Introduction to Industrial Analytics
Syllabus, Master's level, 1TS304
- Code
- 1TS304
- Education cycle
- Second cycle
- Main field(s) of study and in-depth level
- Industrial Engineering and Management A1N
- Grading system
- Fail (U), Pass (G)
- Finalised by
- The Faculty Board of Science and Technology, 25 February 2020
- Responsible department
- Department of Civil and Industrial Engineering
Entry requirements
A Bachelor's degree, equivalent to a Swedish Kandidatexamen, from an internationally recognised university, including 90 credits in mechanical engineering, industrial engineering, production engineering, automation engineering and/or computer science/information technology; 5 credits in computer programming; 20 credits in mathematics; 5 credits in statistics and probability theory. Proficiency in English equivalent to the Swedish upper secondary course English 6.
Learning outcomes
On completion of the course the student shall be able to
- account for the various components and production data of a production system and carry out a process mapping,
- describe the concepts industry 4.0 and Industrial Analytics,
- briefly describe some common tasks in the field of Industrial Analytics and give examples of local and global companies where they occur,
- sift, search and critically review technical information,
- use digital analysis tools to improve production systems,
- carry out work in the form of a project,
- apply the basics of scientific report writing,
- reflect on ethical aspects of the professional role,
- account for equal conditions for all grounds of discrimination.
Content
Overview of the program's objectives and content as well as the field of Industrial Analytics and Industry 4.0. Introduction to studies at an advanced level, regulations, ethical principles and the writing of scientific reports. Equal conditions for all grounds of discrimination. The professional role of the engineer and computer scientist in the field of Industrial Analytics. The various components of the production system, digital models, basic programming, production data and process mapping. Work in project form.
Instruction
Lectures, guest lectures, seminars. laboratory work and supervision of project work.
Assessment
Written presentation of assignments, active participation in seminars and oral and written presentation of project work.
If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the disability coordinator of the university.
Reading list
No reading list found.