Study Guide

Learning outcomes

The student is able to
- create a program using object-oriented programming language with databases (in the cloud too)
- understand the principles of user interfaces
- understand operating systems principles
- understand the role of the cloud platform in an IoT pipeline
- explain the structure of the IoT data pipeline, the meaning of the parts of the pipeline and principles of the machine learning and artificial intelligence
- understand the requirements of sensor data in the data value chain.
- understand the basic principles of the secure data transfer from the IoT device to the IoT data pipeline

Implementation and methods of teaching

Lectures and classes will be held at the Lappeenranta campus

Timing and attendance

Course is held during the second semester.

Lectures will be held according to the timetable.

Attending on the lectures is highly desirable.

Learning material and recommended literature

Material provided during the lectures and in the course's Moodle page.

Learning environment

Study material and instructions in the Moodle platform.

Lectures on the Lappeenranta campus

Student time use and work load

Course (15 ECTs) is divided into three sub modules.
Each submodule requires approximately 135h studying, which totals to 405 h.

The actual workload depends on the student. The required course assignments must be completed.

Estimated time division:
- 100h lectures
- 150h self studying
- 150h exercises

Contents

Topics:
- Object-oriented programming
- Operating system
- IoT Pipeline
- Cloud platform
- ML, AI
- Data to applications

Additional information for students: previous knowledge etc.

Programming basics with Python

Assessment criteria

Grade scale: 0-5

Course evaluation consists of the weekly tasks and possibly exams.

Assessment scale

1-5

Failed (0)

Not able to reach level 1 criterias.

Assessment criteria: level 1 (assessment scale 1–5)

The student and the group have been able to cooperate productively and document their work. The student and the group are able to present their output to other students as instructed. The output mainly meets the criteria of the assignments.

Assessment criteria: level 3 (assessment scale 1–5)

Student and group presentations are clear and logical. They clearly show the implementation in accordance with the tasks given and any deviations have been reported. The student's and group's presentations reflect an understanding of the work, e.g. the terms and professional words are correct. The student and group members also talk about what they have learned and solutions to challenges (reflection).

Assessment criteria: level 5 (assessment scale 1–5)

The student's and group's presentations also contain a link to the theory of the course, and you can compare and justify with facts the solutions that have been used in the work. The student and the group have been able to add something extra they came up with to the work. The presentations are illustrative and things are explained with the help of examples

Learning outcomes

The student is able to
- create a program using object-oriented programming language with databases (in the cloud too)
- understand the principles of user interfaces
- understand operating systems principles
- understand the role of the cloud platform in an IoT pipeline
- explain the structure of the IoT data pipeline, the meaning of the parts of the pipeline and principles of the machine learning and artificial intelligence
- understand the requirements of sensor data in the data value chain.
- understand the basic principles of the secure data transfer from the IoT device to the IoT data pipeline

Implementation and methods of teaching

Course consists of 3 modules:

Assessment scale

1-5

Learning outcomes

The student is able to
- create a program using object-oriented programming language with databases (in the cloud too)
- understand the principles of user interfaces
- understand operating systems principles
- understand the role of the cloud platform in an IoT pipeline
- explain the structure of the IoT data pipeline, the meaning of the parts of the pipeline and principles of the machine learning and artificial intelligence
- understand the requirements of sensor data in the data value chain.
- understand the basic principles of the secure data transfer from the IoT device to the IoT data pipeline

Assessment scale

1-5