Master's Degree Programme in Information and Communication Technology: Data Analytics
The Master’s Degree Programme in Information and Communication Technology provides versatile and high quality ICT education in selected fields of ICT, with an established reputation in innovative, interdisciplinary, and international education.
The Programme has an international double-degree collaboration with the European Institute of Innovation and Technology (EIT) Master School. In the academic year 2023-2024, the Programme offers five specialisation tracks:
The Data Analytics track trains specialists for the effective utilization and communication of data in research, decision-making, and society. The focus of teaching is on understanding and applying the operational principles of the key data analysis methods in practice.
Teaching in the field is linked to the current research themes of the Department of Computing, from health technology to computational humanities and from language technology to intelligent systems. Our research activities and collaborative projects with applied research fields, companies, and public actors enable a diverse in-depth selection of courses and thesis topics.
You are an eligible applicant for Master’s degree studies if
- you have a nationally recognized first cycle degree – normally a Bachelor’s degree – from an accredited institution of higher education,
- your degree corresponds to at least 180 ECTS (European credits) or to three years of full-time study,
- your degree is in a relevant field for the Master’s degree programme that you’re applying to. Please check the section on track-specific admission requirements for detailed degree requirements.
Applicants must have excellent English language skills and a certificate that proves those skills. You can indicate your language skills by taking one of the internationally recognized English language tests.
Applicants must reach the minimum required test results to be considered eligible to the University of Turku. No exceptions will be made. Read more about the language requirements here.
Before you start preparing your application, always read the full admission requirements on the application portal Studyinfo.fi
The applicant’s previous degree on the basis of which s/he is seeking admission to the Master’s Degree Programme in Information and Communication Technology must be in a relevant field of study. Relevant fields of previous studies for the Data Analytics specialisation track are:
- Computer Science
- Computer Technology
- Information Technology
- Other relevant fields of studies (e.g. statistics, mathematics, physics) provided that sufficient knowledge of programming is achieved.
In addition to the education diploma and language certificate, you should include testimonials of any relevant work experience in your application. Here you can see the programme-specific questions on the application form.
You may apply to one specialisation track out of the five tracks offered in the Master’s Degree Programme in Information and Communication Technology.
Annually 55 students are admitted to the Master's Degree Programme in Information and Communication Technology. The decision of admission will be based on:
- the relevance of the applicant’s awarded degree(s)
- the amount, relevance, and grades of the courses in the degree(s)
- the language test result (see Language requirements)
- the motivation letter
- the possible answers to the optional questions included in the application
- the possible relevant work experience
- the possible interview.
Please tell in the motivation letter why you are applying to the Master's Degree Programme in Information and Communication Technology at the University of Turku, and especially why you are applying to the chosen track.
It is possible to have only one Bachelor’s or Master’s study right at the Faculty of Technology. Therefore, when accepting an offered study place, the student will lose any previous BSc or MSc study right at the same faculty.
Programme in brief
A. Advanced-level studies in the major subject 50 ECTS
B. Thematic module or minor subject 20-25 ECTS
C. Elective studies 15-20 ECTS
D. Master of Science in Technology thesis 30 ECTS
The research of the data analytics team is currently organized in three laboratories: Algorithmics and Computational Intelligence (ACI), Natural language processing (NLP), and Data Science.
The research of the ACI group is based on the long tradition of combining basic research on algorithm development and analysis with active cooperation with companies and academic partners on solving real-life problems by the use of combinatorial optimization and latest techniques on Artificial Intelligence and especially machine learning.
The NLP group studies various aspects of natural language processing, human language technology and digital linguistics, with a distinct focus on machine learning methods.
The Data Science group's research combines theory and methods of algorithmic data analysis, with a particular focus on probabilistic machine learning, complex systems, high-throughput data analysis and statistical programming. The main application areas are in computational humanities and molecular life sciences.
In the Master's thesis, the student must prove their ability to do scientific work and mastery of research methods, knowledge of the research field, and skills in scientific writing. In writing the Master’s thesis, the student needs to apply their technology skills, abilities and expertise to identify an engineering problem, study its relevance to the field, propose and design a solution, and test and analyse the solution and evaluate its usefulness.
The thesis is often commissioned by a company, which means that an efficient and capable student could be directly employed by the commissioning company, giving a head start to a career in industry. On the other hand, an academically-oriented student might choose a thesis topic related to research conducted by one of our research groups, and apply for the position of doctoral candidate upon completion of an academically distinguished Master’s thesis.
After completing a degree in data analytics, you
- are aware of the main methods of data analysis, their theoretical basis, their possibilities and their limitations;
- can apply scientific methods in the field and either find existing, or, when necessary, develop your own methods for data analysis;
- can search and critically evaluate scientific literature;
- acquire the skills based on the latest research for wide-ranging and responsible expert and management positions in the field of data analysis and more broadly in ICT;
- achieve multidisciplinary competencies that support diverse professional development and lifelong learning in changing work environments;
- master the central cooperation and oral and written communication skills required in working life;
- know how to work and get around in a multidisciplinary and multicultural work environment;
- understand the importance of science and technology to the individual, society, the environment; and
- are ready for postgraduate scientific studies in data analytics and related fields.
Data and data analytics are the cornerstones of digitalization and artificial intelligence.
The need for the effective utilization and communication of data in research, decision-making and society is growing rapidly, offering diverse career opportunities for those skilled in the field. The importance of open and transparent data has also grown, hence creating opportunities for a rapidly growing range of services and businesses. Knowledge management has become one of the most important factors in the competitiveness of companies, and data analytics plays a key role in this.
Data analytics is widely applicable in various sectors of society, and the exploitation of explosively increased data requires specialists, who are in high demand in the labor market.
Master's Degree (Technology) provides eligibility for scientific postgraduate degree studies. Postgraduate degrees are doctoral and licentiate degrees. Degrees can be completed within the University of Turku Graduate School.