Data and RDI as a success factorsLaajuus (15 cr)
Code: AT00CK39
Credits
15 op
Teaching language
- English
Objective
The student is able to
- utilize modern analyzing, ML, and AI tools to solve engineering problems
- visualize and report the processed data in a suitable way using modern tools
- create a data visualization using HTML and backend services
- understand digital twin operation principles
- understand the importance and principles of leading, law, marketing and business economy as a part of a company operations
- write a technical report and represent it
Enrollment
15.05.2023 - 01.09.2023
Timing
01.08.2023 - 31.12.2023
Number of ECTS credits allocated
15 op
Mode of delivery
Contact teaching
Unit
Faculty of Technology (LAB)
Campus
Lappeenranta Campus
Teaching languages
- English
Degree programmes
- Bachelor's Degree Programme in Industrial Information Technology
Teachers
- Karri Miettinen
- Antti Jokinen
- Jyrki Antikainen
Scheduling groups
- Luennot (Size: 0. Open UAS: 0.)
Groups
-
TLPRIIT21S
-
TLPRIIT22S
Small groups
- Lectures
Learning outcomes
The student is able to
- utilize modern analyzing, ML, and AI tools to solve engineering problems
- visualize and report the processed data in a suitable way using modern tools
- create a data visualization using HTML and backend services
- understand digital twin operation principles
- understand the importance and principles of leading, law, marketing and business economy as a part of a company operations
- write a technical report and represent it
Implementation and methods of teaching
Course consists of many individual assignments and some group assignments. Some of the assignments requires attending on the premises.
Timing and attendance
Attending classes is required according to the schedules in the Time-edit.
Learning material and recommended literature
Material is shared in the Moodle platform and some presented during the lessons.
Alternative completion methods
None
Working life cooperation
None
Exam retakes
The course grading is based on assigments given during the classes and some group assignments.
In case there are exams during the course, then the LAB's rules and regulations are applied to the exam (two or more changes to take a replacement exam.)
Learning environment
Lappeenranta Campus & Moodle
Student time use and work load
The scope of the course is 15 credits, which corresponds to an average of 412 hours of studying, containing the scheduled lectures and self-studying.
Contents
Digital Twin
AI/ML tools
Leading Law, Marketing and business economy
Additional information for students: previous knowledge etc.
Object-oriented programming
Assessment criteria
Assessment will be based on exercises, individual and team assignments and possible exams.
Student activity will affect grading.
Assessment scale
1-5
Failed (0)
Student doesn't reach the course's competence goals.
Assessment criteria: level 1 (assessment scale 1–5)
Understands the course topics and can contribute to RDI processes.
Assessment criteria: level 3 (assessment scale 1–5)
Can utilize modern analyzing, ML and AI tools to solve engineering problems. Is able to visualize data using modern web development related tools and understands the importance of the leading, law, marketing and business economy as a part of a company operations.
Assessment criteria: level 5 (assessment scale 1–5)
Rules the level 3 matters and can take leading role in RDI processes