Curricula

Objectives

You understand and master the various phases of Data Analytics and Machine learning project. You are able to select the applicable methods for the problem to be solved and apply them to the problem to be solved. You are able to interpret the obtained results and draw conclusions based on them.

EUR-ACE Competences:
Knowledge and Understanding
Communication and team-working
Engineering Practice

Content

Analysis of pre-selected data in Python programming environment, includes all stages of data analysis and machine learning project:
- Data preprocessing
- Data description and descriptors
- Selection of a suitable predictive model and its implementation (at least two alternative models)
- Assessment of the accuracy of the predictive models
- Analysis of results

Time and location

Online implementation (group work and online guidance sessions)

Learning materials and recommended literature

The material of other courses in the module of data analytics and artificial intelligence can be applied in this project implementation.

Teaching methods

The students implement the project as a group work. Guidance is organized virtually during the study period.

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 number of credits (5 ECTS) corresponds to 135 hours of student work (project guidance sessions, group work in the project).

Further information for students

The phases of the project are evaluated for the whole group.
In the course, the areas of the projects are evaluated according to the given schedule.

Evaluation scale

0-5

Evaluation criteria, satisfactory (1-2)

Satisfactory 2: You know the various phases of a data analytics and machine learning project. You are able to select the most common techniques for the problem to be solved and are able to apply your technical know-how to practice. In addition, you are able to assess your implementation and validate the conclusions.

Sufficient 1: You know the various phases of a data analytics and machine learning project. You know the most common techniques and are able to apply them to practice. Additionally, you are able to assess briefly your implementation and validate the conclusions.

Evaluation criteria, good (3-4)

Very good 4: You know the various phases of a data analytics and machine learning project and are able to proceed systematically step by step. You are able to select the correct techniques regardless of the problem to be solved and are able to apply your technical know-how to practice. In addition, you are able to assess your implementation and validate the conclusions.

Good 3: You know the various phases of a data analytics and machine learning project and are able to proceed step by step. You are able to select the most common techniques for the problem to be solved and are able to apply your technical know-how to practice. Additionally, you are able to assess your implementation and validate the conclusions.

Evaluation criteria, excellent (5)

Excellent 5: You know the various phases of a data analytics and machine learning project and are able to proceed systematically step by step. You are able to select the correct techniques regardless of the problem to be solved and are able to apply your technical know-how to practice. Additionally, you are able to critically assess your implementation and validate the conclusions.

Prerequisites

Basics in computing and programming, knowledge and know-how of Python programming language.

Additionally, courses in Computational algorithms, Data analytics and Machine Learning Practice, Data Preprocessing, Data Analysis and Visualization, Machine Learning and Deep Learning.

Objectives

You understand and master the various phases of Data Analytics and Machine learning project. You are able to select the applicable methods for the problem to be solved and apply them to the problem to be solved. You are able to interpret the obtained results and draw conclusions based on them.

EUR-ACE Competences:
Knowledge and Understanding
Communication and team-working
Engineering Practice

Content

Analysis of pre-selected data in Python programming environment, includes all stages of data analysis and machine learning project:
- Data preprocessing
- Data description and descriptors
- Selection of a suitable predictive model and its implementation (at least two alternative models)
- Assessment of the accuracy of the predictive models
- Analysis of results

Time and location

Online implementation (group work and online guidance sessions)

Learning materials and recommended literature

The material of other courses in the module of data analytics and artificial intelligence can be applied in this project implementation.

Teaching methods

The students implement the project as a group work. Guidance is organized online during the study period.

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 number of credits (5 ECTS) corresponds to 135 hours of student work (project guidance sessions, group work in the project).

Further information for students

The phases of the project are evaluated for the whole group.
In the course, the areas of the projects are evaluated according to the given schedule.

Evaluation scale

0-5

Evaluation criteria, satisfactory (1-2)

Satisfactory 2: You know the various phases of a data analytics and machine learning project. You are able to select the most common techniques for the problem to be solved and are able to apply your technical know-how to practice. In addition, you are able to assess your implementation and validate the conclusions.

Sufficient 1: You know the various phases of a data analytics and machine learning project. You know the most common techniques and are able to apply them to practice. Additionally, you are able to assess briefly your implementation and validate the conclusions.

Evaluation criteria, good (3-4)

Very good 4: You know the various phases of a data analytics and machine learning project and are able to proceed systematically step by step. You are able to select the correct techniques regardless of the problem to be solved and are able to apply your technical know-how to practice. In addition, you are able to assess your implementation and validate the conclusions.

Good 3: You know the various phases of a data analytics and machine learning project and are able to proceed step by step. You are able to select the most common techniques for the problem to be solved and are able to apply your technical know-how to practice. Additionally, you are able to assess your implementation and validate the conclusions.

Evaluation criteria, excellent (5)

Excellent 5: You know the various phases of a data analytics and machine learning project and are able to proceed systematically step by step. You are able to select the correct techniques regardless of the problem to be solved and are able to apply your technical know-how to practice. Additionally, you are able to critically assess your implementation and validate the conclusions.

Prerequisites

Basics in computing and programming, knowledge and know-how of Python programming language.

Additionally, courses in Computational algorithms, Data analytics and Machine Learning Practice, Data Preprocessing, Data Analysis and Visualization, Machine Learning and Deep Learning.

Objectives

You understand and master the various phases of Data Analytics and Machine learning project. You are able to select the applicable methods for the problem to be solved and apply them to the problem to be solved. You are able to interpret the obtained results and draw conclusions based on them.

EUR-ACE Competences:
Knowledge and Understanding
Communication and team-working
Engineering Practice

Content

Analysis of pre-selected data in Python programming environment, includes all stages of data analysis and machine learning project:
- Data preprocessing
- Data description and descriptors
- Selection of a suitable predictive model and its implementation (at least two alternative models)
- Assessment of the accuracy of the predictive models
- Analysis of results

Time and location

Online implementation (group work and online guidance sessions)

Learning materials and recommended literature

The material of other courses in the module of data analytics and artificial intelligence can be applied in this project implementation.

Teaching methods

The students implement the project as a group work. Guidance is organized online during the study period.

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 number of credits (5 ECTS) corresponds to 135 hours of student work (project guidance sessions, group work in the project).

Further information for students

The phases of the project are evaluated for the whole group.
In the course, the areas of the projects are evaluated according to the given schedule.

Evaluation scale

0-5

Evaluation criteria, satisfactory (1-2)

Satisfactory 2: You know the various phases of a data analytics and machine learning project. You are able to select the most common techniques for the problem to be solved and are able to apply your technical know-how to practice. In addition, you are able to assess your implementation and validate the conclusions.

Sufficient 1: You know the various phases of a data analytics and machine learning project. You know the most common techniques and are able to apply them to practice. Additionally, you are able to assess briefly your implementation and validate the conclusions.

Evaluation criteria, good (3-4)

Very good 4: You know the various phases of a data analytics and machine learning project and are able to proceed systematically step by step. You are able to select the correct techniques regardless of the problem to be solved and are able to apply your technical know-how to practice. In addition, you are able to assess your implementation and validate the conclusions.

Good 3: You know the various phases of a data analytics and machine learning project and are able to proceed step by step. You are able to select the most common techniques for the problem to be solved and are able to apply your technical know-how to practice. Additionally, you are able to assess your implementation and validate the conclusions.

Evaluation criteria, excellent (5)

Excellent 5: You know the various phases of a data analytics and machine learning project and are able to proceed systematically step by step. You are able to select the correct techniques regardless of the problem to be solved and are able to apply your technical know-how to practice. Additionally, you are able to critically assess your implementation and validate the conclusions.

Prerequisites

Basics in computing and programming, knowledge and know-how of Python programming language.

Additionally, courses in Computational algorithms, Data analytics and Machine Learning Practice, Data Preprocessing, Data Analysis and Visualization, Machine Learning and Deep Learning.

Objectives

You understand and master the various phases of Data Analytics and Machine learning project. You are able to select the applicable methods for the problem to be solved and apply them to the problem to be solved. You are able to interpret the obtained results and draw conclusions based on them.

EUR-ACE Competences:
Knowledge and Understanding
Communication and team-working
Engineering Practice

Content

Analysis of pre-selected data in Python programming environment, includes all stages of data analysis and machine learning project:
- Data preprocessing
- Data description and descriptors
- Selection of a suitable predictive model and its implementation (at least two alternative models)
- Assessment of the accuracy of the predictive models
- Analysis of results

Time and location

Verkkototeutus (ryhmätyöskentely ja ohjaus verkossa)

Learning materials and recommended literature

Data-analytiikan ja tekoälyn erikoistumismoduulin muiden opintojaksojen materiaali on sovellettavissa tässä projektitoteutuksessa.

Teaching methods

Opiskelijat toteuttavat projektin ryhmätyönä. Ohjausta järjestetään opintojakson aikana verkossa.

Practical training and working life connections

Opintojakson sisältö pyritään kytkemään työelämässä esiintyviin ongelmiin.

Student workload

Opintopistemäärää vastaava tuntimäärä 135 tuntia (projektin ohjaustilaisuudet, ryhmätyöskentely projektissa)

Further information for students

Projektin osa-alueet arvioidaan koko ryhmän osalta.
Opintojaksossa arvioidaan projektien osa-alueet annetun aikataulun mukaisesti.

Evaluation scale

0-5

Evaluation criteria, satisfactory (1-2)

Satisfactory 2: You know the various phases of a data analytics and machine learning project. You are able to select the most common techniques for the problem to be solved and are able to apply your technical know-how to practice. In addition, you are able to assess your implementation and validate the conclusions.

Sufficient 1: You know the various phases of a data analytics and machine learning project. You know the most common techniques and are able to apply them to practice. Additionally, you are able to assess briefly your implementation and validate the conclusions.

Evaluation criteria, good (3-4)

Very good 4: You know the various phases of a data analytics and machine learning project and are able to proceed systematically step by step. You are able to select the correct techniques regardless of the problem to be solved and are able to apply your technical know-how to practice. In addition, you are able to assess your implementation and validate the conclusions.

Good 3: You know the various phases of a data analytics and machine learning project and are able to proceed step by step. You are able to select the most common techniques for the problem to be solved and are able to apply your technical know-how to practice. Additionally, you are able to assess your implementation and validate the conclusions.

Evaluation criteria, excellent (5)

Excellent 5: You know the various phases of a data analytics and machine learning project and are able to proceed systematically step by step. You are able to select the correct techniques regardless of the problem to be solved and are able to apply your technical know-how to practice. Additionally, you are able to critically assess your implementation and validate the conclusions.

Prerequisites

Basics in computing and programming, knowledge and know-how of Python programming language.

Additionally, courses in Computational algorithms, Data analytics and Machine Learning Practice, Data Preprocessing, Data Analysis and Visualization, Machine Learning and Deep Learning.

Objectives

You understand and master the various phases of Data Analytics and Machine learning project. You are able to select the applicable methods for the problem to be solved and apply them to the problem to be solved. You are able to interpret the obtained results and draw conclusions based on them.

EUR-ACE Competences:
Knowledge and Understanding
Communication and team-working
Engineering Practice

Content

Analysis of pre-selected data in Python programming environment, includes all stages of data analysis and machine learning project:
- Data preprocessing
- Data description and descriptors
- Selection of a suitable predictive model and its implementation (at least two alternative models)
- Assessment of the accuracy of the predictive models
- Analysis of results

Time and location

Verkkototeutus (ryhmätyöskentely ja ohjaus verkossa)

Learning materials and recommended literature

Data-analytiikan ja tekoälyn erikoistumismoduulin muiden opintojaksojen materiaali on sovellettavissa tässä projektitoteutuksessa.

Teaching methods

Opiskelijat toteuttavat projektin ryhmätyönä. Ohjausta järjestetään opintojakson aikana verkossa.

Practical training and working life connections

Opintojakson sisältö pyritään kytkemään työelämässä esiintyviin ongelmiin.

Student workload

Opintopistemäärää vastaava tuntimäärä 135 tuntia (projektin ohjaustilaisuudet, ryhmätyöskentely projektissa)

Further information for students

Projektin osa-alueet arvioidaan koko ryhmän osalta.
Opintojaksossa arvioidaan projektien osa-alueet annetun aikataulun mukaisesti.

Evaluation scale

0-5

Evaluation criteria, satisfactory (1-2)

Satisfactory 2: You know the various phases of a data analytics and machine learning project. You are able to select the most common techniques for the problem to be solved and are able to apply your technical know-how to practice. In addition, you are able to assess your implementation and validate the conclusions.

Sufficient 1: You know the various phases of a data analytics and machine learning project. You know the most common techniques and are able to apply them to practice. Additionally, you are able to assess briefly your implementation and validate the conclusions.

Evaluation criteria, good (3-4)

Very good 4: You know the various phases of a data analytics and machine learning project and are able to proceed systematically step by step. You are able to select the correct techniques regardless of the problem to be solved and are able to apply your technical know-how to practice. In addition, you are able to assess your implementation and validate the conclusions.

Good 3: You know the various phases of a data analytics and machine learning project and are able to proceed step by step. You are able to select the most common techniques for the problem to be solved and are able to apply your technical know-how to practice. Additionally, you are able to assess your implementation and validate the conclusions.

Evaluation criteria, excellent (5)

Excellent 5: You know the various phases of a data analytics and machine learning project and are able to proceed systematically step by step. You are able to select the correct techniques regardless of the problem to be solved and are able to apply your technical know-how to practice. Additionally, you are able to critically assess your implementation and validate the conclusions.

Prerequisites

Basics in computing and programming, knowledge and know-how of Python programming language.

Additionally, courses in Computational algorithms, Data analytics and Machine Learning Practice, Data Preprocessing, Data Analysis and Visualization, Machine Learning and Deep Learning.

Objectives

You understand and master the various phases of Data Analytics and Machine learning project. You are able to select the applicable methods for the problem to be solved and apply them to the problem to be solved. You are able to interpret the obtained results and draw conclusions based on them.

EUR-ACE Competences:
Knowledge and Understanding
Communication and team-working
Engineering Practice

Content

Analysis of pre-selected data in Python programming environment, includes all stages of data analysis and machine learning project:
- Data preprocessing
- Data description and descriptors
- Selection of a suitable predictive model and its implementation (at least two alternative models)
- Assessment of the accuracy of the predictive models
- Analysis of results

Time and location

Verkkototeutus (ryhmätyöskentely ja ohjaus verkossa)

Learning materials and recommended literature

Data-analytiikan ja tekoälyn erikoistumismoduulin muiden opintojaksojen materiaali on sovellettavissa tässä projektitoteutuksessa.

Teaching methods

Opiskelijat toteuttavat projektin ryhmätyönä. Ohjausta järjestetään opintojakson aikana verkossa.

Practical training and working life connections

Opintojakson sisältö pyritään kytkemään työelämässä esiintyviin ongelmiin.

Student workload

Opintopistemäärää vastaava tuntimäärä 135 tuntia (projektin ohjaustilaisuudet, ryhmätyöskentely projektissa)

Further information for students

Projektin osa-alueet arvioidaan koko ryhmän osalta.
Opintojaksossa arvioidaan projektien osa-alueet annetun aikataulun mukaisesti.

Evaluation scale

0-5

Evaluation criteria, satisfactory (1-2)

Satisfactory 2: You know the various phases of a data analytics and machine learning project. You are able to select the most common techniques for the problem to be solved and are able to apply your technical know-how to practice. In addition, you are able to assess your implementation and validate the conclusions.

Sufficient 1: You know the various phases of a data analytics and machine learning project. You know the most common techniques and are able to apply them to practice. Additionally, you are able to assess briefly your implementation and validate the conclusions.

Evaluation criteria, good (3-4)

Very good 4: You know the various phases of a data analytics and machine learning project and are able to proceed systematically step by step. You are able to select the correct techniques regardless of the problem to be solved and are able to apply your technical know-how to practice. In addition, you are able to assess your implementation and validate the conclusions.

Good 3: You know the various phases of a data analytics and machine learning project and are able to proceed step by step. You are able to select the most common techniques for the problem to be solved and are able to apply your technical know-how to practice. Additionally, you are able to assess your implementation and validate the conclusions.

Evaluation criteria, excellent (5)

Excellent 5: You know the various phases of a data analytics and machine learning project and are able to proceed systematically step by step. You are able to select the correct techniques regardless of the problem to be solved and are able to apply your technical know-how to practice. Additionally, you are able to critically assess your implementation and validate the conclusions.

Prerequisites

Basics in computing and programming, knowledge and know-how of Python programming language.

Additionally, courses in Computational algorithms, Data analytics and Machine Learning Practice, Data Preprocessing, Data Analysis and Visualization, Machine Learning and Deep Learning.