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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
  • TTV22S5
    Tieto- ja viestintätekniikka (AMK)
  • TTV22S2
    Tieto- ja viestintätekniikka (AMK)
  • TTV22S3
    Tieto- ja viestintätekniikka (AMK)
  • TIC22S1
    Bachelor's Degree Programme in Information and Communications Technology
  • TTV22S1
    Tieto- ja viestintätekniikka (AMK)
  • TTV22SM
    Tieto- ja viestintätekniikka (AMK)
  • TTV22S4
    Tieto- ja viestintätekniikka (AMK)
  • TTV22SM2
    Tieto- ja viestintätekniikka (AMK)
  • ZJA25KTIDA1
    Avoin 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
  • TTV22S5
    Tieto- ja viestintätekniikka (AMK)
  • TTV22S2
    Tieto- ja viestintätekniikka (AMK)
  • TTV22S3
    Tieto- ja viestintätekniikka (AMK)
  • TTV22S1
    Tieto- ja viestintätekniikka (AMK)
  • TTV22SM
    Tieto- ja viestintätekniikka (AMK)
  • TTV22S4
    Tieto- ja viestintätekniikka (AMK)
  • TTV22SM2
    Tieto- ja viestintätekniikka (AMK)
  • ZJA24STIDA1
    Avoin 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
  • TTV21S3
    Tieto- ja viestintätekniikka (AMK)
  • TTV21S5
    Tieto- ja viestintätekniikka (AMK)
  • TTV21SM
    Tieto- ja viestintätekniikka (AMK)
  • TIC21S1
    Bachelor's Degree Programme in Information and Communications Technology
  • TTV21S2
    Tieto- ja viestintätekniikka (AMK)
  • ZJA24KTIDA1
    Avoin amk, Data-analytiikka 1, Verkko
  • TTV21S1
    Tieto- 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
  • TTV21S3
    Tieto- ja viestintätekniikka (AMK)
  • TTV21S5
    Tieto- ja viestintätekniikka (AMK)
  • TTV21SM
    Tieto- ja viestintätekniikka (AMK)
  • TTV21S2
    Tieto- ja viestintätekniikka (AMK)
  • ZJA23STIDA1
    Avoin amk, Data-analytiikka 1, Verkko
  • TTV21S1
    Tieto- 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
  • ZJA23KTIDA1
    Avoin 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
  • ZJA22STIDA1
    Avoin 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
  • TTV19SM
    Tieto- ja viestintätekniikka
  • TTV19S1
    Tieto- ja viestintätekniikka
  • TTV20SM
    Tieto- ja viestintätekniikka
  • TTV19S3
    Tieto- ja viestintätekniikka
  • TTV19S2
    Tieto- ja viestintätekniikka
  • TTV19S5
    Tieto- ja viestintätekniikka
  • ZJA22KTIDA1
    Avoin 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.