Course syllabus for Data science in product realization

Course syllabus adopted 2025-02-19 by Head of Programme (or corresponding).

Overview

  • Swedish nameData science inom produktframtagning
  • CodeIMS065
  • Credits7.5 Credits
  • OwnerMPPEN
  • Education cycleSecond-cycle
  • Main field of studyAutomation and Mechatronics Engineering, Mechanical Engineering
  • DepartmentINDUSTRIAL AND MATERIALS SCIENCE
  • GradingTH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail

Course round 1

  • Teaching language English
  • Application code 34119
  • Maximum participants50 (at least 10% of the seats are reserved for exchange students)
  • Open for exchange studentsYes

Credit distribution

0120 Project 7.5 c
Grading: TH		 7.5 c

In programmes

Examiner

Eligibility

General entry requirements for Master's level (second cycle)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.

Specific entry requirements

English 6 (or by other approved means with the equivalent proficiency level)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.

Course specific prerequisites

Programming, statistics, fundamentals of product and/or production development.

Basic experience in Matlab or similar software for data analysis is highly desirable. Consider studying a preparatory course in Matlab (such as TME265) as a preparatory course if applicable.

Aim

The purpose is 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 and Machine Learning) and abilities to apply data science techniques for improving production systems and product development.

Learning outcomes (after completion of the course the student should be able to)

On successful completion of the course, the student will 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. Perform data pre-processing methods to ensure multi-dimensional measure of data quality.
LO6. Explain and interpret the utilization of data and the 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.

Content

The course is divided into 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 and Visualization
  • Introduction to the data mining process
  • Exploratory Data Analysis (EDA) & Statistics
  • 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 the product realization process - project work
  • Practicing with group work project for understanding AI/ML systems through the selected industrial cases from product realization life cycle

Organisation

The course applies active learning methods including problem-based learning activities and flipped classroom techniques to be able to engage with students and support their learning in a creative way. Different learning activities will be used in the modules:
  • Lectures
  • Laboratory exercises including introductive programming tutorial
  • Modelling exercises for training different visualization, data pre-processing techniques, and AI/ML applications
  • Project work
  • Presentation and discussion of scientific papers related to applications in the product realization process

Literature

- Scientific papers
- Lecture materials
- Selected parts of e-books and other on-line materials

Examination including compulsory elements

The examination project work consists of a technical report and its attachments, which include software code and an impact description poster. Students must be approved on all assessment tasks individually such as project work, self-paced hands-on exercises, mandatory knowledge test (online quiz) through CANVAS, and literature seminar presentation to pass the course. Grades are individual and the grading scale is Failed, 3, 4 and 5. The following logic will be used for deciding the individual grades (maximum 100p):
  • Mandatory project report (including its attachments) = maximum 70 points
  • Mandatory knowledge test (online quiz) = maximum 30 points
Individual grade 5: Same as for grade 3 AND total number of points ≥ 80 p
Individual grade 4: Same as for grade 3 AND total number of points ≥ 60 p
Individual grade 3: Project report (including its attachments) ≥ 35 p AND knowledge test ≥ 15 p AND other assessment tasks complete.
Individual grade F: Project report (including its attachments) < 35 p OR knowledge test < 15 p OR other assessment tasks incomplete.

The course examiner may assess individual students in other ways than what is stated above if there are special reasons for doing so, for example if a student has a decision from Chalmers about disability study support.