Course syllabus


IMS065 Data science in product realization lp2 HT21 (7.5 hp)

Course is offered by the department of Industrial and Materials Science

You could find updated course PM as in the following link:


Contact details

Course Examiner
Anders Skoogh, PhD
Professor of Production Maintenance
Director of the Production Engineering master’s program
Department of Industrial and Materials Science
Division of Production Systems


Phone: +46 (0)31 772 48 06

Course Coordinator & Administration
Ebru Turanoglu Bekar, PhD
Department of Industrial and Materials Science
Division of Production Systems


Phone: +46 (0)31 772 64 13

Course aim

The course aims to enable data-driven and facts-based decisions in mechanical engineering, specifically in the industrial product realization process. Therefore, the course aims to provide the students with fundamental knowledge about data science (including elements of Artificial Intelligence - AI and Machine Learning - ML) and abilities to apply data science techniques for improving production systems and product development. This course provides well understanding of AI systems through the appropriate formulation of the problem and the choice/application of suitable ML algorithms in order to assess the effectiveness of such algorithms using industrial case studies from product realization life cycle. 

Course literature

See Course PM for more details.

  • Lecture materials (power-point presentations) (available at Canvas homepage of the course)
  • Scientific papers (available at Canvas homepage of the course)
  • Recommended resources to support learning which are provided in Course PM.

Course content and organization

The course is divided four modules and each module covers the following topics:

Module 1 - Introduction to Data Science

  • Fundamentals of data science (AI/ML)
  • An overview of data-driven modelling
  • Introducing toolboxes for data scientists

Module 2 - Data Mining & Visualization

  • Introduction to the data mining process and work procedures
  • Plotting for exploratory data analysis (EDA)
  • An overview of data quality dimensions
  • Methods for data pre-processing

Module 3 - AI and ML

  • A general introduction to AI and ML
  • Examples of ML algorithms to understand in what situations they can be used
  • Examples of deep learning
  • Analysis of different industrial applications from product realization life cycle using AL/ML
  • The ethics of AI (will be covered by reading scientific papers and discussion in literature seminar presentation)

Module 4 - How to drive AI in your business - Project work

  • Practicing with group work project for understanding AI/ML systems through the appropriate formulation of the selected industrial cases from product realization life cycle

Different teaching and learning activities (TLAs) will be used during the course summarized as below:

  • Lectures – Basis for theoretical understanding of the concepts of data science.
  • Guest lectures – Basis for understanding different industrial applications of data science.
  • Workshops – To reinforce the learning via more engagement with the students related to data utilization and analytics of the selected industrial cases from product realization life cycle.
  • MATLAB seminars – To support learning of the necessary toolboxes, which are expected to use in examination project work.
  • Self-paced hands-on exercises – Training in an interactive tutorial in MATLAB called Machine Learning Onramp course, which introduces practical ML methods, preparation for MATLAB seminars and examination project work.
  • Project work, which is mandatory for examination, and it aims to practice skills learned throughout the course based on a structured project methodology.
  • Presentation and discussion of scientific papers related to applications of data science in the product realization process.

Changes made since the last occasion

No changes from the last year lp2 HT20.

Learning objectives and syllabus

On successful completion of the course, the student should be able to:

LO1. Describe the fundamentals of data science, its applications (AI/ML), data-driven modelling and big data analytics.

LO2. Apply the basics of well-known libraries of the toolboxes for data scientists.

LO3. Describe steps of the data mining process.

LO4. Describe and apply visualization techniques with respect to the data mining process.

LO5. Describe and perform data pre-processing methods to ensure multi-dimensional measure of data quality.

LO6. Explain and interpret utilization of data and applicability of AI/ML algorithms for improving production systems and product development.

LO7. Interpret and discuss state-of-the-art knowledge from scientific papers related with data science in mechanical engineering.

LO8. Implement commonly used AI/ML algorithms, analyze their performance, and discuss their application using industrial applications from product realization life cycle.

LO9. Critically analyze and argue key ethical principles and potential impacts of AI on people and society and evaluate social and human requirements of systems and scenarios.

Link to the syllabus on Studieportalen.

Study plan

If the course is a joint course (Chalmers and Göteborgs Universitet) you should link to both syllabus (Chalmers and Göteborgs Universitet).

Examination form

In this course, project-based learning is designed to give students opportunities to explore new concepts, take on new problems, formulate the problems by experiencing in different ways, and reflect on these experiences in order to improve their performance in an iterative cycle based on a structured project methodology called Cross Industry Standard Process for Data Mining (CRISP-DM). This project work is mandatory for examination and it aims to practice skills learned throughout the course.

Grading is based on the examination project work including a technical report and recorded oral presentation. Students must be approved on all assessment tasks individually such as project work, hands-on exercises, mandatory knowledge test (online quiz) through CANVAS, and literature seminar presentation to pass the course. The grading scale are Failed, 3, 4, and 5. The result of the project work has outstanding weight for grading. The result from the mandatory knowledge test will serve as decision support in borderline cases. The literature seminar and project presentations will also used as decision support for borderline grades. Further details about examination will be presented throughout the course.

Course schedule

Most course activities will be hybrid (on campus and in Zoom) and recorded. A document with suggestions on how to handle potential scheduling clashes will be published before course start. You could find preliminary schedule in course PM as given in the top of this page. 

Course summary:

Date Details Due