Student Guide
Joint International Master's Programme in Communications and Data Science
Programme main page
Curriculum 2020–2022
Degree structure
The master’s degree consists of
- Major studies 60 ECTS
- Specialisation 30 ECTS
- Master's thesis 30 ECTS
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.
Entry year in Aalto
| Area | Course code and name | ECTS credits |
|---|---|---|
| General studies | ||
| ELEC-E0110 Academic skills in MSc studies I-IV | 3 | |
| Compulsory Language course | 3 | |
| Communications | ||
| ELEC-E7130 Internet Traffic Management and Analysis I-II | 5 | |
| ELEC-E7230 Mobile communication Systems I | 5 | |
| Data Science | ||
| CS-C3240 Machine learning I | 5 | |
| CS-C1000 Artificial Intelligence IV | 3 | |
| Mathematics and Programming | ||
| MS-E1600 Probability Theory III | 5 | |
| Specialization | ||
| ELEC-C8201 Control and Automation III-IV | 5 | |
| Project | ||
| ELEC-E7633 Project Course III-V | 6 | |
| Electives – fulfill 60 credits | ||
| Student chooses from the list: Electives |
| Electives | ECTS credits |
|---|---|
| CS-A1110 Programming I | 5 |
| CS-C3130 Information Security I | 5 |
| CS-E4002 Special Course in Computer Science I-Summer | 1-10 |
| CS-E4190 Cloud Software and Systems I-II | 5 |
| CS-E4300 Network Security I-Summer | 5 |
| CS-E4340 Cryptography I-II | 5 |
| CS-E4350 Security Engineering III-IV | 5 |
| CS-E4650 Methods of Data Mining I-II | 5 |
| CS-E4710 Machine Learning: Supervised Methods I-II | 5 |
| CS-E4820 Machine Learning: Advanced Probabilistic Methods III-IV | 5 |
| CS-E4830 Kernel Methods in Machine Learning IV-V | 5 |
| CS-E4890 Deep Learning IV-V | 5 |
| CS-E5370 Law in Digital Society III-IV | 5-6 |
| CS-E5480 Digital Ethics V-summer | 3-5 |
| CS-E5710 Bayesian Data Analysis I-II | 5 |
| ELEC-E4420 Microwave Engineering III-IV | 5 |
| ELEC-E5410 Signal Processing for Communications I - II | 5 |
| ELEC-E5422 Convex Optimization 1 I-II | 5 |
| ELEC-E5423 Convex Optimization 2 II | 5 |
| ELEC-E5431 Large Scale Data Analysis III-IV | 5 |
| ELEC-E5440 Statistical Signal Processing I-II | 5 |
| ELEC-E7120 Wireless Systems I | 5 |
| ELEC-E7130 Internet Traffic Measurements and Analysis I - II | 5 |
| ELEC-E7210 Communication Theory I-II | 5 |
| ELEC-E7221 Machine Type Communications for Internet of Things III - IV | 5 |
| ELEC-E7230 Mobile Communication Systems I | 5 |
| ELEC-E7240 Coding Methods III | 5 |
| ELEC-E7311 SDN Fundamentals & Techniques III - IV | 5 |
| ELEC-E7320 Internet Protocols III - IV | 5 |
| ELEC-E7450 Performance Analysis V | 5 |
| ELEC-E7460 Modelling and Simulation I-II | 5 |
| ELEC-E7470 Cybersecurity V | 5 |
| ELEC-E7810 Patterns in Communications Ecosystems II | 5 |
| ELEC-E8001 Embedded Real-Time Systems I-II | 5 |
| ELEC-E8101 Digital and Optimal Control I-II | 5 |
| ELEC-E8102 Distributed and Intelligent Automation Systems I-II | 5 |
| ELEC-E8103 Modelling, Estimation and Dynamic Systems I-II | 5 |
| ELEC-E8740 Basics of Sensor Fusion I-II | 5 |
| MS-C1620 Statistical inference III-IV | 5 |
Entry year in a partner university
Basic studies common to all partner universities 60 ECTS
First year
| Area and ECTS credits | Course name | ECTS credits |
|---|---|---|
| General studies 12 ECTS | ||
| Engineering Project Management | 6 | |
| "Soft skills option" | 6 | |
| Communications 18 ECTS | ||
| Digital Transmission | 6 | |
| Mobile Networks and Internet of Things | 6 | |
| Multimedia Communication | 6 | |
| Data Science 24 ECTS | ||
| Object Oriented Programming | 6 | |
| Statistical Methods in Data Mining | 6 | |
| Data Analysis and Integration | 6 | |
| Information Systems and Data Bases | 6 | |
| Project 6 ECTS | ||
| Project in Electrical and Computers Eng. | 6 |
First year
| Area and ECTS credits | Course name | ECTS credits |
|---|---|---|
| General studies 7 ECTS | ||
| Research Methodology (elective) | 3 | |
| Technical Writing and Speaking in English | 3 | |
| French as a Foreign Language (elective) | 3 | |
| French Culture for Foreigners | 4 | |
| Python (elective) | 4 | |
| Other from other Universities (elective) | 4 | |
| Communications 13 ECTS | ||
| Principles of Internet | 8 | |
| Digital Transmission from Técnico Lisboa | 5 | |
| Programming 12 ECTS | ||
| Data Base Foundations | 6 | |
| Algorithmic Problem Solving | 6 | |
| Security 10 ECTS | ||
| Introduction to Cybersecurity | 10 | |
| Project 6 ECTS | ||
| Project Course | 6 |
Exit year in Aalto
| Area | Course name | ECTS credits |
|---|---|---|
| Communications | ||
| ELEC-E7910 Special Project in Communications Engineering | 5 | |
| Data Science | ||
| ELEC-E7260 Machine Learning for Mobile Systems | 5 | |
| Automation | ||
| ELEC-E8123 Networked Control Systems | 5 | |
| ELEC-E8101 Digital and optimal control | 5 | |
| MSc thesis | ||
| M.Sc. Thesis | 30 | |
| Electives – fulfill 60 credits | ||
| Student chooses from the list: Electives |
| Electives | ECTS credits |
|---|---|
| CS-A1110 Programming I | 5 |
| CS-C3130 Information Security I | 5 |
| CS-E4002 Special Course in Computer Science I-Summer | 1-10 |
| CS-E4190 Cloud Software and Systems I-II | 5 |
| CS-E4300 Network Security I-Summer | 5 |
| CS-E4340 Cryptography I-II | 5 |
| CS-E4350 Security Engineering III-IV | 5 |
| CS-E4650 Methods of Data Mining I-II | 5 |
| CS-E4710 Machine Learning: Supervised Methods I-II | 5 |
| CS-E4820 Machine Learning: Advanced Probabilistic Methods III-IV | 5 |
| CS-E4830 Kernel Methods in Machine Learning IV-V | 5 |
| CS-E4890 Deep Learning IV-V | 5 |
| CS-E5370 Law in Digital Society III-IV | 5-6 |
| CS-E5480 Digital Ethics V-summer | 3-5 |
| CS-E5710 Bayesian Data Analysis I-II | 5 |
| ELEC-E4420 Microwave Engineering III-IV | 5 |
| ELEC-E5410 Signal Processing for Communications I - II | 5 |
| ELEC-E5422 Convex Optimization 1 I-II | 5 |
| ELEC-E5423 Convex Optimization 2 II | 5 |
| ELEC-E5431 Large Scale Data Analysis III-IV | 5 |
| ELEC-E5440 Statistical Signal Processing I-II | 5 |
| ELEC-E7120 Wireless Systems I | 5 |
| ELEC-E7130 Internet Traffic Measurements and Analysis I - II | 5 |
| ELEC-E7210 Communication Theory I-II | 5 |
| ELEC-E7221 Machine Type Communications for Internet of Things III - IV | 5 |
| ELEC-E7230 Mobile Communication Systems I | 5 |
| ELEC-E7240 Coding Methods III | 5 |
| ELEC-E7311 SDN Fundamentals & Techniques III - IV | 5 |
| ELEC-E7320 Internet Protocols III - IV | 5 |
| ELEC-E7450 Performance Analysis V | 5 |
| ELEC-E7460 Modelling and Simulation I-II | 5 |
| ELEC-E7470 Cybersecurity V | 5 |
| ELEC-E7810 Patterns in Communications Ecosystems II | 5 |
| ELEC-E8001 Embedded Real-Time Systems I-II | 5 |
| ELEC-E8101 Digital and Optimal Control I-II | 5 |
| ELEC-E8102 Distributed and Intelligent Automation Systems I-II | 5 |
| ELEC-E8103 Modelling, Estimation and Dynamic Systems I-II | 5 |
| ELEC-E8740 Basics of Sensor Fusion I-II | 5 |
| MS-C1620 Statistical inference III-IV | 5 |
Exit year in a partner university
Specialization courses and Thesis
Second year
| Area and ECTS credits | Course name | ECTS credits |
|---|---|---|
| Communications 18 ECTS | ||
| High Speed Networks | 6 | |
| Mobile Communications Systems | 6 | |
| Programmable Networks | 6 | |
| Data Science 12 ECTS | ||
| Data Coding and Compression | 6 | |
| Machine Learning | 6 | |
| MSc thesis 30 ECTS | ||
| M.Sc. Thesis | 30 |
| Area and ECTS credits | Course Name | ECTS credits |
|---|---|---|
| Communications 12 ECTS | ||
| Wireless Networks an IoT | 3 | |
| Mobile Communication Systems | 4 | |
| Advanced Data Networks | 5 | |
| Data Science 12 ECTS | ||
| Fundamentals of Probabilistic Data Mining | 3 | |
| Machine Learning Fundamentals | 3 | |
| Advanced Algorithms for Machine Learning and Data Mining | 3 | |
| Security 9 ECTS | ||
| Network Security | 9 | |
| MSc thesis 30 ECTS | ||
| M.Sc. Thesis | 30 |
Master’s thesis 30 cr
Students are required to complete a Master's thesis, which is a research assignment with a workload corresponding to 30 credits. The thesis is written on a topic usually related to the student's major and agreed upon between the student and a professor who specializes in the topic of the thesis. The supervisor of the thesis is a professor or university lecturer at Aalto University. The thesis advisor(s) can be from a company or from another university. The thesis advisor(s) must have at least a Master’s degree.
Master’s thesis work includes a seminar presentation or equivalent presentation. The student is also required to write a maturity essay related to the Master’s thesis.
The Master’s thesis is a public document and cannot be concealed.
See also Thesis tab.
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