Data Collection and ProcessingLaajuus (5 cr)
Code: 5Y00FD86
Credits
5 op
Objectives
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
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)
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)
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)
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.
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
15.11.2021 - 23.01.2022
Timing
01.01.2022 - 08.05.2022
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
- Esa Kujansuu
- Pekka Pöyry
Person in charge
Esa Kujansuu
Groups
-
22YDT
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