Data Analysis and VisualizationLaajuus (4 cr)
Code: TTC8040
Credits
4 op
Teaching language
- Finnish
Responsible person
- Juha Peltomäki
- Tuomas Huopana
Objective
Purpose:
For the development of modern applications and for their functionality, a vital part is played by the data analysis concerning the data. Applications use data that is to be presented to the end users. For the end user, data as such is not in a presentable format. Hence, analysis methods are needed to support the end user who makes the decisions based on the information content.
EUR-ACE Competences:
Knowledge and Understanding
Engineering Practice
Investigations and information retrieval
Course objectives:
You are able to identify data with the help of its content and metadata. You are able to present the data in a way that is appropriate to the situation. You have analyzed the data based on its definition in such a way that conclusions can be drawn from the results of the analysis. You are able to present the data you are analyzing.
Content
- Quantity and quality of data
- Datan analysis as a part of information processing
- Describing data
- Modifying data
- Data visualization
- Statistics
- Time series
- Correlation
- Linear ja nonlinear regression model
- Modelling periodical data
- Representing the analysed results
Qualifications
Basics in computing, programming, knowledge and know-how of Python programming language.
Assessment criteria, satisfactory (1)
Sufficient (1):
You can identify data based on content and metadata. You know the most common techniques of data analytics used in data analysis tasks. You can apply the most common techniques for your data analysis. You can assess the results of data analysis in a restricted manner. You can present results of the data analyzed by you.
Satisfactory (2):
You are able to take data into use based on its content and metadata. You are familiar with the most commonly used techniques of data analytics in data analysis tasks. You are able to select the techniques for data analysis and apply the technical competence in practice. You are able to assess the data analysis result superficially. You are able to present the essential results of the data analyzed by you.
Assessment criteria, good (3)
Good (3):
You can take essential data into used based on its content and metadata. You know the most commonly used techniques of data analytics in various data analysis tasks. You can justify and select the techniques for data analysis and apply your technical competence in practice. Additionally, you are able to assess the implementation and justify its development. You are able to present the essential results of the data analyzed by you.
Very good (4):
You are able to take the essential data into use based on its content and metadata. You know the most commonly used techniques in data analytics and are able to justify comprehensively the use of the implemented techniques in various data analysis tasks. You are able to justify and select the correct techniques for data analysis. You are able to apply your technical competence to practice. You are able to assess the implementation comprehensively and validate its further development. You are able to present the data analyzed by you extensively.
Assessment criteria, excellent (5)
Excellent (5):
You are able to take the essential data take into use based on its content and metadata taking into account the substance. You know the most commonly used techniques in data analytics and are able to critically justify the use of the implemented techniques in various data analysis tasks. You are able to apply your technical competence to practice. You are able to assess the implementation critically and validate its further development. You are able to present the results of the data analyzed by you comprehensively.
Enrollment
18.11.2024 - 09.01.2025
Timing
10.02.2025 - 28.03.2025
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
- Juha Peltomäki
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
Purpose:
For the development of modern applications and for their functionality, a vital part is played by the data analysis concerning the data. Applications use data that is to be presented to the end users. For the end user, data as such is not in a presentable format. Hence, analysis methods are needed to support the end user who makes the decisions based on the information content.
EUR-ACE Competences:
Knowledge and Understanding
Engineering Practice
Investigations and information retrieval
Course objectives:
You are able to identify data with the help of its content and metadata. You are able to present the data in a way that is appropriate to the situation. You have analyzed the data based on its definition in such a way that conclusions can be drawn from the results of the analysis. You are able to present the data you are analyzing.
Content
- Quantity and quality of data
- Datan analysis as a part of information processing
- Describing data
- Modifying data
- Data visualization
- Statistics
- Time series
- Correlation
- Linear ja nonlinear regression model
- Modelling periodical data
- Representing the analysed results
Time and location
The course will be implemented in the spring semester of 2025.
Learning materials and recommended literature
The material for the assignments and the content to be studied will be shared during the course. The course utilizes the Python 3.10+ environment, Git version control, NumPy and Pandas libraries, visualization libraries and other applicable libraries.
Teaching methods
Virtual study including doing assignments and familiarizing yourself with related lecture and example materials.
Practical training and working life connections
The aim is to connect the content of the course to problems that occur in working life.
Alternative completion methods
The admission procedures are described in the degree rule and the study guide. The teacher of the course will give you more information on possible specific course practices.
Student workload
The workload of one credit corresponds to 27 hours of study. The total amount of study work (4 ECTS) in the course is 108 hours.
Further information for students
The course is evaluated based on the returned assignments. Assignments must be returned within the given timescales.
Evaluation scale
0-5
Evaluation criteria, satisfactory (1-2)
Sufficient (1):
You can identify data based on content and metadata. You know the most common techniques of data analytics used in data analysis tasks. You can apply the most common techniques for your data analysis. You can assess the results of data analysis in a restricted manner. You can present results of the data analyzed by you.
Satisfactory (2):
You are able to take data into use based on its content and metadata. You are familiar with the most commonly used techniques of data analytics in data analysis tasks. You are able to select the techniques for data analysis and apply the technical competence in practice. You are able to assess the data analysis result superficially. You are able to present the essential results of the data analyzed by you.
Evaluation criteria, good (3-4)
Good (3):
You can take essential data into used based on its content and metadata. You know the most commonly used techniques of data analytics in various data analysis tasks. You can justify and select the techniques for data analysis and apply your technical competence in practice. Additionally, you are able to assess the implementation and justify its development. You are able to present the essential results of the data analyzed by you.
Very good (4):
You are able to take the essential data into use based on its content and metadata. You know the most commonly used techniques in data analytics and are able to justify comprehensively the use of the implemented techniques in various data analysis tasks. You are able to justify and select the correct techniques for data analysis. You are able to apply your technical competence to practice. You are able to assess the implementation comprehensively and validate its further development. You are able to present the data analyzed by you extensively.
Evaluation criteria, excellent (5)
Excellent (5):
You are able to take the essential data take into use based on its content and metadata taking into account the substance. You know the most commonly used techniques in data analytics and are able to critically justify the use of the implemented techniques in various data analysis tasks. You are able to apply your technical competence to practice. You are able to assess the implementation critically and validate its further development. You are able to present the results of the data analyzed by you comprehensively.
Prerequisites
Basics in computing, programming, knowledge and know-how of Python programming language.
Enrollment
01.08.2024 - 22.08.2024
Timing
30.09.2024 - 10.11.2024
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
- Juha Peltomäki
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
Purpose:
For the development of modern applications and for their functionality, a vital part is played by the data analysis concerning the data. Applications use data that is to be presented to the end users. For the end user, data as such is not in a presentable format. Hence, analysis methods are needed to support the end user who makes the decisions based on the information content.
EUR-ACE Competences:
Knowledge and Understanding
Engineering Practice
Investigations and information retrieval
Course objectives:
You are able to identify data with the help of its content and metadata. You are able to present the data in a way that is appropriate to the situation. You have analyzed the data based on its definition in such a way that conclusions can be drawn from the results of the analysis. You are able to present the data you are analyzing.
Content
- Quantity and quality of data
- Datan analysis as a part of information processing
- Describing data
- Modifying data
- Data visualization
- Statistics
- Time series
- Correlation
- Linear ja nonlinear regression model
- Modelling periodical data
- Representing the analysed results
Time and location
The course will be implemented in the fall semester of 2024.
Learning materials and recommended literature
The material for the assignments and the content to be studied will be shared during the course. The course utilizes the Python 3.11+ environment, Git version control, NumPy and Pandas libraries, visualization libraries and other applicable libraries.
Teaching methods
Virtual study including doing assignments and familiarizing yourself with related lecture and example materials.
Practical training and working life connections
The aim is to connect the content of the course to problems that occur in working life.
Alternative completion methods
The admission procedures are described in the degree rule and the study guide. The teacher of the course will give you more information on possible specific course practices.
Student workload
The workload of one credit corresponds to 27 hours of study. The total amount of study work (4 ECTS) in the course is 108 hours.
Further information for students
The course is evaluated based on the returned assignments. Assignments must be returned within the given timescales.
Evaluation scale
0-5
Evaluation criteria, satisfactory (1-2)
Sufficient (1):
You can identify data based on content and metadata. You know the most common techniques of data analytics used in data analysis tasks. You can apply the most common techniques for your data analysis. You can assess the results of data analysis in a restricted manner. You can present results of the data analyzed by you.
Satisfactory (2):
You are able to take data into use based on its content and metadata. You are familiar with the most commonly used techniques of data analytics in data analysis tasks. You are able to select the techniques for data analysis and apply the technical competence in practice. You are able to assess the data analysis result superficially. You are able to present the essential results of the data analyzed by you.
Evaluation criteria, good (3-4)
Good (3):
You can take essential data into used based on its content and metadata. You know the most commonly used techniques of data analytics in various data analysis tasks. You can justify and select the techniques for data analysis and apply your technical competence in practice. Additionally, you are able to assess the implementation and justify its development. You are able to present the essential results of the data analyzed by you.
Very good (4):
You are able to take the essential data into use based on its content and metadata. You know the most commonly used techniques in data analytics and are able to justify comprehensively the use of the implemented techniques in various data analysis tasks. You are able to justify and select the correct techniques for data analysis. You are able to apply your technical competence to practice. You are able to assess the implementation comprehensively and validate its further development. You are able to present the data analyzed by you extensively.
Evaluation criteria, excellent (5)
Excellent (5):
You are able to take the essential data take into use based on its content and metadata taking into account the substance. You know the most commonly used techniques in data analytics and are able to critically justify the use of the implemented techniques in various data analysis tasks. You are able to apply your technical competence to practice. You are able to assess the implementation critically and validate its further development. You are able to present the results of the data analyzed by you comprehensively.
Prerequisites
Basics in computing, programming, knowledge and know-how of Python programming language.
Enrollment
20.11.2023 - 04.01.2024
Timing
12.02.2024 - 30.03.2024
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
- Juha Peltomäki
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
Purpose:
For the development of modern applications and for their functionality, a vital part is played by the data analysis concerning the data. Applications use data that is to be presented to the end users. For the end user, data as such is not in a presentable format. Hence, analysis methods are needed to support the end user who makes the decisions based on the information content.
EUR-ACE Competences:
Knowledge and Understanding
Engineering Practice
Investigations and information retrieval
Course objectives:
You are able to identify data with the help of its content and metadata. You are able to present the data in a way that is appropriate to the situation. You have analyzed the data based on its definition in such a way that conclusions can be drawn from the results of the analysis. You are able to present the data you are analyzing.
Content
- Quantity and quality of data
- Datan analysis as a part of information processing
- Describing data
- Modifying data
- Data visualization
- Statistics
- Time series
- Correlation
- Linear ja nonlinear regression model
- Modelling periodical data
- Representing the analysed results
Time and location
The course will be implemented in the spring semester of 2024.
Learning materials and recommended literature
The material for the assignments and the content to be studied will be shared during the course. The course utilizes the Python 3.9+ environment, Git version control, NumPy and Pandas libraries, visualization libraries and other applicable libraries.
Teaching methods
Virtual study including doing assignments and familiarizing yourself with related lecture and example materials.
Practical training and working life connections
The aim is to connect the content of the course to problems that occur in working life.
Alternative completion methods
The admission procedures are described in the degree rule and the study guide. The teacher of the course will give you more information on possible specific course practices.
Student workload
The workload of one credit corresponds to 27 hours of study. The total amount of study work (4 ECTS) in the course is 108 hours.
Further information for students
The course is evaluated based on the returned assignments. Assignments must be returned within the given timescales.
Evaluation scale
0-5
Evaluation criteria, satisfactory (1-2)
Sufficient (1):
You can identify data based on content and metadata. You know the most common techniques of data analytics used in data analysis tasks. You can apply the most common techniques for your data analysis. You can assess the results of data analysis in a restricted manner. You can present results of the data analyzed by you.
Satisfactory (2):
You are able to take data into use based on its content and metadata. You are familiar with the most commonly used techniques of data analytics in data analysis tasks. You are able to select the techniques for data analysis and apply the technical competence in practice. You are able to assess the data analysis result superficially. You are able to present the essential results of the data analyzed by you.
Evaluation criteria, good (3-4)
Good (3):
You can take essential data into used based on its content and metadata. You know the most commonly used techniques of data analytics in various data analysis tasks. You can justify and select the techniques for data analysis and apply your technical competence in practice. Additionally, you are able to assess the implementation and justify its development. You are able to present the essential results of the data analyzed by you.
Very good (4):
You are able to take the essential data into use based on its content and metadata. You know the most commonly used techniques in data analytics and are able to justify comprehensively the use of the implemented techniques in various data analysis tasks. You are able to justify and select the correct techniques for data analysis. You are able to apply your technical competence to practice. You are able to assess the implementation comprehensively and validate its further development. You are able to present the data analyzed by you extensively.
Evaluation criteria, excellent (5)
Excellent (5):
You are able to take the essential data take into use based on its content and metadata taking into account the substance. You know the most commonly used techniques in data analytics and are able to critically justify the use of the implemented techniques in various data analysis tasks. You are able to apply your technical competence to practice. You are able to assess the implementation critically and validate its further development. You are able to present the results of the data analyzed by you comprehensively.
Prerequisites
Basics in computing, programming, knowledge and know-how of Python programming language.
Enrollment
01.08.2023 - 24.08.2023
Timing
02.10.2023 - 12.11.2023
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
- Juha Peltomäki
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
Purpose:
For the development of modern applications and for their functionality, a vital part is played by the data analysis concerning the data. Applications use data that is to be presented to the end users. For the end user, data as such is not in a presentable format. Hence, analysis methods are needed to support the end user who makes the decisions based on the information content.
EUR-ACE Competences:
Knowledge and Understanding
Engineering Practice
Investigations and information retrieval
Course objectives:
You are able to identify data with the help of its content and metadata. You are able to present the data in a way that is appropriate to the situation. You have analyzed the data based on its definition in such a way that conclusions can be drawn from the results of the analysis. You are able to present the data you are analyzing.
Content
- Quantity and quality of data
- Datan analysis as a part of information processing
- Describing data
- Modifying data
- Data visualization
- Statistics
- Time series
- Correlation
- Linear ja nonlinear regression model
- Modelling periodical data
- Representing the analysed results
Time and location
The course will be implemented in the fall semester of 2023.
Learning materials and recommended literature
The material for the assignments and the content to be studied will be shared during the course. The course utilizes the Python 3.9+ environment, Git version control, NumPy and Pandas libraries, visualization libraries and other applicable libraries.
Teaching methods
Virtual study including doing assignments and familiarizing yourself with related lecture and example materials.
Practical training and working life connections
The aim is to connect the content of the course to problems that occur in working life.
Alternative completion methods
The admission procedures are described in the degree rule and the study guide. The teacher of the course will give you more information on possible specific course practices.
Student workload
The workload of one credit corresponds to 27 hours of study. The total amount of study work (4 ECTS) in the course is 108 hours.
Further information for students
The course is evaluated based on the returned assignments. Assignments must be returned within the given timescales.
Evaluation scale
0-5
Evaluation criteria, satisfactory (1-2)
Sufficient (1):
You can identify data based on content and metadata. You know the most common techniques of data analytics used in data analysis tasks. You can apply the most common techniques for your data analysis. You can assess the results of data analysis in a restricted manner. You can present results of the data analyzed by you.
Satisfactory (2):
You are able to take data into use based on its content and metadata. You are familiar with the most commonly used techniques of data analytics in data analysis tasks. You are able to select the techniques for data analysis and apply the technical competence in practice. You are able to assess the data analysis result superficially. You are able to present the essential results of the data analyzed by you.
Evaluation criteria, good (3-4)
Good (3):
You can take essential data into used based on its content and metadata. You know the most commonly used techniques of data analytics in various data analysis tasks. You can justify and select the techniques for data analysis and apply your technical competence in practice. Additionally, you are able to assess the implementation and justify its development. You are able to present the essential results of the data analyzed by you.
Very good (4):
You are able to take the essential data into use based on its content and metadata. You know the most commonly used techniques in data analytics and are able to justify comprehensively the use of the implemented techniques in various data analysis tasks. You are able to justify and select the correct techniques for data analysis. You are able to apply your technical competence to practice. You are able to assess the implementation comprehensively and validate its further development. You are able to present the data analyzed by you extensively.
Evaluation criteria, excellent (5)
Excellent (5):
You are able to take the essential data take into use based on its content and metadata taking into account the substance. You know the most commonly used techniques in data analytics and are able to critically justify the use of the implemented techniques in various data analysis tasks. You are able to apply your technical competence to practice. You are able to assess the implementation critically and validate its further development. You are able to present the results of the data analyzed by you comprehensively.
Prerequisites
Basics in computing, programming, knowledge and know-how of Python programming language.
Timing
20.03.2023 - 28.04.2023
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
- Juha Peltomäki
Groups
-
ZJA23KTIDA1Avoin amk, Data-analytiikka 1, Verkko
Objectives
Purpose:
For the development of modern applications and for their functionality, a vital part is played by the data analysis concerning the data. Applications use data that is to be presented to the end users. For the end user, data as such is not in a presentable format. Hence, analysis methods are needed to support the end user who makes the decisions based on the information content.
EUR-ACE Competences:
Knowledge and Understanding
Engineering Practice
Investigations and information retrieval
Course objectives:
You are able to identify data with the help of its content and metadata. You are able to present the data in a way that is appropriate to the situation. You have analyzed the data based on its definition in such a way that conclusions can be drawn from the results of the analysis. You are able to present the data you are analyzing.
Content
- Quantity and quality of data
- Datan analysis as a part of information processing
- Describing data
- Modifying data
- Data visualization
- Statistics
- Time series
- Correlation
- Linear ja nonlinear regression model
- Modelling periodical data
- Representing the analysed results
Time and location
Opintojakso toteutetaan kevätlukukaudella 2023.
Learning materials and recommended literature
Materiaali harjoitustehtäviä ja opiskeltavia asiasisältöjä varten jaetaan kurssin aikana. Opintojaksolla hyödynnetään Python 3.9+-ympäristöä, Git-versiohallintaa, NumPy- ja Pandas-kirjastoja, visualisointikirjastoja sekä muita soveltuvia kirjastoja.
Harjoitustehtävien tekemisessä hyödynnetään myös Anaconda-ympäristöä sekä Jupyter Notebook -formaattia.
Teaching methods
Virtuaalinen opiskelu sisältäen harjoitustehtävien tekemisen sekä niihin liittyviin luento- ja esimerkkimateriaaleihin perehtymisen.
Practical training and working life connections
Opintojakson sisältö pyritään kytkemään työelämässä esiintyviin ongelmiin.
Exam dates and retake possibilities
Opintojakso arvioidaan palautettujen harjoitustehtävien avulla. Palautukset tulee suorittaa annettujen aikataulujen puitteissa.
Alternative completion methods
Hyväksilukemisen menettelytavat kuvataan tutkintosäännössä ja opinto-oppaassa. Opintojakson opettaja antaa lisätietoa mahdollisista opintojakson erityiskäytänteistä.
Student workload
Yhden opintopisteen työmäärä vastaa 27 tunnin opiskelutyötä. Yhteensä opiskelutyömäärä (4 op.) kurssilla on 108 tuntia.
Evaluation scale
0-5
Evaluation criteria, satisfactory (1-2)
Sufficient (1):
You can identify data based on content and metadata. You know the most common techniques of data analytics used in data analysis tasks. You can apply the most common techniques for your data analysis. You can assess the results of data analysis in a restricted manner. You can present results of the data analyzed by you.
Satisfactory (2):
You are able to take data into use based on its content and metadata. You are familiar with the most commonly used techniques of data analytics in data analysis tasks. You are able to select the techniques for data analysis and apply the technical competence in practice. You are able to assess the data analysis result superficially. You are able to present the essential results of the data analyzed by you.
Evaluation criteria, good (3-4)
Good (3):
You can take essential data into used based on its content and metadata. You know the most commonly used techniques of data analytics in various data analysis tasks. You can justify and select the techniques for data analysis and apply your technical competence in practice. Additionally, you are able to assess the implementation and justify its development. You are able to present the essential results of the data analyzed by you.
Very good (4):
You are able to take the essential data into use based on its content and metadata. You know the most commonly used techniques in data analytics and are able to justify comprehensively the use of the implemented techniques in various data analysis tasks. You are able to justify and select the correct techniques for data analysis. You are able to apply your technical competence to practice. You are able to assess the implementation comprehensively and validate its further development. You are able to present the data analyzed by you extensively.
Evaluation criteria, excellent (5)
Excellent (5):
You are able to take the essential data take into use based on its content and metadata taking into account the substance. You know the most commonly used techniques in data analytics and are able to critically justify the use of the implemented techniques in various data analysis tasks. You are able to apply your technical competence to practice. You are able to assess the implementation critically and validate its further development. You are able to present the results of the data analyzed by you comprehensively.
Prerequisites
Basics in computing, programming, knowledge and know-how of Python programming language.
Enrollment
01.11.2022 - 05.01.2023
Timing
20.03.2023 - 28.04.2023
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
- Juha Peltomäki
Objectives
Purpose:
For the development of modern applications and for their functionality, a vital part is played by the data analysis concerning the data. Applications use data that is to be presented to the end users. For the end user, data as such is not in a presentable format. Hence, analysis methods are needed to support the end user who makes the decisions based on the information content.
EUR-ACE Competences:
Knowledge and Understanding
Engineering Practice
Investigations and information retrieval
Course objectives:
You are able to identify data with the help of its content and metadata. You are able to present the data in a way that is appropriate to the situation. You have analyzed the data based on its definition in such a way that conclusions can be drawn from the results of the analysis. You are able to present the data you are analyzing.
Content
- Quantity and quality of data
- Datan analysis as a part of information processing
- Describing data
- Modifying data
- Data visualization
- Statistics
- Time series
- Correlation
- Linear ja nonlinear regression model
- Modelling periodical data
- Representing the analysed results
Time and location
Opintojakso toteutetaan kevätlukukaudella 2023.
Learning materials and recommended literature
Materiaali harjoitustehtäviä ja opiskeltavia asiasisältöjä varten jaetaan kurssin aikana. Opintojaksolla hyödynnetään Python 3.9+-ympäristöä, Git-versiohallintaa, NumPy- ja Pandas-kirjastoja, visualisointikirjastoja sekä muita soveltuvia kirjastoja.
Harjoitustehtävien tekemisessä hyödynnetään myös Anaconda-ympäristöä sekä Jupyter Notebook -formaattia.
Teaching methods
Virtuaalinen opiskelu sisältäen harjoitustehtävien tekemisen sekä niihin liittyviin luento- ja esimerkkimateriaaleihin perehtymisen.
Practical training and working life connections
Opintojakson sisältö pyritään kytkemään työelämässä esiintyviin ongelmiin.
Exam dates and retake possibilities
Opintojakso arvioidaan palautettujen harjoitustehtävien avulla. Palautukset tulee suorittaa annettujen aikataulujen puitteissa.
Alternative completion methods
Hyväksilukemisen menettelytavat kuvataan tutkintosäännössä ja opinto-oppaassa. Opintojakson opettaja antaa lisätietoa mahdollisista opintojakson erityiskäytänteistä.
Student workload
Yhden opintopisteen työmäärä vastaa 27 tunnin opiskelutyötä. Yhteensä opiskelutyömäärä (4 op.) kurssilla on 108 tuntia.
Evaluation scale
0-5
Evaluation criteria, satisfactory (1-2)
Sufficient (1):
You can identify data based on content and metadata. You know the most common techniques of data analytics used in data analysis tasks. You can apply the most common techniques for your data analysis. You can assess the results of data analysis in a restricted manner. You can present results of the data analyzed by you.
Satisfactory (2):
You are able to take data into use based on its content and metadata. You are familiar with the most commonly used techniques of data analytics in data analysis tasks. You are able to select the techniques for data analysis and apply the technical competence in practice. You are able to assess the data analysis result superficially. You are able to present the essential results of the data analyzed by you.
Evaluation criteria, good (3-4)
Good (3):
You can take essential data into used based on its content and metadata. You know the most commonly used techniques of data analytics in various data analysis tasks. You can justify and select the techniques for data analysis and apply your technical competence in practice. Additionally, you are able to assess the implementation and justify its development. You are able to present the essential results of the data analyzed by you.
Very good (4):
You are able to take the essential data into use based on its content and metadata. You know the most commonly used techniques in data analytics and are able to justify comprehensively the use of the implemented techniques in various data analysis tasks. You are able to justify and select the correct techniques for data analysis. You are able to apply your technical competence to practice. You are able to assess the implementation comprehensively and validate its further development. You are able to present the data analyzed by you extensively.
Evaluation criteria, excellent (5)
Excellent (5):
You are able to take the essential data take into use based on its content and metadata taking into account the substance. You know the most commonly used techniques in data analytics and are able to critically justify the use of the implemented techniques in various data analysis tasks. You are able to apply your technical competence to practice. You are able to assess the implementation critically and validate its further development. You are able to present the results of the data analyzed by you comprehensively.
Prerequisites
Basics in computing, programming, knowledge and know-how of Python programming language.
Timing
07.11.2022 - 20.12.2022
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
- Juha Peltomäki
Groups
-
ZJA22STIDA1Avoin amk, Data-analytiikka 1, Verkko
Objectives
Purpose:
For the development of modern applications and for their functionality, a vital part is played by the data analysis concerning the data. Applications use data that is to be presented to the end users. For the end user, data as such is not in a presentable format. Hence, analysis methods are needed to support the end user who makes the decisions based on the information content.
EUR-ACE Competences:
Knowledge and Understanding
Engineering Practice
Investigations and information retrieval
Course objectives:
You are able to identify data with the help of its content and metadata. You are able to present the data in a way that is appropriate to the situation. You have analyzed the data based on its definition in such a way that conclusions can be drawn from the results of the analysis. You are able to present the data you are analyzing.
Content
- Quantity and quality of data
- Datan analysis as a part of information processing
- Describing data
- Modifying data
- Data visualization
- Statistics
- Time series
- Correlation
- Linear ja nonlinear regression model
- Modelling periodical data
- Representing the analysed results
Time and location
Opintojakso toteutetaan syyslukukaudella 2022.
Learning materials and recommended literature
Materiaali harjoitustehtäviä ja opiskeltavia asiasisältöjä varten jaetaan kurssin aikana. Opintojaksolla hyödynnetään Python 3.8+-ympäristöä, Git-versiohallintaa, NumPy- ja Pandas-kirjastoja, visualisointikirjastoja sekä muita soveltuvia kirjastoja.
Harjoitustehtävien tekemisessä hyödynnetään myös Anaconda-ympäristöä sekä Jupyter Notebook -formaattia.
Teaching methods
Virtuaalinen opiskelu sisältäen harjoitustehtävien tekemisen sekä niihin liittyviin luento- ja esimerkkimateriaaleihin perehtymisen.
Practical training and working life connections
Opintojakson sisältö pyritään kytkemään työelämässä esiintyviin ongelmiin.
Exam dates and retake possibilities
Opintojakso arvioidaan palautettujen harjoitustehtävien avulla. Palautukset tulee suorittaa annettujen aikataulujen puitteissa.
Alternative completion methods
Hyväksilukemisen menettelytavat kuvataan tutkintosäännössä ja opinto-oppaassa. Opintojakson opettaja antaa lisätietoa mahdollisista opintojakson erityiskäytänteistä.
Student workload
Yhden opintopisteen työmäärä vastaa 27 tunnin opiskelutyötä. Yhteensä opiskelutyömäärä (4 op.) kurssilla on 108 tuntia.
Evaluation scale
0-5
Evaluation criteria, satisfactory (1-2)
Sufficient (1):
You can identify data based on content and metadata. You know the most common techniques of data analytics used in data analysis tasks. You can apply the most common techniques for your data analysis. You can assess the results of data analysis in a restricted manner. You can present results of the data analyzed by you.
Satisfactory (2):
You are able to take data into use based on its content and metadata. You are familiar with the most commonly used techniques of data analytics in data analysis tasks. You are able to select the techniques for data analysis and apply the technical competence in practice. You are able to assess the data analysis result superficially. You are able to present the essential results of the data analyzed by you.
Evaluation criteria, good (3-4)
Good (3):
You can take essential data into used based on its content and metadata. You know the most commonly used techniques of data analytics in various data analysis tasks. You can justify and select the techniques for data analysis and apply your technical competence in practice. Additionally, you are able to assess the implementation and justify its development. You are able to present the essential results of the data analyzed by you.
Very good (4):
You are able to take the essential data into use based on its content and metadata. You know the most commonly used techniques in data analytics and are able to justify comprehensively the use of the implemented techniques in various data analysis tasks. You are able to justify and select the correct techniques for data analysis. You are able to apply your technical competence to practice. You are able to assess the implementation comprehensively and validate its further development. You are able to present the data analyzed by you extensively.
Evaluation criteria, excellent (5)
Excellent (5):
You are able to take the essential data take into use based on its content and metadata taking into account the substance. You know the most commonly used techniques in data analytics and are able to critically justify the use of the implemented techniques in various data analysis tasks. You are able to apply your technical competence to practice. You are able to assess the implementation critically and validate its further development. You are able to present the results of the data analyzed by you comprehensively.
Prerequisites
Basics in computing, programming, knowledge and know-how of Python programming language.
Enrollment
01.08.2022 - 25.08.2022
Timing
07.11.2022 - 20.12.2022
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
- Juha Peltomäki
Objectives
Purpose:
For the development of modern applications and for their functionality, a vital part is played by the data analysis concerning the data. Applications use data that is to be presented to the end users. For the end user, data as such is not in a presentable format. Hence, analysis methods are needed to support the end user who makes the decisions based on the information content.
EUR-ACE Competences:
Knowledge and Understanding
Engineering Practice
Investigations and information retrieval
Course objectives:
You are able to identify data with the help of its content and metadata. You are able to present the data in a way that is appropriate to the situation. You have analyzed the data based on its definition in such a way that conclusions can be drawn from the results of the analysis. You are able to present the data you are analyzing.
Content
- Quantity and quality of data
- Datan analysis as a part of information processing
- Describing data
- Modifying data
- Data visualization
- Statistics
- Time series
- Correlation
- Linear ja nonlinear regression model
- Modelling periodical data
- Representing the analysed results
Time and location
Opintojakso toteutetaan syyslukukaudella 2022.
Learning materials and recommended literature
Materiaali harjoitustehtäviä ja opiskeltavia asiasisältöjä varten jaetaan kurssin aikana. Opintojaksolla hyödynnetään Python 3.8+-ympäristöä, Git-versiohallintaa, NumPy- ja Pandas-kirjastoja, visualisointikirjastoja sekä muita soveltuvia kirjastoja.
Harjoitustehtävien tekemisessä hyödynnetään myös Anaconda-ympäristöä sekä Jupyter Notebook -formaattia.
Teaching methods
Virtuaalinen opiskelu sisältäen harjoitustehtävien tekemisen sekä niihin liittyviin luento- ja esimerkkimateriaaleihin perehtymisen.
Practical training and working life connections
Opintojakson sisältö pyritään kytkemään työelämässä esiintyviin ongelmiin.
Exam dates and retake possibilities
Opintojakso arvioidaan palautettujen harjoitustehtävien avulla. Palautukset tulee suorittaa annettujen aikataulujen puitteissa.
Alternative completion methods
Hyväksilukemisen menettelytavat kuvataan tutkintosäännössä ja opinto-oppaassa. Opintojakson opettaja antaa lisätietoa mahdollisista opintojakson erityiskäytänteistä.
Student workload
Yhden opintopisteen työmäärä vastaa 27 tunnin opiskelutyötä. Yhteensä opiskelutyömäärä (4 op.) kurssilla on 108 tuntia.
Evaluation scale
0-5
Evaluation criteria, satisfactory (1-2)
Sufficient (1):
You can identify data based on content and metadata. You know the most common techniques of data analytics used in data analysis tasks. You can apply the most common techniques for your data analysis. You can assess the results of data analysis in a restricted manner. You can present results of the data analyzed by you.
Satisfactory (2):
You are able to take data into use based on its content and metadata. You are familiar with the most commonly used techniques of data analytics in data analysis tasks. You are able to select the techniques for data analysis and apply the technical competence in practice. You are able to assess the data analysis result superficially. You are able to present the essential results of the data analyzed by you.
Evaluation criteria, good (3-4)
Good (3):
You can take essential data into used based on its content and metadata. You know the most commonly used techniques of data analytics in various data analysis tasks. You can justify and select the techniques for data analysis and apply your technical competence in practice. Additionally, you are able to assess the implementation and justify its development. You are able to present the essential results of the data analyzed by you.
Very good (4):
You are able to take the essential data into use based on its content and metadata. You know the most commonly used techniques in data analytics and are able to justify comprehensively the use of the implemented techniques in various data analysis tasks. You are able to justify and select the correct techniques for data analysis. You are able to apply your technical competence to practice. You are able to assess the implementation comprehensively and validate its further development. You are able to present the data analyzed by you extensively.
Evaluation criteria, excellent (5)
Excellent (5):
You are able to take the essential data take into use based on its content and metadata taking into account the substance. You know the most commonly used techniques in data analytics and are able to critically justify the use of the implemented techniques in various data analysis tasks. You are able to apply your technical competence to practice. You are able to assess the implementation critically and validate its further development. You are able to present the results of the data analyzed by you comprehensively.
Prerequisites
Basics in computing, programming, knowledge and know-how of Python programming language.
Timing
29.08.2022 - 02.10.2022
Number of ECTS credits allocated
4 op
Virtual portion
4 op
Mode of delivery
Online learning
Unit
School of Technology
Teaching languages
- Finnish
Degree programmes
- Bachelor's Degree Programme in Information and Communications Technology
Teachers
- Juha Peltomäki
Groups
-
ZJA22STIDA2Avoin amk, Data-analytiikka 2, Verkko
Objectives
Purpose:
For the development of modern applications and for their functionality, a vital part is played by the data analysis concerning the data. Applications use data that is to be presented to the end users. For the end user, data as such is not in a presentable format. Hence, analysis methods are needed to support the end user who makes the decisions based on the information content.
EUR-ACE Competences:
Knowledge and Understanding
Engineering Practice
Investigations and information retrieval
Course objectives:
You are able to identify data with the help of its content and metadata. You are able to present the data in a way that is appropriate to the situation. You have analyzed the data based on its definition in such a way that conclusions can be drawn from the results of the analysis. You are able to present the data you are analyzing.
Content
- Quantity and quality of data
- Datan analysis as a part of information processing
- Describing data
- Modifying data
- Data visualization
- Statistics
- Time series
- Correlation
- Linear ja nonlinear regression model
- Modelling periodical data
- Representing the analysed results
Time and location
Opintojakso toteutetaan syyslukukaudella 2022.
Learning materials and recommended literature
Materiaali harjoitustehtäviä ja opiskeltavia asiasisältöjä varten jaetaan kurssin aikana. Opintojaksolla hyödynnetään Python 3.8+-ympäristöä, Git-versiohallintaa, NumPy- ja Pandas-kirjastoja, visualisointikirjastoja sekä muita soveltuvia kirjastoja.
Harjoitustehtävien tekemisessä hyödynnetään myös Anaconda-ympäristöä sekä Jupyter Notebook -formaattia.
Teaching methods
Virtuaalinen opiskelu sisältäen harjoitustehtävien tekemisen sekä niihin liittyviin luento- ja esimerkkimateriaaleihin perehtymisen.
Practical training and working life connections
Opintojakson sisältö pyritään kytkemään työelämässä esiintyviin ongelmiin.
Exam dates and retake possibilities
Opintojakso arvioidaan palautettujen harjoitustehtävien avulla. Palautukset tulee suorittaa annettujen aikataulujen puitteissa.
Alternative completion methods
Hyväksilukemisen menettelytavat kuvataan tutkintosäännössä ja opinto-oppaassa. Opintojakson opettaja antaa lisätietoa mahdollisista opintojakson erityiskäytänteistä.
Student workload
Yhden opintopisteen työmäärä vastaa 27 tunnin opiskelutyötä. Yhteensä opiskelutyömäärä (4 op.) kurssilla on 108 tuntia.
Evaluation scale
0-5
Evaluation criteria, satisfactory (1-2)
Sufficient (1):
You can identify data based on content and metadata. You know the most common techniques of data analytics used in data analysis tasks. You can apply the most common techniques for your data analysis. You can assess the results of data analysis in a restricted manner. You can present results of the data analyzed by you.
Satisfactory (2):
You are able to take data into use based on its content and metadata. You are familiar with the most commonly used techniques of data analytics in data analysis tasks. You are able to select the techniques for data analysis and apply the technical competence in practice. You are able to assess the data analysis result superficially. You are able to present the essential results of the data analyzed by you.
Evaluation criteria, good (3-4)
Good (3):
You can take essential data into used based on its content and metadata. You know the most commonly used techniques of data analytics in various data analysis tasks. You can justify and select the techniques for data analysis and apply your technical competence in practice. Additionally, you are able to assess the implementation and justify its development. You are able to present the essential results of the data analyzed by you.
Very good (4):
You are able to take the essential data into use based on its content and metadata. You know the most commonly used techniques in data analytics and are able to justify comprehensively the use of the implemented techniques in various data analysis tasks. You are able to justify and select the correct techniques for data analysis. You are able to apply your technical competence to practice. You are able to assess the implementation comprehensively and validate its further development. You are able to present the data analyzed by you extensively.
Evaluation criteria, excellent (5)
Excellent (5):
You are able to take the essential data take into use based on its content and metadata taking into account the substance. You know the most commonly used techniques in data analytics and are able to critically justify the use of the implemented techniques in various data analysis tasks. You are able to apply your technical competence to practice. You are able to assess the implementation critically and validate its further development. You are able to present the results of the data analyzed by you comprehensively.
Prerequisites
Basics in computing, programming, knowledge and know-how of Python programming language.
Enrollment
01.08.2022 - 25.08.2022
Timing
29.08.2022 - 02.10.2022
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
- Juha Peltomäki
Objectives
Purpose:
For the development of modern applications and for their functionality, a vital part is played by the data analysis concerning the data. Applications use data that is to be presented to the end users. For the end user, data as such is not in a presentable format. Hence, analysis methods are needed to support the end user who makes the decisions based on the information content.
EUR-ACE Competences:
Knowledge and Understanding
Engineering Practice
Investigations and information retrieval
Course objectives:
You are able to identify data with the help of its content and metadata. You are able to present the data in a way that is appropriate to the situation. You have analyzed the data based on its definition in such a way that conclusions can be drawn from the results of the analysis. You are able to present the data you are analyzing.
Content
- Quantity and quality of data
- Datan analysis as a part of information processing
- Describing data
- Modifying data
- Data visualization
- Statistics
- Time series
- Correlation
- Linear ja nonlinear regression model
- Modelling periodical data
- Representing the analysed results
Time and location
Opintojakso toteutetaan syyslukukaudella 2022.
Learning materials and recommended literature
Materiaali harjoitustehtäviä ja opiskeltavia asiasisältöjä varten jaetaan kurssin aikana. Opintojaksolla hyödynnetään Python 3.8+-ympäristöä, Git-versiohallintaa, NumPy- ja Pandas-kirjastoja, visualisointikirjastoja sekä muita soveltuvia kirjastoja.
Harjoitustehtävien tekemisessä hyödynnetään myös Anaconda-ympäristöä sekä Jupyter Notebook -formaattia.
Teaching methods
Virtuaalinen opiskelu sisältäen harjoitustehtävien tekemisen sekä niihin liittyviin luento- ja esimerkkimateriaaleihin perehtymisen.
Practical training and working life connections
Opintojakson sisältö pyritään kytkemään työelämässä esiintyviin ongelmiin.
Exam dates and retake possibilities
Opintojakso arvioidaan palautettujen harjoitustehtävien avulla. Palautukset tulee suorittaa annettujen aikataulujen puitteissa.
Alternative completion methods
Hyväksilukemisen menettelytavat kuvataan tutkintosäännössä ja opinto-oppaassa. Opintojakson opettaja antaa lisätietoa mahdollisista opintojakson erityiskäytänteistä.
Student workload
Yhden opintopisteen työmäärä vastaa 27 tunnin opiskelutyötä. Yhteensä opiskelutyömäärä (4 op.) kurssilla on 108 tuntia.
Evaluation scale
0-5
Evaluation criteria, satisfactory (1-2)
Sufficient (1):
You can identify data based on content and metadata. You know the most common techniques of data analytics used in data analysis tasks. You can apply the most common techniques for your data analysis. You can assess the results of data analysis in a restricted manner. You can present results of the data analyzed by you.
Satisfactory (2):
You are able to take data into use based on its content and metadata. You are familiar with the most commonly used techniques of data analytics in data analysis tasks. You are able to select the techniques for data analysis and apply the technical competence in practice. You are able to assess the data analysis result superficially. You are able to present the essential results of the data analyzed by you.
Evaluation criteria, good (3-4)
Good (3):
You can take essential data into used based on its content and metadata. You know the most commonly used techniques of data analytics in various data analysis tasks. You can justify and select the techniques for data analysis and apply your technical competence in practice. Additionally, you are able to assess the implementation and justify its development. You are able to present the essential results of the data analyzed by you.
Very good (4):
You are able to take the essential data into use based on its content and metadata. You know the most commonly used techniques in data analytics and are able to justify comprehensively the use of the implemented techniques in various data analysis tasks. You are able to justify and select the correct techniques for data analysis. You are able to apply your technical competence to practice. You are able to assess the implementation comprehensively and validate its further development. You are able to present the data analyzed by you extensively.
Evaluation criteria, excellent (5)
Excellent (5):
You are able to take the essential data take into use based on its content and metadata taking into account the substance. You know the most commonly used techniques in data analytics and are able to critically justify the use of the implemented techniques in various data analysis tasks. You are able to apply your technical competence to practice. You are able to assess the implementation critically and validate its further development. You are able to present the results of the data analyzed by you comprehensively.
Prerequisites
Basics in computing, programming, knowledge and know-how of Python programming language.
Enrollment
01.11.2021 - 09.01.2022
Timing
03.01.2022 - 28.02.2022
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
- Juha Peltomäki
Groups
-
ZJA21STIDAAvoin AMK, tekniikka, ICT, Data-analytiikka
-
TTV19SMTieto- ja viestintätekniikka
-
TTV19S1Tieto- ja viestintätekniikka
-
TTV20SMTieto- ja viestintätekniikka
-
TTV19S3Tieto- ja viestintätekniikka
-
TTV19S2Tieto- ja viestintätekniikka
-
TTV19S5Tieto- ja viestintätekniikka
Objectives
Purpose:
For the development of modern applications and for their functionality, a vital part is played by the data analysis concerning the data. Applications use data that is to be presented to the end users. For the end user, data as such is not in a presentable format. Hence, analysis methods are needed to support the end user who makes the decisions based on the information content.
EUR-ACE Competences:
Knowledge and Understanding
Engineering Practice
Investigations and information retrieval
Course objectives:
You are able to identify data with the help of its content and metadata. You are able to present the data in a way that is appropriate to the situation. You have analyzed the data based on its definition in such a way that conclusions can be drawn from the results of the analysis. You are able to present the data you are analyzing.
Content
- Quantity and quality of data
- Datan analysis as a part of information processing
- Describing data
- Modifying data
- Data visualization
- Statistics
- Time series
- Correlation
- Linear ja nonlinear regression model
- Modelling periodical data
- Representing the analysed results
Time and location
Opintojakso toteutetaan 1.3.2022 - 28.2.2022 välisenä aikana.
Learning materials and recommended literature
Harjoitustöiden tekemisessä hyödynnetään Anaconda ohjelmistoa (Python 3.7-versio tai uudempi): https://www.anaconda.com/download/
Teaching methods
Virtuaalinen opiskelu sisältäen harjoitustehtävien tekemisen sekä niihin liittyvään sisältöön perehtymisen.
Practical training and working life connections
Kurssin sisältö pyritään kytkemään työelämässä esiintyviin ongelmiin.
Alternative completion methods
Hyväksilukemisen menettelytavat kuvataan tutkintosäännössä ja opinto-oppaassa. Opintojakson opettaja antaa lisätietoa mahdollisista opintojakson erityiskäytänteistä.
Student workload
Yhden opintopisteen työmäärä vastaa 27 tunnin opiskelutyötä. Yhteensä opiskelutyömäärä (4 op.) kurssilla on 108 tuntia.
Further information for students
Kurssi arvioidaan harjoitustehtävien perusteella. Harjoitustehtäviä voi palauttaa vain kurssitoteutuksen aikana.
Evaluation scale
0-5
Evaluation criteria, satisfactory (1-2)
Sufficient (1):
You can identify data based on content and metadata. You know the most common techniques of data analytics used in data analysis tasks. You can apply the most common techniques for your data analysis. You can assess the results of data analysis in a restricted manner. You can present results of the data analyzed by you.
Satisfactory (2):
You are able to take data into use based on its content and metadata. You are familiar with the most commonly used techniques of data analytics in data analysis tasks. You are able to select the techniques for data analysis and apply the technical competence in practice. You are able to assess the data analysis result superficially. You are able to present the essential results of the data analyzed by you.
Evaluation criteria, good (3-4)
Good (3):
You can take essential data into used based on its content and metadata. You know the most commonly used techniques of data analytics in various data analysis tasks. You can justify and select the techniques for data analysis and apply your technical competence in practice. Additionally, you are able to assess the implementation and justify its development. You are able to present the essential results of the data analyzed by you.
Very good (4):
You are able to take the essential data into use based on its content and metadata. You know the most commonly used techniques in data analytics and are able to justify comprehensively the use of the implemented techniques in various data analysis tasks. You are able to justify and select the correct techniques for data analysis. You are able to apply your technical competence to practice. You are able to assess the implementation comprehensively and validate its further development. You are able to present the data analyzed by you extensively.
Evaluation criteria, excellent (5)
Excellent (5):
You are able to take the essential data take into use based on its content and metadata taking into account the substance. You know the most commonly used techniques in data analytics and are able to critically justify the use of the implemented techniques in various data analysis tasks. You are able to apply your technical competence to practice. You are able to assess the implementation critically and validate its further development. You are able to present the results of the data analyzed by you comprehensively.
Prerequisites
Basics in computing, programming, knowledge and know-how of Python programming language.