Data-analysis, Data Visualization and Artificial Intelligence (5 cr)

Objectives (course unit)

The student knows the basics of data analysis and the most important methods. The student knows how to process, analyze and visualize data. The student knows the basics of artificial intelligence, the most important concepts and is able to interpret the results and know the limitations and possibilities of the methods.

Content (course unit)

The basics of data analysis and the main methods. Processing, analyzing and visualizing data. Basics of Artificial Intelligence, key concepts and methodology evaluation.

Assessment criteria, satisfactory (1-2) (course unit)

The student is able to process data, analyze and make visualizations. The student knows the basics of artificial intelligence and the most important concepts.

Assessment criteria, good (3-4) (course unit)

The student is able to process data, analyze and make visualizations. The student knows the basics of artificial intelligence, the most important concepts and knows the limitations and possibilities of methods.

Assessment criteria, excellent (5) (course unit)

The student is able to handle data in a versatile way, analyze and visualize. Students are familiar with the basics of artificial intelligence, the most important concepts and are able to interpret the results and know the limitations and possibilities of the methods.

Exam schedules

Ei tenttiä

Evaluation methods and criteria

Yhteispisteet 100, joka jakautuu puoliksi data-analyysin ja tekoälyn kesken.

Seuraavassa pisteet ja arvosana:

0 0
30 1
45 2
60 3
75 4
90 5

Assessment scale

0-5

Teaching methods

Etäopetusta teamsissa, harjoituksia ja oppimistehtäviä

Learning materials

Materiaali moodlessa

Further information

Läsnäolo teamsissa toivottavaa, koska tehdään tehtäviä hands-on tyyppisesti.

Assessment criteria - fail (0) (Not in use, Look at the Assessment criteria above)

Oppimistehtäviä ei ole palautettu tai ne eivät vastaa arviointikriteerejä. Tarkemmin moodlessa.