Advanced Methods of Data ProcessingLaajuus (5 cr)
Code: TT00CC57
Credits
5 op
Teaching language
- Finnish
Objective
The goal of the course is to get to know the advanced methods of data processing, using the python libraries NumPy, Pandas, and Matplotlib. The course covers the calculation of data characteristics, data distributions, data visualization and the use of regular expressions (regex). The course also introduces data clustering.
Using these methods, the course creates a data processing chain (pipeline), which is used to perform feature engineering from the data.
Assessment criteria, excellent (5)
The course consists of several exercises. At least 92% of the course's practice points must be accumulated for a grade of 5.
Assessment criteria, satisfactory (1)
The course consists of several exercises. At least 50% of the course's practice points must be accumulated for grade 1.
Enrollment
30.12.2024 - 26.01.2025
Timing
01.01.2025 - 31.07.2025
Number of ECTS credits allocated
5 op
Mode of delivery
Contact teaching
Unit
Teknologia
Teaching languages
- Finnish
Degree programmes
- Bachelor’s Degree in Information and Communication Technology
Teachers
- Pekka Huttunen
Groups
-
TTM24SAITTM24SAI
Objective
The goal of the course is to get to know the advanced methods of data processing, using the python libraries NumPy, Pandas, and Matplotlib. The course covers the calculation of data characteristics, data distributions, data visualization and the use of regular expressions (regex). The course also introduces data clustering.
Using these methods, the course creates a data processing chain (pipeline), which is used to perform feature engineering from the data.
Evaluation scale
0 - 5
Assessment criteria, excellent (5)
The course consists of several exercises. At least 92% of the course's practice points must be accumulated for a grade of 5.
Assessment criteria, satisfactory (1)
The course consists of several exercises. At least 50% of the course's practice points must be accumulated for grade 1.
Prerequisites
Python programming
Modern software development
Algebra
Enrollment
30.12.2024 - 26.01.2025
Timing
01.01.2025 - 31.07.2025
Number of ECTS credits allocated
5 op
Mode of delivery
Contact teaching
Unit
Teknologia
Teaching languages
- Finnish
Degree programmes
- Bachelor’s Degree in Information and Communication Technology
Teachers
- Pekka Huttunen
Groups
-
TTV23SRAATTV23SRAA
Objective
The goal of the course is to get to know the advanced methods of data processing, using the python libraries NumPy, Pandas, and Matplotlib. The course covers the calculation of data characteristics, data distributions, data visualization and the use of regular expressions (regex). The course also introduces data clustering.
Using these methods, the course creates a data processing chain (pipeline), which is used to perform feature engineering from the data.
Evaluation scale
0 - 5
Assessment criteria, excellent (5)
The course consists of several exercises. At least 92% of the course's practice points must be accumulated for a grade of 5.
Assessment criteria, satisfactory (1)
The course consists of several exercises. At least 50% of the course's practice points must be accumulated for grade 1.
Prerequisites
Python programming
Modern software development
Algebra
Enrollment
01.12.2023 - 31.01.2024
Timing
01.01.2024 - 08.04.2024
Number of ECTS credits allocated
5 op
Mode of delivery
Contact teaching
Unit
Teknologia
Teaching languages
- Finnish
Degree programmes
- Bachelor’s Degree in Information and Communication Technology
Teachers
- Pekka Huttunen
Groups
-
TTM23SAITTM23SAI
Objective
The goal of the course is to get to know the advanced methods of data processing, using the python libraries NumPy, Pandas, and Matplotlib. The course covers the calculation of data characteristics, data distributions, data visualization and the use of regular expressions (regex). The course also introduces data clustering.
Using these methods, the course creates a data processing chain (pipeline), which is used to perform feature engineering from the data.
Evaluation scale
0 - 5
Assessment criteria, excellent (5)
The course consists of several exercises. At least 92% of the course's practice points must be accumulated for a grade of 5.
Assessment criteria, satisfactory (1)
The course consists of several exercises. At least 50% of the course's practice points must be accumulated for grade 1.
Prerequisites
Python programming
Modern software development
Algebra
Enrollment
02.12.2022 - 31.01.2023
Timing
01.01.2023 - 01.05.2023
Number of ECTS credits allocated
5 op
Mode of delivery
Contact teaching
Unit
Teknologia
Teaching languages
- Finnish
Degree programmes
- Bachelor’s Degree in Information and Communication Technology
Teachers
- Pekka Huttunen
Groups
-
TTV22SAITTV22SAI
Objective
The goal of the course is to get to know the advanced methods of data processing, using the python libraries NumPy, Pandas, and Matplotlib. The course covers the calculation of data characteristics, data distributions, data visualization and the use of regular expressions (regex). The course also introduces data clustering.
Using these methods, the course creates a data processing chain (pipeline), which is used to perform feature engineering from the data.
Evaluation scale
0 - 5
Assessment criteria, excellent (5)
The course consists of several exercises. At least 92% of the course's practice points must be accumulated for a grade of 5.
Assessment criteria, satisfactory (1)
The course consists of several exercises. At least 50% of the course's practice points must be accumulated for grade 1.
Prerequisites
Python programming
Modern software development
Algebra
Enrollment
02.12.2022 - 31.01.2023
Timing
01.01.2023 - 12.04.2023
Number of ECTS credits allocated
5 op
Mode of delivery
Contact teaching
Unit
Teknologia
Teaching languages
- Finnish
Degree programmes
- Bachelor’s Degree in Information and Communication Technology
Teachers
- Pekka Huttunen
Groups
-
TTM22SAITTM22SAI
Objective
The goal of the course is to get to know the advanced methods of data processing, using the python libraries NumPy, Pandas, and Matplotlib. The course covers the calculation of data characteristics, data distributions, data visualization and the use of regular expressions (regex). The course also introduces data clustering.
Using these methods, the course creates a data processing chain (pipeline), which is used to perform feature engineering from the data.
Evaluation scale
0 - 5
Assessment criteria, excellent (5)
The course consists of several exercises. At least 92% of the course's practice points must be accumulated for a grade of 5.
Assessment criteria, satisfactory (1)
The course consists of several exercises. At least 50% of the course's practice points must be accumulated for grade 1.
Prerequisites
Python programming
Modern software development
Algebra