-E7140 | Networked Systems | 5 | I / 2 |
| Data Science 5 ECTS | |||
| ELEC-E7261 | Ambient Intelligence D | 1-8 | 2024-2025: III-IV / 2 2025-2026: I-II / 2 |
| Automation 5 ECTS | |||
| ELEC-E8101 | Digital and optimal control | 5 | I-II / 2 |
| MSc thesis 30 ECTS | |||
| ELEC.thes | M.Sc. Thesis | 30 | III-V / 2 |
| Electives – fulfill 60 credits | |||
| Student chooses from the list below: Elective studies | I-V / 2 | ||
Elective studies
| Code | Course name | ECTS | Period |
|---|---|---|---|
| CS-C3130 | Information Security | 5 | I |
| CS-E4190 | Cloud Software and Systems | 5 | I-II |
| CS-E4300 | Network Security | 5 | II |
| CS-E4340 | Cryptography | 5 | I-II |
| CS-E4370 | Applied Cryptography | 5 | III-IV |
| CS-E4380 / MS-E1687 | Special course: Advanced Cryptography | 5 | I-II |
| CS-E4650 | Methods of Data Mining | 5 | I-II |
| CS-E4715 | Supervised Machine Learning D | 5 | I-II |
| CS-E4825 | Probabilistic Machine Learning D | 5 | III-IV |
| CS-E4890 | Deep Learning | 5 | III-IV |
| CS-E5480 | Digital Ethics | 3-5 | V |
| CS-E5710 | Bayesian Data Analysis | 5 | I-II |
| ELEC-E4420 | Microwave Engineering | 5 | III-IV |
| ELEC-E5410 | Signal Processing for Communications | 5 | I - II |
| ELEC-E5424 | Convex Optimization D | 5 | I-II |
| ELEC-E5431 | Large Scale Data Analysis | 5 | III-IV |
| ELEC-E5440 | Statistical Signal Processing | 5 | I-II |
| ELEC-E7120 | Wireless Systems | 5 | I |
| ELEC-E7131 | Internet Traffic Measurements and Analysis | 5-10 | III - IV |
| ELEC-E7230 | Mobile Communication Systems | 5 | I |
| ELEC-E7240 | Coding Methods | 5 | III |
| ELEC-E7311 | SDN Fundamentals & Techniques | 5 | III - IV |
| ELEC-E7470 | Cybersecurity | 5 | V |
| ELEC-E8001 | Embedded Real-Time Systems | 5 | I-II |
| ELEC-E8101 | Digital and Optimal Control | 5 | I-II |
| ELEC-E8102 | Distributed and Intelligent Automation Systems | 5 | I-II |
| ELEC-E8103 | Modelling, Estimation and Dynamic Systems | 5 | I-II |
| ELEC-E8740 | Basics of Sensor Fusion | 5 | I-II |
| ELEC-E5810 | Biosignal processing | 5 | I |
| ELEC-E7340 | Machine learning for Wireless Communications D | 5 | III-IV |
| MS-C1620 | Statistical inference | 5 | III - IV |
Entry year in Grenoble INP
Please check the up to date curriculums from partner University's own website.
| Code | Course name | ECTS | Year |
|---|---|---|---|
| General studies 11 ECTS | |||
| Research Methodology (elective) | 3 | 1st | |
| Technical Writing and Speaking in English | 3 | 1st | |
| French as a Foreign Language (elective) | 0 | 1st | |
| Python (elective) | 0 | 1st | |
| Other from other Universities (elective) | 4 | 1st | |
| Communications 12 ECTS | |||
| Principles of Internet | 6 | 1st | |
| Digital Transmission from Técnico Lisboa | 6 | 1st | |
| Programming 10 ECTS | |||
| Data Base Foundations | 5 | 1st | |
| Algorithmic Problem Solving | 5 | 1st | |
| Security 6 ECTS | |||
| Introduction to Cybersecurity | 6 | 1st | |
|
Specialization 5-6 ECTS Track specific specialization course from exit year university |
|||
| Project 9 ECTS | |||
| Project Course | 6 | 1st | |
| Internet measurement project | 3 | 1st | |
Exit year in Grenoble INP
| Code | Course name | ECTS | Year |
|---|---|---|---|
| Communications 8 ECTS | |||
| Cellular Networks | 3 | 2nd | |
| Advanced Networking | 5 | 2nd | |
| Data Science 15 ECTS | |||
| Mathematical Foundations of Machine Learning | 3 | 2nd | |
| From Basic Machine Learning Models to Advanced Kernel Learning | 3 | 2nd | |
| Advanced Machine Learning: Applications to Vision, Audio and Text | 3 | 2nd | |
| Security 7 ECTS | |||
| Network Security | 7 | 2nd | |
| MSc thesis 30 ECTS | |||
| M.Sc. Thesis | 30 | 2nd | |
Entry year in Técnico Lisboa
Please check the up to date curriculums from partner University's own website.
| Code | Course name | ECTS | Year |
|---|---|---|---|
| General studies 12 ECTS | |||
| Engineering Project Management | 6 | 1st | |
| Entrepreneurship, Innovation and Technology | 6 | 1st | |
| Communications 12 ECTS | |||
| Wireless telecommunications systems | 6 | 1st | |
| Computer networks and internet | 6 | 1st | |
| Programming 18 ECTS | |||
| Object Oriented Programming | 6 | 1st | |
| Computational statistics | 6 | 1st | |
| Multivariate analysis | 6 | 1st | |
| Data Science 18 ECTS | |||
| Information Systems and Data Bases | 6 | 1st | |
| Machine learning | 6 | 1st | |
| Optimatization and algorithms | 6 | 1st | |
|
Specialization 5-6 ECTS Track specific specialization course from exit year university |
|||
| Project 6 ECTS | |||
| Project in Electrical and Computers Eng. | 6 | 1st | |
Exit year in Técnico Lisboa
| Code | Course name | ECTS | Year |
|---|---|---|---|
| Communications 18 ECTS | |||
| Mobile Communications Systems | 6 | 2nd | |
| Audio and video communications | 6 | 2nd | |
| Wireless and mobile networks | 6 | 2nd | |
| Data Science 12 ECTS | |||
| Data Coding and Compression | 6 | 2nd | |
| Information visualization | 6 | 2nd | |
| Artificial intelligence and decision systems | 6 | 2nd | |
| MSc thesis 30 ECTS | |||
| M.Sc. Thesis | 30 | 2nd | |
Entry year in TU Braunschweig
Please check the up to date curriculums from partner University's own website.
| Code | Course name | ECTS | Year |
|---|---|---|---|
| General studies 5 ECTS | |||
| Language course | 5 | 1st | |
| Communications 10 ECTS | |||
| Computer networks | 5 | 1st | |
| Mobile communications | 5 | 1st | |
| Programming 5 ECTS | |||
| Filled from optional courses | 5 | 1st | |
| Data Science 15 ECTS | |||
| Introduction to machine learning | 5 | 1st | |
| Mathematical foundations of data science | 10 | 1st | |
|
Specialization 5-6 ECTS Track specific specialization course from exit year university |
|||
| Project 6 ECTS | |||
| Project course | 6 | 1st | |
Exit year in TU Braunschweig
| Code | Course name | ECTS | Year |
|---|---|---|---|
| Communications 15 ECTS | |||
| Recent topics in computer networking | 5 | 2nd | |
| Seminar connected and mobile systems | 5 | 2nd | |
| Wireless networking lab | 5 | 2nd | |
| Medical informatics and biomedicine 20 ECTS | |||
| Health-enabling technologies A | 5 | 2nd | |
| Biomedical image and signal analysis | 5 | 2nd | |
| Data science in biomedicine (seminar) | 5 | 2nd | |
| Network biology | 5 | 2nd | |
| MSc thesis 30 ECTS | |||
| M.Sc. Thesis | 30 | 2nd | |
Entry year in UPC
Please check the up to date curriculums from partner University's own website.
| Code | Course name | ECTS | Year |
|---|---|---|---|
| General studies 12 ECTS | |||
| ICT-Based entrepreneurship | 3 | 1st | |
| Project on ICT based business model | 3 | 1st | |
| Service Engineering | 3 | 1st | |
| Creativity and engineering | 3 | 1st | |
| Communications 9 ECTS | |||
| Next generation wireless communications and IoT | 3 | 1st | |
| NG optical network for future cloud-based services | 3 | 1st | |
| Network security authentication and authorization | 3 | 1st | |
| Mathematics and Programming 6 ECTS | |||
| Network engineering (network science) | 3 | 1st | |
| Optimization for applied engineering design | 3 | 1st | |
| Data Science 11 ECTS | |||
| Machine learning from data | 5 | 1st | |
| Big data and data mining | 6 | 1st | |
|
Specialization 5-6 ECTS Track specific specialization course from exit year university |
|||
| Project 6 ECTS | |||
| Project course | 6 | 1st | |
Exit year in UPC
| Code | Course name | ECTS | Year |
|---|---|---|---|
| Communications 6 ECTS | |||
| Network support for 5G | 3 | 2nd | |
| 5G mobile network planning | 3 | 2nd | |
| Internet of Things ECTS | |||
| Sensors and interfaces | 3 | 2nd | |
| Low-power systems with energy harvesting | 3 | 2nd | |
| IoT and ubiquitous IP | 3 | 2nd | |
| Body sensor nodes | 3 | 2nd | |
| Mathematics and Programming 8 ECTS | |||
| Augmented reality and smart objects | 3 | ||
| Software architecture | 5 | ||
| Data Science 3 ECTS | |||
| Applied image processing (includes AI applications) | 3 | ||
| MSc thesis 30 ECTS | |||
| M.Sc. Thesis | 30 | 2nd | |
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