AI / DA -ProjectLaajuus (5 cr)
Code: TTC8070
Credits
5 op
Teaching language
- Finnish
Responsible person
- Antti Häkkinen
- Juha Peltomäki
Objective
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
Qualifications
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.
Assessment criteria, satisfactory (1)
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.
Assessment criteria, good (3)
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.
Assessment 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.
Enrollment
01.08.2023 - 24.08.2023
Timing
09.10.2023 - 18.12.2023
Number of ECTS credits allocated
5 op
Virtual portion
5 op
Mode of delivery
Distance 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
- Antti Häkkinen
Groups
-
TTV21S3Tieto- ja viestintätekniikka (AMK)
-
TTV21S5Tieto- ja viestintätekniikka (AMK)
-
TTV21SMTieto- ja viestintätekniikka (AMK)
-
TTV21S2Tieto- ja viestintätekniikka (AMK)
-
TTV21S1Tieto- ja viestintätekniikka (AMK)
-
ZJA23STIDA2Avoin amk, Data-analytiikka 2, Verkko
Objective
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
Location and time
Online implementation (group work and online guidance sessions)
Materials
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.
Employer connections
The aim is to connect the content of the course to problems that occur in working life.
Completion alternatives
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
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
Assessment criteria, satisfactory (1)
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.
Assessment criteria, good (3)
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.
Assessment 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.
Qualifications
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.
Timing
09.01.2023 - 28.04.2023
Number of ECTS credits allocated
5 op
Mode of delivery
Contact teaching
Unit
School of Technology
Campus
Lutakko Campus
Teaching languages
- Finnish
Degree programmes
- Bachelor's Degree Programme in Information and Communications Technology
Teachers
- Juha Peltomäki
Groups
-
ZJA23KTIDA2Avoin amk, Data-analytiikka 2, Verkko
Objective
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
Location and time
Verkkototeutus (ryhmätyöskentely ja ohjaus verkossa)
Materials
Data-analytiikan ja tekoälyn erikoistumismoduulin muiden opintojaksojen materiaali on sovellettavissa tässä projektitoteutuksessa.
Teaching methods
Opiskelijat toteuttavat projektin ryhmätyönä. Ohjausta järjestetään opintojakson aikana verkossa.
Employer connections
Opintojakson sisältö pyritään kytkemään työelämässä esiintyviin ongelmiin.
Student workload
Opintopistemäärää vastaava tuntimäärä 135 tuntia (projektin ohjaustilaisuudet, ryhmätyöskentely projektissa)
Further information
Projektin osa-alueet arvioidaan koko ryhmän osalta.
Opintojaksossa arvioidaan projektien osa-alueet annetun aikataulun mukaisesti.
Evaluation scale
0-5
Assessment criteria, satisfactory (1)
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.
Assessment criteria, good (3)
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.
Assessment 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.
Qualifications
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.
Enrollment
01.11.2022 - 05.01.2023
Timing
09.01.2023 - 28.04.2023
Number of ECTS credits allocated
5 op
Virtual portion
5 op
Mode of delivery
Distance 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
Objective
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
Location and time
Verkkototeutus (ryhmätyöskentely ja ohjaus verkossa)
Materials
Data-analytiikan ja tekoälyn erikoistumismoduulin muiden opintojaksojen materiaali on sovellettavissa tässä projektitoteutuksessa.
Teaching methods
Opiskelijat toteuttavat projektin ryhmätyönä. Ohjausta järjestetään opintojakson aikana verkossa.
Employer connections
Opintojakson sisältö pyritään kytkemään työelämässä esiintyviin ongelmiin.
Student workload
Opintopistemäärää vastaava tuntimäärä 135 tuntia (projektin ohjaustilaisuudet, ryhmätyöskentely projektissa)
Further information
Projektin osa-alueet arvioidaan koko ryhmän osalta.
Opintojaksossa arvioidaan projektien osa-alueet annetun aikataulun mukaisesti.
Evaluation scale
0-5
Assessment criteria, satisfactory (1)
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.
Assessment criteria, good (3)
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.
Assessment 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.
Qualifications
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.
Enrollment
01.08.2022 - 25.08.2022
Timing
29.08.2022 - 16.12.2022
Number of ECTS credits allocated
5 op
Virtual portion
5 op
Mode of delivery
Distance 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
- Antti Häkkinen
Objective
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
Location and time
Verkkototeutus (ryhmätyöskentely ja ohjaus verkossa)
Materials
Data-analytiikan ja tekoälyn erikoistumismoduulin muiden opintojaksojen materiaali on sovellettavissa tässä projektitoteutuksessa.
Teaching methods
Opiskelijat toteuttavat projektin ryhmätyönä. Ohjausta järjestetään opintojakson aikana verkossa.
Employer connections
Opintojakson sisältö pyritään kytkemään työelämässä esiintyviin ongelmiin.
Student workload
Opintopistemäärää vastaava tuntimäärä 135 tuntia (projektin ohjaustilaisuudet, ryhmätyöskentely projektissa)
Further information
Projektin osa-alueet arvioidaan koko ryhmän osalta.
Opintojaksossa arvioidaan projektien osa-alueet annetun aikataulun mukaisesti.
Evaluation scale
0-5
Assessment criteria, satisfactory (1)
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.
Assessment criteria, good (3)
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.
Assessment 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.
Qualifications
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.