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Data Collection and Processing (5 cr)

Code: 5Y00FD86-3004

General information


Enrolment period
22.11.2023 - 29.01.2024
Registration for the implementation has ended.
Timing
01.01.2024 - 09.06.2024
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
Pekka Pöyry
Person in charge
Pekka Pöyry
Course
5Y00FD86

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.

Exam schedules

ppimistehtävä

Assessment methods and criteria

Arvosana muodostuu oppimistehtävän perusteella.
Seuraavassa pisteet ja arvosana:

0 0
16 1
22 2
30 3
38 4
46 5

Assessment scale

0-5

Learning materials

moodlessa

Student workload

lähiopetusta 6*4 tuntia, itsenäistä työskentelyä 5*26,7 - 6*4 tuntia

Completion alternatives

oppimistehtävä

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