Master's Degree Programme in Data Expertise and Artificial Intelligence
Master of Health Care
Master of Health Care
Master of Health Care
Master of Health Care
Master of Health Care
Master of Social Services
Master of Health Care
Qualification Awarded and the Level of Qualification
Master of Engineering, EQF 7
Master of Health Care, Master of Health Care, Master of Health Care, Master of Health Care, Master of Health Care, Master of Health Care, Master of Social Services, Master of Health Care, EQF 7
Master of Business Administration, EQF 7
Contact Information
Head of Degree Programme:
Pekka Pöyry, firstname.lastname@tuni.fi
Study Affairs Coordinator:
Teija Helin
Student Counsellor:
Anja Salo
Special Admission Requirements
The Master’s degree (polytechnic) is intended for people who have completed the Bachelor degree and who have at least two years of working experience in a related field, after having completed the degree. For an applicant who has first completed a vocational or higher vocational diploma and then the applicable Bachelor’s degree, the minimum of three years of work experience may be accepted as the required work experience, provided that at lest one year has been accumulated after the completion of the Bachelor’s degree.
Recognition of Prior Learning
It is possible for students to have their prior competence recognised.
See TAMK’s credit transfer guidelines
Qualification Requirements and Regulations
Completion of curriculum studies and achievement of related competence objectives.
Further information:
TAMK Degree Regulations
Ammattikorkeakoululaki ja asetus ammattikorkeakouluista
Profile of the Programme
The degree is a master-level professional higher education degree.
The degree complies with the criteria set by the Finnish national degree system as well as with the European framework for degrees and other competence.
Ammattikorkeakoululaki ja asetus ammattikorkeakouluista
Key Learning Outcomes
Digitalization is changing society and organizations rapidly. As a result, the needs of working life are also changing. The importance of data is constantly growing and artificial intelligence is increasingly being used in various applications. These changes brought about by digitalization require experts in various fields to increase their knowledge of the data and identify the potential of artificial intelligence, without forgetting the ethical aspects involved.
The Master’s Degree Programme in Data Expertise and Artificial Intelligence programs will equip you with the ability to independently act as an expert and developer in demanding tasks that require data and utilize artificial intelligence or are intended to be utilized in the future. During the training, you will learn to recognize the different data needs of your specialist area and understand the potential of utilizing data in your expertice area. You will also learn how to pre-process, analyze and visualize data in practice.
During the training you will familiarize yourself with the concepts and methods of artificial intelligence and you will also apply the methods and applications of artificial intelligence in practice. Participating students come from a variety of educational backgrounds (including engineers, nurses, and tradenomes), which gives you an excellent opportunity to network and understand the concepts of experts in different disciplines and ways to solve problems. The aim is for students to share knowledge and skills with each other. Training will equip you to deepen your professional skills and provide you with methods and tools to continue developing your skills beyond training.
Occupational Profiles of Graduates with Examples
Companies and organizations are in dire need of experts who have not only their own substantive knowledge but also data and artificial intelligence expertise. They can work as experts in a variety of project, design and development tasks.
Job titles can include a senior engineer, a data expert, an application specialist, a project manager, a system manager, a special engineer, or a specialist.
Access to Further Studies
The master’s degree from a university of applied sciences produces the eligibility for postgraduate studies as a master’s degree from a university.
Examination Regulations, Assessment and Grading
Assessment of study performances is based on TAMK’s assessment criteria
The detailed assessment criteria can be found in course implementation plans. The teaching and assessment methods are agreed on with students at the beginning of each course.
TAMK Degree Regulations
Graduation Requirements
Completion of studies and achievement of competence objectives in the extent set by the curriculum.
Mode of Study
The tuition will be conducted as blended studies, which can be completed while working. Classroom education takes place on average every other week, on Thursdays at 16.30-20.00 and Fridays at 8.30-16.00. Alternative advanced studies may have a different schedule. This applies in particular to a degree of 90 credits.
Development of the Programme
The Ministry of Education and Culture’ definitions of policy and TAMK’s strategy have been considered in the curriculum.
The curriculum is developed in cooperation with TAMK’s other master’s degree programmes. Working life cooperation is implemented through thesises and projects as well as network cooperation and advisory councils. Student feedback is collected annually in connection with courses. Alumni feedback is collected regularly through online surveys.
Master’s Degree Programme in Data Expertise and Artificial Intelligence
Master’s Degree Programme in Data Expertise and Artificial Intelligence
Master’s Degree Programme in Data Expertise and Artificial Intelligence
Master’s Degree Programme in Data Expertise and Artificial Intelligence
Master’s Degree Programme in Data Expertise and Artificial Intelligence
Master’s Degree Programme in Data Expertise and Artificial Intelligence
Master’s Degree Programme in Data Expertise and Artificial Intelligence
Master’s Degree Programme in Data Expertise and Artificial Intelligence
Master’s Degree Programme in Data Expertise and Artificial Intelligence
Master’s Degree Programme in Data Expertise and Artificial Intelligence
Master’s Degree Programme in Data Expertise and Artificial Intelligence
Master’s Degree Programme in Data Expertise and Artificial Intelligence
Enrolment period
22.11.2023 - 05.02.2024
Timing
01.01.2024 - 09.06.2024
Credits
5 op
Mode of delivery
Contact teaching
Unit
MD in Data Expertise and Artificial Intelligence
Campus
TAMK Main Campus
Teaching languages
- Finnish
Degree programmes
- Master's Degree Programme in Data Expertise and Artificial Intelligence
Teachers
- Pekka Pöyry
Person in charge
Pekka Pöyry
Groups
-
24YDT
-
24YDS
-
24YDL
Objectives (course unit)
The student knows what data analysis and data visualization mean. The student is able to analyze data by various methods and produce data visualizations suitable for the need. The student knows the data of his / her field and its possibilities for analysis and visualization.
Content (course unit)
Theory, methods and techniques of data analysis. Various data visualization techniques. Utilization of a suitable analysis and visualization tool or tools. The student knows what data analysis and data visualization mean. The student is able to analyze data by various methods and produce data visualizations suitable for the need. The student knows the data of his / her field and its possibilities for analysis and visualization.
Assessment criteria, satisfactory (1-2) (course unit)
The student knows what data analysis and data visualization is and knows how to do data analysis and visualization. The student recognizes data related to his / her field.
Assessment criteria, good (3-4) (course unit)
The student is able to analyze data and produce some data visualizations. The student knows the data of his / her field and knows about the possibilities of analysis and visualization.
Assessment criteria, excellent (5) (course unit)
The student is able to analyze data in many ways and produces various data visualizations. The student has a good understanding of the data in his / her field and its analysis and visualization possibilities.
Assessment scale
0-5
Enrolment period
22.11.2023 - 29.01.2024
Timing
01.01.2024 - 09.06.2024
Credits
5 op
Mode of delivery
Contact teaching
Unit
MD in Data Expertise and Artificial Intelligence
Campus
TAMK Main Campus
Teaching languages
- Finnish
Degree programmes
- Master's Degree Programme in Data Expertise and Artificial Intelligence
Teachers
- Pekka Pöyry
Person in charge
Pekka Pöyry
Groups
-
24YDT
-
24YDS
-
24YDL
Objectives (course unit)
The student knows what is meant by data collected for data analysis and artificial intelligence. The student knows the techniques used to collect data and is able to solve the challenges related to data collection and processing. The student is able to collect and process data in his / her field for analysis and utilization of artificial intelligence.
Content (course unit)
Data collection and storage technologies. Methods for combining and processing data. Data collection and preparation for follow-up. Examining various scenarios for collecting and processing data in the student's field of expertise.
Assessment criteria, satisfactory (1-2) (course unit)
The student knows some data collection and storage techniques suitable for his / her field. The student is able to use some data combining and processing method in the preparation of data in his / her field. The student is able to design a data collection and processing scenario for his / her field.
Assessment criteria, good (3-4) (course unit)
The student knows the most commonly used data collection and storage techniques. The student can use the most common data combining and processing methods in the preparation of data in his / her field. The student is able to design various data collection and processing scenarios in his / her field.
Assessment criteria, excellent (5) (course unit)
The student is familiar with various data collection and storage techniques. The student is able to use various methods of data combining and processing in the preparation of data in his / her field. The student will be able to design various data collection and processing scenarios in his / her field.
Assessment scale
0-5
Enrolment period
22.11.2023 - 21.01.2024
Timing
01.01.2024 - 10.05.2024
Credits
5 op
Mode of delivery
Contact teaching
Unit
MD in Data Expertise and Artificial Intelligence
Campus
TAMK Main Campus
Teaching languages
- Finnish
Degree programmes
- Master's Degree Programme in Data Expertise and Artificial Intelligence
Teachers
- Tony Torp
Person in charge
Tony Torp
Groups
-
24YDT
-
24YDS
-
24YDL
Objectives (course unit)
The student knows the most important technologies driving digitalisation: Internet of Things and Things, Communication Technologies, Cyber Security, Software Engineering. The student knows the basic methods of producing digital services and the technological infrastructure that enables digitalization. The student knows the basic principles of artificial intelligence and machine learning.
Content (course unit)
An overview of technologies behind digitalization. Digitalization and society. Internet of Things. Overview of Communication Technologies. Production of digital services and software engineering. Cyber security. Visions for the future of digitalization.
Assessment criteria, satisfactory (1-2) (course unit)
The student knows and recognizes the technologies in the course content and their roles in different digital solutions. The student understands the general principles of technologies and their applications in digital solutions.
Assessment criteria, good (3-4) (course unit)
The student is familiar with the technologies included in the course content and recognizes their importance and roles in the overall architecture of digital services and systems. The student will also be able to present alternative solutions and development areas for existing systems.
Assessment criteria, excellent (5) (course unit)
The student knows the technologies in the course content. The student is also able to analyze various digital solutions from the point of view of the overall technological architecture and evaluate the suitability of the technologies used in the architecture for the solution. The student is also able to identify digital solutions and alternative implementation methods.
Assessment scale
0-5
Enrolment period
12.12.2023 - 10.01.2024
Timing
11.01.2024 - 31.12.2024
Credits
30 - 60
Mode of delivery
Contact teaching
Campus
TAMK Main Campus
Teaching languages
- Finnish
Seats
0 - 1
Degree programmes
- Master's Degree Programme in Data Expertise and Artificial Intelligence
Groups
-
AVOINAMK
Assessment scale
0-5
Enrolment period
12.12.2023 - 10.01.2024
Timing
11.01.2024 - 31.12.2024
Credits
30 - 60
Mode of delivery
Contact teaching
Campus
TAMK Main Campus
Teaching languages
- Finnish
Seats
0 - 1
Degree programmes
- Master's Degree Programme in Data Expertise and Artificial Intelligence
Groups
-
AVOINAMK
Assessment scale
0-5
Enrolment period
12.12.2023 - 10.01.2024
Timing
11.01.2024 - 31.12.2024
Credits
30 - 60
Mode of delivery
Contact teaching
Campus
TAMK Main Campus
Teaching languages
- Finnish
Seats
0 - 1
Degree programmes
- Master's Degree Programme in Data Expertise and Artificial Intelligence
Groups
-
AVOINAMK
Assessment scale
0-5