Digitalisation Technologies (5cr)

Code: 5Y00FD85-3002

General information

Enrolment period: 15.11.2020 - 31.01.2021; Registration for the implementation has ended.

Timing: 01.01.2021 - 06.06.2021; Implementation has ended.

Credits: 5 cr

Mode of delivery: Contact learning

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

Course: 5Y00FD85

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.

Exam schedules

Ei kirjallisia tenttejä. Kurssin uusiminen yksittäisten tehtävien osalta voidaan sopia toteutettavaksi kurssin toteutusvuoden aikana, mikäli kurssisuoritus on jäänyt vajaaksi annettujen tehtävien aikataulujen osalta.

Assessment scale

0-5

Teaching methods

Etäluennot ja -demonstraatiot. Itsenäinen tehtävien toteuttaminen. Oppimisalustoina Teams ja Moodle.

Learning materials

Verkossa oleva, päivittyvä, oppimateriaali, joka on linkitetty kurssin Moodle ja Teams -alueille.

Student workload

Etäluentoja 24 tuntia. Itsenäistä työskentelyä 106 tuntia. Viikottainen työaika 14 viikolle jaoteltuna on noin 9 tuntia koko kurssin osalta.

Content scheduling

Kurssin jaksotus ja tarkempi ajoitus on kirjattuna kurssin Moodle -sivuilla.