Course syllabus

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Course-PM

IMS065 Data science in product realization lp2 HT24 (7.5 hp)
The course is offered by the Department of Industrial and Materials Science
You could find the updated version of PM in the following link:

IMS065_CoursePM_V3_241103.pdf

Contact details

Course Examiner
Ebru Turanoglu Bekar, PhD
Senior Lecturer in Production Systems Division 
Department of Industrial and Materials Science
Chalmers University of Technology

E-mail: ebrut@chalmers.se
Phone: +46 (0)31 772 64 13

Course Assistant
Mohan Rajashekarappa
Doctoral Student in Production Systems Division 
Department of Industrial and Materials Science
Chalmers University of Technology

E-mail: rmohan@chalmers.se
Phone: +46 (0)31 772 20 26

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 a 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 steps. 

Course literature

See Course PM for more details.

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

Course content and organization

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 & 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 projects for understanding AI/ML systems through the appropriate formulation of the selected industrial cases from the 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.
  • MATLAB seminars: Basis for supporting 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: Practicing skills learned throughout the course based on a structured project methodology, which is mandatory for examination.  
  • Literature seminar: Presentation and discussion of scientific papers related to industrial applications of data science and key ethical principles and potential impacts of AI.

Changes made since the last occasion

The course examiner has been changed from the last year lp2 HT23.

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. 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.

Examination including compulsory elements

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.

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. The result of the project work has outstanding weight for grading. The result from the mandatory knowledge test will serve as decision support in grading as well and it covers the content presented during lectures and guess lectures.

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.

Course schedule

Most course activities will be organized on campus, however, MATLAB seminars will be held online via zoom. More information related to those activities will be provided when the course starts. You could find the schedule in course PM as given at the top of this page. 

Students Representatives

All Chalmers courses have student representatives who are expected to take part in a start-up meeting, one meeting in the middle of the course, and the final course evaluation meeting in the study period after the course where thoughts and impressions about the course are shared. The course representatives for IMS065 HT24 have been randomly selected and consist of:

Sanju Ackalathil Chellappan sanjuackalathil789@gmail.com

Vishwas Aravind vishwasaravind2k@gmail.com     

Nishant Pattanaik pattanaiknishant1298@gmail.com           

Sara Sterne sara.sterne5309@gmail.com       

Jonathan Syrén jonathan.syren@gmail.com          

Course summary:

Date Details Due