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Data and RDI as a success factors (15 cr)

Code: AT00CK39-3001

General information


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