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AI / DA -Project (5 cr)

Code: TTC8070-3008

General information


Enrollment
18.11.2024 - 09.01.2025
Registration for the implementation has ended.
Timing
10.03.2025 - 30.04.2025
Implementation is running.
Number of ECTS credits allocated
5 cr
Local portion
0 cr
Virtual portion
5 cr
Mode of delivery
Distance learning
Unit
School of Technology
Teaching languages
English
Seats
0 - 70
Degree programmes
Bachelor's Degree Programme in Information and Communications Technology
Bachelor's Degree Programme in Information and Communications Technology
Teachers
Juha Peltomäki
Groups
TTV22S5
Tieto- ja viestintätekniikka (AMK)
TTV22S2
Tieto- ja viestintätekniikka (AMK)
TTV22S3
Tieto- ja viestintätekniikka (AMK)
TIC22S1
Bachelor's Degree Programme in Information and Communications Technology
TTV22S1
Tieto- ja viestintätekniikka (AMK)
TTV22SM
Tieto- ja viestintätekniikka (AMK)
TTV22S4
Tieto- ja viestintätekniikka (AMK)
TTV22SM2
Tieto- ja viestintätekniikka (AMK)
ZJA25KTIDA2
Avoin amk, Data-analytiikka 2, Verkko
Course
TTC8070

Materials

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

Evaluation scale

0-5

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.

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.

Employer connections

The aim is to connect the content of the course to problems that occur in working life.

Virtual portion

5

Student workload

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

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.

Teaching language

en

Teaching methods

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

Location and time

Online implementation (group work and online guidance sessions)

Number of ECTS credits allocated

5

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.

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

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

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