Data is widely available, but what is scarce is the ability to extract wisdom from it. Skilled experts with the tools to collect and interpret signals and data are more in demand than ever. The Signal Processing and Data Science major sits at the intersection of many timely challenges, from predicting the stock market to developing better medical technology. As industries shift toward sustainability, you will play a role in shaping smarter, greener solutions, e.g., by developing more efficient algorithms that help reduce the energy demands of large-scale computations needed in AI systems.
Benefiting from world-class research and facilities, students graduating from the Signal Processing and Data Science major will have:
In this webinar, the programme representatives introduce Aalto University, give a programme overview, explain the programme structure and content, discuss career opportunities, and answer questions...
In the Master’s Programme in Computer, Communication and Information Sciences – Signal Processing and Data Science, the language of instruction is English.
The tuition fee for this programme is 17 000 euros per academic year. Citizens of European Union (EU), the European Economic Area (EEA) or Switzerland do not pay tuition fees. Citizens of other countries must pay tuition fees.
Aalto University offers a small number of scholarships in the form of tuition fee waivers to fee-paying students. Scholarships can be awarded to the highest-achieving applicants based on the programme's evaluation criteria. Applicants are ranked according to the criteria outlined on the programme's webpage.
More information on tuition fees and scholarships at Aalto University is available at the Scholarships and Tuition Fees webpage.
The major Signal Processing and Data Science in the Master's Programme in Computer, Communication and Information Sciences is organised by the School of Electrical Engineering and comprises a total of 120 ECTS credits. The two-year programme consists of:
The major can be completed either as a long (60 ECTS) or compact (40 ECTS) major. Students taking a compact major take also a master level minor (20-25 ECTS). Students taking a long major may include an optional minor in their elective studies.
The first year lays the foundation for signals and systems modelling and data analysis with courses such as Signal Processing for Communications (5 ECTS), Statistical Signal Processing (5 ECTS) and Large Scale Data Analysis (5 ECTS). Students also familiarise themselves with machine learning. As the studies proceed, students deepen their understanding on various topics and can create their own unique study tracks according to their interests.
Signal Processing and Data Science is part of the Master's Programme in Computer, Communication and Information Sciences. Students may direct their studies to various specialisation areas through master level minor or elective courses. A student interested in, for example, biomedical engineering and AI lead health technology may select courses in those fields. There is also a possibility to learn more about financial engineering or specialise in data science.
A few examples from the wider course selection are as follows:
More information on the programme content and curriculum can be found in the Student guide.
Lisa Petry, a master's student in the Signal Processing and Data Science major, thinks that the increasing adoption of AI in industries highlights the need for skilled professionals who can work with data and therefore can provide a deeper understanding of the world around us based on given data.
'Strong theoretical background with plenty of practical course project experience is exactly the type of experience that many companies are looking for,' says Eeli Susan.
The study environment in the programme is strongly international – studies are conducted in multicultural groups and students are well equipped to embark on an international career. The School of Electrical Engineering offers many possibilities for student exchange and internships all over the world. Students may find themselves taking part in a research project dealing with global phenomena or taking a summer course at one of Aalto's partner institutions.
Aalto University is international by nature, welcoming thousands of degree and exchange students from abroad every year. These students join the diverse Aalto community not only through their studies, but also through multiple free time events, celebrations and extracurricular activities around the campus. Active tutoring programs and support services work hard to help international students integrate to the Nordic culture and feel at home in Finland.
The degree programme provides eligibility for scientific postgraduate studies in Finland. The skills learned in the programme create an excellent basis for doctoral studies at Aalto University, another Finnish university or top international universities. Doctoral graduates from Aalto University continue to researcher or other academic career or high-level business positions, among other things.
Read more about applying for doctoral studies at Aalto University:
https://www.aalto.fi/en/doctoral-education/how-to-apply-for-doctoral-studies
Graduates of the major have an exceptionally wide range of career paths in front of them. As signals (e.g., images, speech and sound, financial time series, or measurements from diverse sensor systems) are acquired, processed and transmitted nearly in every imaginable field, graduates may be employed in the health technology, quantitative finance, ICT, or data science / AI companies. Common professions include:
Aalto University has well-established career services to support students’ employment in Finland and abroad. Thanks to the flexible curriculum, many Aalto students already work during their studies and guarantee themselves entry positions before graduation. There is also a very active entrepreneurship community at Aalto, working as a springboard for founding a company.
Graduates from the School of Electrical Engineering at Aalto University have very good employment prospects in positions corresponding to their education. On this page, you can find information about employment and career development five years after graduation.
Research projects are thoroughly integrated into the studies to foster students’ ability to conduct scientific work. Examples of research topics include but are not limited to the following:
To find out more about the research conducted in the Department of Information and Communications Engineering, visit their website.
Besides the wide selection of courses available at Aalto University, students have the opportunity to include courses in their curriculum from other universities – even internationally. The major Signal Processing and Data Science also regularly welcomes visiting students from other universities. The CCIS (Computer, Communication and Information Sciences) programme is in itself multidisciplinary, encouraging collaboration across disciplines.
Aalto University is well-known for bridging disciplines of business, arts, technology and science. The lively campus and freedom of choosing elective courses across the university bring students from different fields under one roof. This spontaneous multidisciplinary environment sparks new ideas, gathers enthusiasts around them and gives birth to friendships, networks, and every so often, startups.
The department of Information and Communications Engineering has a strong focus in the ICT area varying from ICT technology to core electrical engineering and its basic phenomena.
Curious about life at Aalto University? Chat with Aalto students on Unibuddy and join them for virtual Coffee sessions on Fridays!
Applicants meeting Aalto’s general eligibility criteria for master's studies will be evaluated and ranked according to the evaluation criteria that have been decided in advance for each study option. The applications to Master’s Programme in Computer, Communication and Information Sciences – Signal Processing and Data Science will be evaluated based on the following criteria.
This is the master version of the programme-specific evaluation criteria and its Finnish and Swedish versions are its translations.
| What is evaluated? | Grades of the previous degree(s) and pace of studies |
| What we look for in an applicant |
The CCIS programme is looking for applicants with excellent academic performance in their previous studies. This means that the applicant has achieved consistently excellent grades throughout the degree studies (very high weighted average grade or GPA). The applicant’s prior academic performance will be evaluated based on the grade point average (GPA) and grades in key courses. The programme needs to be able to assess academic performance adequately. The GPA can only be calculated if more than 50% of the evaluation credits are numerically graded (i.e., scales beyond a simple Pass/Fail). Also the key courses should be numerically graded. The time spent on the previous studies will also be considered. All the applicant’s previous university studies, including incomplete degrees and non‐degree studies, will be taken into consideration when evaluating academic performance. Applicants from Finnish universities of applied sciences (AMK) |
| Method of demonstrating competence | Transcript(s) of records, degree certificate(s) |
| What is evaluated? | Contents and scope of previous studies |
| What we look for in an applicant |
Applicants are expected to have a high-quality Bachelor’s degree in computer science, software engineering, communications engineering, or electrical engineering. Excellent candidates with degrees in other fields including but not limited to information systems, engineering, natural sciences, mathematics, or physics will be considered if they have sufficient studies in the required areas. The required university-level studies, as well as subjects that are considered an advantage, are defined below for major Signal Processing and Data Science: Required university-level studies:
Studies in at least some of the following subjects are considered an advantage:
The contents of the applicant’s previous degree(s) are evaluated based on the courses on the official transcript of records. Relevant work experience, professional certificates and online courses can also be taken into account in the evaluation, but they do not in general compensate for the absence of university-level studies covering the theoretical foundations of the knowledge areas. |
| Method of demonstrating competence | Transcript(s) of records, course descriptions |
| What is evaluated? | Recognition and quality of the applicant's previous institution |
| What we look for in an applicant | We expect applicants to have completed their previous degree in a high-quality university and programme. The recognition of the applicant’s home university also affects the interpretation of the grades when evaluating the academic performance. |
| Method of demonstrating competence | International and national rankings of higher education institutions |
| What is evaluated? | Applicant’s suitability to the study option, motivation, commitment to the studies, and written communication skills |
| What we look for in an applicant |
The applicant should be motivated to study the chosen subject and committed to full‐time studies with a plan to complete the Master’s degree in two years. We are looking for applicants who can express clearly the reasons for applying to the study option and describe why they would be excellent candidates for it. Studies in the Master’s programme should provide genuinely new knowledge for the applicant. If the applicant already has a Master’s or a higher degree or is studying towards one, the motivation questionnaire should clearly indicate why another one is necessary. In most cases, non-degree studies are recommended instead. |
| Method of demonstrating competence | Application as a whole including motivation questionnaire |
| What is evaluated? | Work experience, other qualifications and achievements |
| What we look for in an applicant |
Beyond their academic record, applicants may have other experience, knowledge and qualifications that prepare them for the Master’s studies and distinguish them among their peers. We particularly value demanding work experience related to the planned studies, participation in scientific research leading to publications, entrepreneurship, and special achievements such as success in competitions (e.g. Junction Hackathon). |
| Method of demonstrating competence | Curriculum vitae, recommendation letters, proofs of employment, publications, other certificates, GRE or GMAT results (if available). |
During the evaluation of eligible applicants, applications are first evaluated based on the following criteria:
Only the applications who fulfil the requirements for these criteria will be evaluated against the full set of evaluation criteria. It is not possible to compensate for these criteria with other criteria.
After the evaluation of the remaining criteria below, the best applicants will be selected based on the joint evaluation of all criteria.
The admission process is very competitive and only the best applicants are selected yearly. Not all applicants fulfilling the requirements can be admitted.
Applications missing any of the required study-option specific documents i.e. motivation questionnaire, curriculum vitae, and the declaration on the use of AI in study-option-specific documents, are rejected.
This study option (master’s programme) requires two sets of documents from their applicants:
Applicants need to fill in the motivation questionnaire (.doc-file) according to the questionnaire instructions. No other document can be used to showcase the applicant’s motivation for the programme. Note that the same motivation questionnaire template is also used for other study options than Signal Processing and Data Science, and the applicant is asked to mark in the questionnaire, to which study option it is attached to.
The lack of this document will cause rejection.
The lack of this document will cause rejection.
The lack of this document will cause rejection.
We value authenticity and your personal voice in the application documents. You may use AI tools only for:
Using AI tools to generate or significantly rewrite content, structure, or ideas is discouraged. Be aware that using AI tools may result in application materials that do not contain the qualities sought in the evaluation, and may negatively impact your chances for admission. In all situations, you are responsible for the final content of your application.
All applicants must declare their use or non-use of AI tools in the application. The declaration must allow an evaluator to distinguish between content created by you from content created or enhanced using an AI tool. You must specify:
• Which AI tools were used,
• for which purpose, and
• for which parts of the application.
I used ChatGPT 5.1 for improving the language of the entire motivation letter.
I used the prompt "What points should I bring up in my motivation letter for my CCIS application?" in Claude Sonnet 4.6. I wrote an initial draft of the motivation letter based on the generated points. I then revised the draft with more details to create the final motivation letter.
I used Microsoft Copilot in Word to format my CV. I described the font types and sizes as well as the colours to use, and Copilot implemented the changes.
If no AI tools were used, state: "I did not use any AI tools in preparing my application materials."
Attach the declaration to your application as a separate file.
Using AI tools to fabricate, misrepresent, or impersonate – including submitting AI-generated content as your own – is prohibited and considered academic misconduct or fraud.
Aalto University Admission Services
If you have any questions regarding the application process, application documents or language tests, please contact Aalto University Admission Services
admissions@aalto.fi
School of Electrical Engineering
For enquiries regarding the content of programme, the studies at the School of Electrical Engineering and the study-option specific application documents please contact
masters-elec@aalto.fi
