Advanced Methods of Data Processing (5 cr)
Code: TT00CC57-3005
General information
- Enrollment
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30.12.2024 - 26.01.2025
Registration for the implementation has ended.
- Timing
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01.01.2025 - 31.07.2025
Implementation is running.
- Number of ECTS credits allocated
- 5 cr
- Local portion
- 5 cr
- Mode of delivery
- Contact learning
- Unit
- Teknologia
- Teaching languages
- Finnish
- Degree programmes
- Bachelor’s Degree in Information and Communication Technology
Realization has 7 reservations. Total duration of reservations is 7 h 30 min.
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Thu 09.01.2025 time 12:45 - 14:15 (1 h 30 min) |
Datan käsittelyn kehittyneet menetelmät TT00CC57-3005 |
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Mon 13.01.2025 time 12:45 - 13:45 (1 h 0 min) |
Q&A_Datan käsittelyn kehittyneet menetelmät TT00CC57-3005 |
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Mon 20.01.2025 time 12:45 - 13:45 (1 h 0 min) |
Q&A_Datan käsittelyn kehittyneet menetelmät TT00CC57-3005 |
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Mon 27.01.2025 time 12:45 - 13:45 (1 h 0 min) |
Q&A_Datan käsittelyn kehittyneet menetelmät TT00CC57-3005 |
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Mon 10.02.2025 time 12:45 - 13:45 (1 h 0 min) |
Q&A_Datan käsittelyn kehittyneet menetelmät TT00CC57-3005 |
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Mon 24.02.2025 time 12:45 - 13:45 (1 h 0 min) |
Q&A_Datan käsittelyn kehittyneet menetelmät TT00CC57-3005 |
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Mon 10.03.2025 time 12:45 - 13:45 (1 h 0 min) |
Q&A_Datan käsittelyn kehittyneet menetelmät TT00CC57-3005 |
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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