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
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