Course syllabus for Data-driven product realization

Course syllabus adopted 2023-02-12 by Head of Programme (or corresponding).

Overview

  • Swedish nameDatadrivet produktförverkligande
  • CodeTRA235
  • Credits7.5 Credits
  • OwnerTRACKS
  • Education cycleSecond-cycle
  • DepartmentTRACKS
  • GradingTH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail

Course round 1

The course round is cancelled. For further questions, please contact the director of studies
  • Teaching language English
  • Application code 97150
  • Open for exchange studentsYes

Credit distribution

0123 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

In addition to the general requirements to study at advanced level at Chalmers, necessary subject or project specific prerequisite competences (if any) must be fulfilled. Alternatively, the student must obtain the necessary competences during the course. The examiner will formulate and check these prerequisite competences.

The student will only be admitted in agreement with the examiner.

Aim

The aim of the course is to provide a platform to work and solve challenging cross-disciplinary authentic problems from different stakeholders in society such as the academy, industry or public institutions. Additionally, the aim is that students from different educational programs practice working efficiently in global multidisciplinary development teams.

The demand for future engineers with multi-disciplinary competencies in developing and applying AL/ML solutions in industry has skyrocketed. Therefore, the course aims to provide students with fundamental knowledge about data science (including AI and ML) as well as skills in applying data science techniques for improving production systems and product development.

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


Valid for all Tracks courses:
  • critically and creatively identify and/or formulate advanced architectural or engineering problems
  • master problems with open solutions spaces which includes to be able to handle uncertainties and limited information.
  • lead and participate in the development of new products, processes and systems using a holistic approach by following a design process and/or a systematic development process.
  • work in multidisciplinary teams and collaborate in teams with different compositions
  • show insights about cultural differences and to be able to work sensitively with them.
  • show insights about and deal with the impact of architecture and/or engineering solutions in a global, economic, environment and societal context.
  • identify ethical aspects and discuss and judge their consequences in relation to the specific problem
  • orally and in writing explain and discuss information, problems, methods, design/development processes and solutions
  • fulfill project specific learning outcomes
Course specific:
  • Critically and creatively identify and formulate data analytics problems
  • Solve open-ended data analytics problems
  • Lead and participate in data analytics projects
  • Work in multidisciplinary analytics teams
  • Work sensitively with cultural differences in analytics teams
  • Assess the impact of data analytics solutions
  • Identify and discuss ethical aspects of data analytics
  • Present the results of data analytics project to various stakeholders

Content

The course is structured according to the Cross-Industry Standard for Process Mining (CRISP-DM) model, which is a systematic approach for planning, executing, and deploying data analytics projects.
The course includes 8 major phases with the following content:
Phase 1 – Course Introduction & Project Introduction
  • Course and project introduction
  • Data science in Product Realization
  • Introduction to Data science
  • CRISP-DM
Phase 2 – Business Understanding
  • General project management skills
  • Multi-disciplinary skills & Roles and responsibilities
  • Problem-solution mapping
  • Intercultural communication
  • Diversity and inclusion
Phase 3 – Data Understanding
  • Exploratory Data Analysis
  • Data quality
Phase 4 – Data Preparation
  • Methods for pre-processing
  • Phase 5 – Modeling
  • AI/ML/Deep Learning
Phase 6 – Evaluation
  • AI Ethics
  • Confusion matrix & Business impact
Phase 7 – Deployment
  • Model deployment
  • Operation and Maintenance
Phase 8 – Report & Presentation
  • Final report and presentation seminar
  • Self-studies
  • Hand-in of final report
Multi-disciplinary teams collaborate on solving complex industrial problems together with manufacturing companies. Real-world needs, requirements, and data sets are provided by our partner companies. The course is a part of the Tracks Theme “Sustainable Production” and targets the UN Sustainable Development Goals 9 (Industry, Innovation, and Infrastructure) and 12 (Responsible Consumption and Production).
The projects in the course encompass areas of product development, production improvements, quality management, and maintenance. In addition to project-based learning, we provide lectures and workshops developing: fundamental understanding of AI/ML; communication and teamwork skills to successfully integrate key roles (e.g., data scientists and domain experts); and insights about managing the organizational change required to harness the value of AI/ML.

Organisation

The course is run by a teaching team.
The main part of the course is a challenge driven project. The challenge may range from being broad societal to profound research driven. The project task is solved in a group. The course is supplemented by on-demand teaching and learning of the skills necessary for the project. The project team will have one university examiner, one or a pole of university supervisors and one or a pole of external co-supervisors if applicable.

Tracks-theme: Sustainable Production

The course follows a project-based learning pedagogy and consists of a mix of Teaching and Learning
Activities (TLAs):

  • Lecture: Theoretical knowledge, skills, and abilities needed for the project work.
  • Community: Industry & Experts talks to get inspiration from state-of-the-art data analytics
  • applications, combined with social learning across project groups.
  • Team Project: Practical work focused on the achievement of data-driven and fact-based decisions in the industrial product realizing process.
  • Examination: Assessment activities that constitute the basis for the final grade.

Literature

Relevant literature is retrieved and acquired by the students as a part of the project.

Examination including compulsory elements

The course examination is based on three parts: (1) project work, (2) individual quiz, and (3) individual reflection. The final grade includes the students’ performance on all three parts, and all three parts are mandatory and must be approved separately to pass the course.
The project work covers the entire process from initial project formulation to the final presentation and report (see practical details under heading “Project work”). The report constitutes the main basis for grading the project work. The individual quiz covers the content presented during lectures, community, and literature. The quiz will be conducted as an online knowledge test in Canvas. The individual reflection covers the student’s ability for in-depth reflection on success factors experienced during the project work.
The final grade includes the students’ performance on all three assessment tasks:
  • Project report and seminar: maximum 60 points.
  • Individual quiz: maximum 25 points.
  • Individual reflection: maximum 15 points.
Grades are individual. The grading scale is Failed, 3, 4, and 5. All points are summarized and the final grade is decided accordingly (total sum cannot surpass 100):
0-49 points = Fail
50-64 points = 3 (and all assessment task must be separately approved)
65-84 points = 4
85-100 points = 5


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 on educational support due to disability.