Data PreprocessingLaajuus (4 cr)
Code: TTC8030
Credits
4 op
Teaching language
- Finnish
Responsible person
- Antti Häkkinen
Objective
After the course, you will understand the data analytics process and the challenges it brings. You can identify different data formats, the most common interface solutions and the tools and methods used in data preprocessing. In addition, you know how to apply the methods necessary for data preprocessing.
EUR-ACE Competences:
Knowledge and Understanding
Engineering Practice
Content
o Various data sources and data formats, JSON, APIt, retrieval of tables from SQL
o Types of variables
o Data preprocessing before bringing it into analysis program
o Data preprocessing in Pandas (basics of Pandas/DataFrames)
o Connecting various data sources
Qualifications
Basic skills of computer use, basic skills in programming, knowledge of Python programming language.
Assessment criteria, satisfactory (1)
Satisfactory 2: The student masters data retrieval from a selected source. You are able to implement data preprocessing to datasets. You are able to apply simple methods used in data preprocessing to his/her data. You can assess your own solutions for data preprocessing.
Sufficient 1: You know and understand the significance of data and its advantages. You know the significance of data preprocessing and its most common methods. You are able to apply simple methods obtained and used in data preprocessing.
Assessment criteria, good (3)
Very good 4: You master the retrieval of data from various sources. You are able to plan and implement data preprocessing for various datasets. You are able to apply the methods used in data preprocessing widely. You can assess and justify your own solutions for data preprocessing.
Good 3: You master data retrieval from several sources. You are able to plan and implement data preprocessign for datasets. You are able to apply methods used in data preprocessing. You can to assess and justify your own solutions for data preprocessing.
Assessment criteria, excellent (5)
Excellent 5: You master the retrieval of data from various sources. You are able to plan and implement data processing for various datasets. You are able to apply the methods used in data preprocessing. You can critically assess and justify your own solutions for data preprocessing.
Enrollment
18.11.2024 - 09.01.2025
Timing
20.01.2025 - 16.02.2025
Number of ECTS credits allocated
4 op
Virtual portion
4 op
Mode of delivery
Online learning
Unit
School of Technology
Teaching languages
- English
Seats
0 - 35
Degree programmes
- Bachelor's Degree Programme in Information and Communications Technology
- Bachelor's Degree Programme in Information and Communications Technology
Teachers
- Antti Häkkinen
Groups
-
TTV22S5Tieto- ja viestintätekniikka (AMK)
-
TTV22S2Tieto- ja viestintätekniikka (AMK)
-
TTV22S3Tieto- ja viestintätekniikka (AMK)
-
TIC22S1Bachelor's Degree Programme in Information and Communications Technology
-
TTV22S1Tieto- ja viestintätekniikka (AMK)
-
TTV22SMTieto- ja viestintätekniikka (AMK)
-
TTV22S4Tieto- ja viestintätekniikka (AMK)
-
TTV22SM2Tieto- ja viestintätekniikka (AMK)
-
ZJA25KTIDA1Avoin amk, Data-analytiikka 1, Verkko
Objectives
After the course, you will understand the data analytics process and the challenges it brings. You can identify different data formats, the most common interface solutions and the tools and methods used in data preprocessing. In addition, you know how to apply the methods necessary for data preprocessing.
EUR-ACE Competences:
Knowledge and Understanding
Engineering Practice
Content
o Various data sources and data formats, JSON, APIt, retrieval of tables from SQL
o Types of variables
o Data preprocessing before bringing it into analysis program
o Data preprocessing in Pandas (basics of Pandas/DataFrames)
o Connecting various data sources
Time and location
The course is implemented online (no contact teaching). The student can proceed at his own pace during the course.
Learning materials and recommended literature
Course website (lecture material, exercises, other instructions).
Teaching methods
The course includes assignments from different subject areas of the course.
Student workload
Environment preparations, exercises and familiarisation with the material 108 h. Total 108 h
Evaluation scale
0-5
Evaluation criteria, satisfactory (1-2)
Satisfactory 2: The student masters data retrieval from a selected source. You are able to implement data preprocessing to datasets. You are able to apply simple methods used in data preprocessing to his/her data. You can assess your own solutions for data preprocessing.
Sufficient 1: You know and understand the significance of data and its advantages. You know the significance of data preprocessing and its most common methods. You are able to apply simple methods obtained and used in data preprocessing.
Evaluation criteria, good (3-4)
Very good 4: You master the retrieval of data from various sources. You are able to plan and implement data preprocessing for various datasets. You are able to apply the methods used in data preprocessing widely. You can assess and justify your own solutions for data preprocessing.
Good 3: You master data retrieval from several sources. You are able to plan and implement data preprocessign for datasets. You are able to apply methods used in data preprocessing. You can to assess and justify your own solutions for data preprocessing.
Evaluation criteria, excellent (5)
Excellent 5: You master the retrieval of data from various sources. You are able to plan and implement data processing for various datasets. You are able to apply the methods used in data preprocessing. You can critically assess and justify your own solutions for data preprocessing.
Prerequisites
Basic skills of computer use, basic skills in programming, knowledge of Python programming language.
Enrollment
01.08.2024 - 22.08.2024
Timing
02.09.2024 - 06.10.2024
Number of ECTS credits allocated
4 op
Virtual portion
4 op
Mode of delivery
Online learning
Unit
School of Technology
Teaching languages
- Finnish
Seats
0 - 35
Degree programmes
- Bachelor's Degree Programme in Information and Communications Technology
Teachers
- Antti Häkkinen
Groups
-
TTV22S5Tieto- ja viestintätekniikka (AMK)
-
TTV22S2Tieto- ja viestintätekniikka (AMK)
-
TTV22S3Tieto- ja viestintätekniikka (AMK)
-
TTV22S1Tieto- ja viestintätekniikka (AMK)
-
TTV22SMTieto- ja viestintätekniikka (AMK)
-
TTV22S4Tieto- ja viestintätekniikka (AMK)
-
TTV22SM2Tieto- ja viestintätekniikka (AMK)
-
ZJA24STIDA1Avoin amk, Data-analytiikka 1, Verkko
Objectives
After the course, you will understand the data analytics process and the challenges it brings. You can identify different data formats, the most common interface solutions and the tools and methods used in data preprocessing. In addition, you know how to apply the methods necessary for data preprocessing.
EUR-ACE Competences:
Knowledge and Understanding
Engineering Practice
Content
o Various data sources and data formats, JSON, APIt, retrieval of tables from SQL
o Types of variables
o Data preprocessing before bringing it into analysis program
o Data preprocessing in Pandas (basics of Pandas/DataFrames)
o Connecting various data sources
Time and location
The course is implemented online (no contact teaching). The student can proceed at his own pace during the course.
Learning materials and recommended literature
Course website (lecture material, exercises, other instructions).
Teaching methods
The course includes assignments from different subject areas of the course.
Student workload
Environment preparations, exercises and familiarisation with the material 108 h. Total 108 h
Evaluation scale
0-5
Evaluation criteria, satisfactory (1-2)
Satisfactory 2: The student masters data retrieval from a selected source. You are able to implement data preprocessing to datasets. You are able to apply simple methods used in data preprocessing to his/her data. You can assess your own solutions for data preprocessing.
Sufficient 1: You know and understand the significance of data and its advantages. You know the significance of data preprocessing and its most common methods. You are able to apply simple methods obtained and used in data preprocessing.
Evaluation criteria, good (3-4)
Very good 4: You master the retrieval of data from various sources. You are able to plan and implement data preprocessing for various datasets. You are able to apply the methods used in data preprocessing widely. You can assess and justify your own solutions for data preprocessing.
Good 3: You master data retrieval from several sources. You are able to plan and implement data preprocessign for datasets. You are able to apply methods used in data preprocessing. You can to assess and justify your own solutions for data preprocessing.
Evaluation criteria, excellent (5)
Excellent 5: You master the retrieval of data from various sources. You are able to plan and implement data processing for various datasets. You are able to apply the methods used in data preprocessing. You can critically assess and justify your own solutions for data preprocessing.
Prerequisites
Basic skills of computer use, basic skills in programming, knowledge of Python programming language.
Enrollment
20.11.2023 - 04.01.2024
Timing
15.01.2024 - 18.02.2024
Number of ECTS credits allocated
4 op
Virtual portion
4 op
Mode of delivery
Online learning
Unit
School of Technology
Teaching languages
- English
Seats
0 - 30
Degree programmes
- Bachelor's Degree Programme in Information and Communications Technology
- Bachelor's Degree Programme in Information and Communications Technology
Teachers
- Antti Häkkinen
Groups
-
TTV21S3Tieto- ja viestintätekniikka (AMK)
-
TTV21S5Tieto- ja viestintätekniikka (AMK)
-
TTV21SMTieto- ja viestintätekniikka (AMK)
-
TIC21S1Bachelor's Degree Programme in Information and Communications Technology
-
TTV21S2Tieto- ja viestintätekniikka (AMK)
-
ZJA24KTIDA1Avoin amk, Data-analytiikka 1, Verkko
-
TTV21S1Tieto- ja viestintätekniikka (AMK)
Objectives
After the course, you will understand the data analytics process and the challenges it brings. You can identify different data formats, the most common interface solutions and the tools and methods used in data preprocessing. In addition, you know how to apply the methods necessary for data preprocessing.
EUR-ACE Competences:
Knowledge and Understanding
Engineering Practice
Content
o Various data sources and data formats, JSON, APIt, retrieval of tables from SQL
o Types of variables
o Data preprocessing before bringing it into analysis program
o Data preprocessing in Pandas (basics of Pandas/DataFrames)
o Connecting various data sources
Time and location
The course is implemented online (no contact teaching). The student can proceed at his own pace during the course.
Learning materials and recommended literature
Course website (lecture material, exercises, other instructions).
Teaching methods
The course includes assignments from different subject areas of the course.
Student workload
Environment preparations, exercises and familiarisation with the material 108 h. Total 108 h
Evaluation scale
0-5
Evaluation criteria, satisfactory (1-2)
Satisfactory 2: The student masters data retrieval from a selected source. You are able to implement data preprocessing to datasets. You are able to apply simple methods used in data preprocessing to his/her data. You can assess your own solutions for data preprocessing.
Sufficient 1: You know and understand the significance of data and its advantages. You know the significance of data preprocessing and its most common methods. You are able to apply simple methods obtained and used in data preprocessing.
Evaluation criteria, good (3-4)
Very good 4: You master the retrieval of data from various sources. You are able to plan and implement data preprocessing for various datasets. You are able to apply the methods used in data preprocessing widely. You can assess and justify your own solutions for data preprocessing.
Good 3: You master data retrieval from several sources. You are able to plan and implement data preprocessign for datasets. You are able to apply methods used in data preprocessing. You can to assess and justify your own solutions for data preprocessing.
Evaluation criteria, excellent (5)
Excellent 5: You master the retrieval of data from various sources. You are able to plan and implement data processing for various datasets. You are able to apply the methods used in data preprocessing. You can critically assess and justify your own solutions for data preprocessing.
Prerequisites
Basic skills of computer use, basic skills in programming, knowledge of Python programming language.
Enrollment
01.08.2023 - 24.08.2023
Timing
11.09.2023 - 15.10.2023
Number of ECTS credits allocated
4 op
Virtual portion
4 op
Mode of delivery
Online learning
Unit
School of Technology
Teaching languages
- Finnish
Seats
0 - 30
Degree programmes
- Bachelor's Degree Programme in Information and Communications Technology
Teachers
- Antti Häkkinen
Groups
-
TTV21S3Tieto- ja viestintätekniikka (AMK)
-
TTV21S5Tieto- ja viestintätekniikka (AMK)
-
TTV21SMTieto- ja viestintätekniikka (AMK)
-
TTV21S2Tieto- ja viestintätekniikka (AMK)
-
ZJA23STIDA1Avoin amk, Data-analytiikka 1, Verkko
-
TTV21S1Tieto- ja viestintätekniikka (AMK)
Objectives
After the course, you will understand the data analytics process and the challenges it brings. You can identify different data formats, the most common interface solutions and the tools and methods used in data preprocessing. In addition, you know how to apply the methods necessary for data preprocessing.
EUR-ACE Competences:
Knowledge and Understanding
Engineering Practice
Content
o Various data sources and data formats, JSON, APIt, retrieval of tables from SQL
o Types of variables
o Data preprocessing before bringing it into analysis program
o Data preprocessing in Pandas (basics of Pandas/DataFrames)
o Connecting various data sources
Time and location
The course is implemented online (no contact teaching). The student can proceed at his own pace during the course.
Learning materials and recommended literature
Course website (lecture material, exercises, other instructions).
Teaching methods
The course includes assignments from different subject areas of the course.
Student workload
Environment preparations, exercises and familiarisation with the material 108 h. Total 108 h
Evaluation scale
0-5
Evaluation criteria, satisfactory (1-2)
Satisfactory 2: The student masters data retrieval from a selected source. You are able to implement data preprocessing to datasets. You are able to apply simple methods used in data preprocessing to his/her data. You can assess your own solutions for data preprocessing.
Sufficient 1: You know and understand the significance of data and its advantages. You know the significance of data preprocessing and its most common methods. You are able to apply simple methods obtained and used in data preprocessing.
Evaluation criteria, good (3-4)
Very good 4: You master the retrieval of data from various sources. You are able to plan and implement data preprocessing for various datasets. You are able to apply the methods used in data preprocessing widely. You can assess and justify your own solutions for data preprocessing.
Good 3: You master data retrieval from several sources. You are able to plan and implement data preprocessign for datasets. You are able to apply methods used in data preprocessing. You can to assess and justify your own solutions for data preprocessing.
Evaluation criteria, excellent (5)
Excellent 5: You master the retrieval of data from various sources. You are able to plan and implement data processing for various datasets. You are able to apply the methods used in data preprocessing. You can critically assess and justify your own solutions for data preprocessing.
Prerequisites
Basic skills of computer use, basic skills in programming, knowledge of Python programming language.
Timing
13.02.2023 - 26.03.2023
Number of ECTS credits allocated
4 op
Mode of delivery
Face-to-face
Unit
School of Technology
Teaching languages
- Finnish
Degree programmes
- Bachelor's Degree Programme in Information and Communications Technology
Teachers
- Antti Häkkinen
Groups
-
ZJA23KTIDA1Avoin amk, Data-analytiikka 1, Verkko
Objectives
After the course, you will understand the data analytics process and the challenges it brings. You can identify different data formats, the most common interface solutions and the tools and methods used in data preprocessing. In addition, you know how to apply the methods necessary for data preprocessing.
EUR-ACE Competences:
Knowledge and Understanding
Engineering Practice
Content
o Various data sources and data formats, JSON, APIt, retrieval of tables from SQL
o Types of variables
o Data preprocessing before bringing it into analysis program
o Data preprocessing in Pandas (basics of Pandas/DataFrames)
o Connecting various data sources
Time and location
Opintojakso toteutetaan verkkototeutuksena (ei kontaktiopetusta). Opiskelija voi edetä toteutuksella omaan tahtiin.
Learning materials and recommended literature
Opintojakson verkkosivut (luentomateriaali, harjoitukset, harjoitustyöohjeistus).
Teaching methods
Opintojakso sisältää harjoituksia eri aihealueilta sekä opintojakson aihepiirejä yhdistävän harjoitustyön.
Student workload
Harjoitukset 80 h ja harjoitustyö 28 h. Yhteensä 108 h
Evaluation scale
0-5
Evaluation criteria, satisfactory (1-2)
Satisfactory 2: The student masters data retrieval from a selected source. You are able to implement data preprocessing to datasets. You are able to apply simple methods used in data preprocessing to his/her data. You can assess your own solutions for data preprocessing.
Sufficient 1: You know and understand the significance of data and its advantages. You know the significance of data preprocessing and its most common methods. You are able to apply simple methods obtained and used in data preprocessing.
Evaluation criteria, good (3-4)
Very good 4: You master the retrieval of data from various sources. You are able to plan and implement data preprocessing for various datasets. You are able to apply the methods used in data preprocessing widely. You can assess and justify your own solutions for data preprocessing.
Good 3: You master data retrieval from several sources. You are able to plan and implement data preprocessign for datasets. You are able to apply methods used in data preprocessing. You can to assess and justify your own solutions for data preprocessing.
Evaluation criteria, excellent (5)
Excellent 5: You master the retrieval of data from various sources. You are able to plan and implement data processing for various datasets. You are able to apply the methods used in data preprocessing. You can critically assess and justify your own solutions for data preprocessing.
Prerequisites
Basic skills of computer use, basic skills in programming, knowledge of Python programming language.
Enrollment
01.11.2022 - 05.01.2023
Timing
13.02.2023 - 26.03.2023
Number of ECTS credits allocated
4 op
Virtual portion
4 op
Mode of delivery
Online learning
Unit
School of Technology
Campus
Lutakko Campus
Teaching languages
- Finnish
Seats
0 - 30
Degree programmes
- Bachelor's Degree Programme in Information and Communications Technology
Teachers
- Antti Häkkinen
Objectives
After the course, you will understand the data analytics process and the challenges it brings. You can identify different data formats, the most common interface solutions and the tools and methods used in data preprocessing. In addition, you know how to apply the methods necessary for data preprocessing.
EUR-ACE Competences:
Knowledge and Understanding
Engineering Practice
Content
o Various data sources and data formats, JSON, APIt, retrieval of tables from SQL
o Types of variables
o Data preprocessing before bringing it into analysis program
o Data preprocessing in Pandas (basics of Pandas/DataFrames)
o Connecting various data sources
Time and location
Opintojakso toteutetaan verkkototeutuksena (ei kontaktiopetusta). Opiskelija voi edetä toteutuksella omaan tahtiin.
Learning materials and recommended literature
Opintojakson verkkosivut (luentomateriaali, harjoitukset, harjoitustyöohjeistus).
Teaching methods
Opintojakso sisältää harjoituksia eri aihealueilta sekä opintojakson aihepiirejä yhdistävän harjoitustyön.
Student workload
Harjoitukset 80 h ja harjoitustyö 28 h. Yhteensä 108 h
Evaluation scale
0-5
Evaluation criteria, satisfactory (1-2)
Satisfactory 2: The student masters data retrieval from a selected source. You are able to implement data preprocessing to datasets. You are able to apply simple methods used in data preprocessing to his/her data. You can assess your own solutions for data preprocessing.
Sufficient 1: You know and understand the significance of data and its advantages. You know the significance of data preprocessing and its most common methods. You are able to apply simple methods obtained and used in data preprocessing.
Evaluation criteria, good (3-4)
Very good 4: You master the retrieval of data from various sources. You are able to plan and implement data preprocessing for various datasets. You are able to apply the methods used in data preprocessing widely. You can assess and justify your own solutions for data preprocessing.
Good 3: You master data retrieval from several sources. You are able to plan and implement data preprocessign for datasets. You are able to apply methods used in data preprocessing. You can to assess and justify your own solutions for data preprocessing.
Evaluation criteria, excellent (5)
Excellent 5: You master the retrieval of data from various sources. You are able to plan and implement data processing for various datasets. You are able to apply the methods used in data preprocessing. You can critically assess and justify your own solutions for data preprocessing.
Prerequisites
Basic skills of computer use, basic skills in programming, knowledge of Python programming language.
Enrollment
01.08.2022 - 25.08.2022
Timing
03.10.2022 - 13.11.2022
Number of ECTS credits allocated
4 op
Mode of delivery
Face-to-face
Unit
School of Technology
Teaching languages
- Finnish
Degree programmes
- Bachelor's Degree Programme in Information and Communications Technology
Teachers
- Antti Häkkinen
Groups
-
ZJA22STIDA1Avoin amk, Data-analytiikka 1, Verkko
Objectives
After the course, you will understand the data analytics process and the challenges it brings. You can identify different data formats, the most common interface solutions and the tools and methods used in data preprocessing. In addition, you know how to apply the methods necessary for data preprocessing.
EUR-ACE Competences:
Knowledge and Understanding
Engineering Practice
Content
o Various data sources and data formats, JSON, APIt, retrieval of tables from SQL
o Types of variables
o Data preprocessing before bringing it into analysis program
o Data preprocessing in Pandas (basics of Pandas/DataFrames)
o Connecting various data sources
Time and location
Opintojakso toteutetaan verkkototeutuksena (ei kontaktiopetusta). Opiskelija voi edetä toteutuksella omaan tahtiin.
Learning materials and recommended literature
Opintojakson verkkosivut (luentomateriaali, harjoitukset, harjoitustyöohjeistus).
Teaching methods
Opintojakso sisältää harjoituksia eri aihealueilta sekä opintojakson aihepiirejä yhdistävän harjoitustyön.
Student workload
Harjoitukset 80 h ja harjoitustyö 28 h. Yhteensä 108 h
Evaluation scale
0-5
Evaluation criteria, satisfactory (1-2)
Satisfactory 2: The student masters data retrieval from a selected source. You are able to implement data preprocessing to datasets. You are able to apply simple methods used in data preprocessing to his/her data. You can assess your own solutions for data preprocessing.
Sufficient 1: You know and understand the significance of data and its advantages. You know the significance of data preprocessing and its most common methods. You are able to apply simple methods obtained and used in data preprocessing.
Evaluation criteria, good (3-4)
Very good 4: You master the retrieval of data from various sources. You are able to plan and implement data preprocessing for various datasets. You are able to apply the methods used in data preprocessing widely. You can assess and justify your own solutions for data preprocessing.
Good 3: You master data retrieval from several sources. You are able to plan and implement data preprocessign for datasets. You are able to apply methods used in data preprocessing. You can to assess and justify your own solutions for data preprocessing.
Evaluation criteria, excellent (5)
Excellent 5: You master the retrieval of data from various sources. You are able to plan and implement data processing for various datasets. You are able to apply the methods used in data preprocessing. You can critically assess and justify your own solutions for data preprocessing.
Prerequisites
Basic skills of computer use, basic skills in programming, knowledge of Python programming language.
Enrollment
01.08.2022 - 25.08.2022
Timing
03.10.2022 - 13.11.2022
Number of ECTS credits allocated
4 op
Virtual portion
4 op
Mode of delivery
Online learning
Unit
School of Technology
Campus
Lutakko Campus
Teaching languages
- Finnish
Seats
0 - 30
Degree programmes
- Bachelor's Degree Programme in Information and Communications Technology
Teachers
- Antti Häkkinen
Objectives
After the course, you will understand the data analytics process and the challenges it brings. You can identify different data formats, the most common interface solutions and the tools and methods used in data preprocessing. In addition, you know how to apply the methods necessary for data preprocessing.
EUR-ACE Competences:
Knowledge and Understanding
Engineering Practice
Content
o Various data sources and data formats, JSON, APIt, retrieval of tables from SQL
o Types of variables
o Data preprocessing before bringing it into analysis program
o Data preprocessing in Pandas (basics of Pandas/DataFrames)
o Connecting various data sources
Time and location
Opintojakso toteutetaan verkkototeutuksena (ei kontaktiopetusta). Opiskelija voi edetä toteutuksella omaan tahtiin.
Learning materials and recommended literature
Opintojakson verkkosivut (luentomateriaali, harjoitukset, harjoitustyöohjeistus).
Teaching methods
Opintojakso sisältää harjoituksia eri aihealueilta sekä opintojakson aihepiirejä yhdistävän harjoitustyön.
Student workload
Harjoitukset 80 h ja harjoitustyö 28 h. Yhteensä 108 h
Evaluation scale
0-5
Evaluation criteria, satisfactory (1-2)
Satisfactory 2: The student masters data retrieval from a selected source. You are able to implement data preprocessing to datasets. You are able to apply simple methods used in data preprocessing to his/her data. You can assess your own solutions for data preprocessing.
Sufficient 1: You know and understand the significance of data and its advantages. You know the significance of data preprocessing and its most common methods. You are able to apply simple methods obtained and used in data preprocessing.
Evaluation criteria, good (3-4)
Very good 4: You master the retrieval of data from various sources. You are able to plan and implement data preprocessing for various datasets. You are able to apply the methods used in data preprocessing widely. You can assess and justify your own solutions for data preprocessing.
Good 3: You master data retrieval from several sources. You are able to plan and implement data preprocessign for datasets. You are able to apply methods used in data preprocessing. You can to assess and justify your own solutions for data preprocessing.
Evaluation criteria, excellent (5)
Excellent 5: You master the retrieval of data from various sources. You are able to plan and implement data processing for various datasets. You are able to apply the methods used in data preprocessing. You can critically assess and justify your own solutions for data preprocessing.
Prerequisites
Basic skills of computer use, basic skills in programming, knowledge of Python programming language.
Enrollment
16.12.2021 - 09.01.2022
Timing
14.03.2022 - 01.05.2022
Number of ECTS credits allocated
4 op
Virtual portion
4 op
Mode of delivery
Online learning
Unit
School of Technology
Campus
Lutakko Campus
Teaching languages
- Finnish
Seats
0 - 30
Degree programmes
- Bachelor's Degree Programme in Information and Communications Technology
Teachers
- Antti Häkkinen
Groups
-
TTV19SMTieto- ja viestintätekniikka
-
TTV19S1Tieto- ja viestintätekniikka
-
TTV20SMTieto- ja viestintätekniikka
-
TTV19S3Tieto- ja viestintätekniikka
-
TTV19S2Tieto- ja viestintätekniikka
-
TTV19S5Tieto- ja viestintätekniikka
-
ZJA22KTIDA1Avoin AMK, tekniikka, ICT, Data-analytiikka1
Objectives
After the course, you will understand the data analytics process and the challenges it brings. You can identify different data formats, the most common interface solutions and the tools and methods used in data preprocessing. In addition, you know how to apply the methods necessary for data preprocessing.
EUR-ACE Competences:
Knowledge and Understanding
Engineering Practice
Content
o Various data sources and data formats, JSON, APIt, retrieval of tables from SQL
o Types of variables
o Data preprocessing before bringing it into analysis program
o Data preprocessing in Pandas (basics of Pandas/DataFrames)
o Connecting various data sources
Time and location
Opintojakso toteutetaan verkkototeutuksena (ei kontaktiopetusta). Opiskelija voi edetä toteutuksella omaan tahtiin.
Learning materials and recommended literature
Opintojakson verkkosivut (luentomateriaali, harjoitukset, harjoitustyöohjeistus).
Teaching methods
Opintojakso sisältää harjoituksia eri aihealueilta sekä opintojakson aihepiirejä yhdistävän harjoitustyön.
Student workload
Harjoitukset 80 h ja harjoitustyö 28 h. Yhteensä 108 h
Evaluation scale
0-5
Evaluation criteria, satisfactory (1-2)
Satisfactory 2: The student masters data retrieval from a selected source. You are able to implement data preprocessing to datasets. You are able to apply simple methods used in data preprocessing to his/her data. You can assess your own solutions for data preprocessing.
Sufficient 1: You know and understand the significance of data and its advantages. You know the significance of data preprocessing and its most common methods. You are able to apply simple methods obtained and used in data preprocessing.
Evaluation criteria, good (3-4)
Very good 4: You master the retrieval of data from various sources. You are able to plan and implement data preprocessing for various datasets. You are able to apply the methods used in data preprocessing widely. You can assess and justify your own solutions for data preprocessing.
Good 3: You master data retrieval from several sources. You are able to plan and implement data preprocessign for datasets. You are able to apply methods used in data preprocessing. You can to assess and justify your own solutions for data preprocessing.
Evaluation criteria, excellent (5)
Excellent 5: You master the retrieval of data from various sources. You are able to plan and implement data processing for various datasets. You are able to apply the methods used in data preprocessing. You can critically assess and justify your own solutions for data preprocessing.
Prerequisites
Basic skills of computer use, basic skills in programming, knowledge of Python programming language.