Course syllabus for Statistical engineering practices for industrial development

The course syllabus contains changes
See changes

Course syllabus adopted 2023-10-11 by Head of Programme (or corresponding).

Overview

  • Swedish nameIndustriell utveckling med hjälp av statistiska metoder
  • CodeTRA250
  • 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 97153
  • Open for exchange studentsYes

Credit distribution

0123 Project 7.5 c
Grading: TH		 3.7 c3.8 c

    Examiner

    Go to coursepage (Opens in new tab)

    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.

    Basic course in mathematical statistics on bachelor level

    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 aim of the course is to develop professional skills regarding robust engineering and quality development by utilizing the data to the most. That is learn how to use state-of-the-art data visualization and analysis methods and tools to explore historical data and to build predictive models on both correlated and un-correlated multivariate data; how to plan new data collection in both experiments and simulations (meta-modelling).

    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:
    • Explain the basics in Statistical thinking in order to be able to challenge and redefine problem statements from a pull perspective
    • Characterize the performance of a process/production/operation performance, by quantifying, controlling and reducing variation, with tools such as process capability, control charts and measurement system analysis according to standard operation procedures for Quality Development and Continuous Improvement
    • Conduct Exploratory Data Analysis (EDA) using state-of-art tools for data visualisation to draw conclusions on historical data
    • Draw conclusions on data using basic statistical inference testing for different data types, to support Decision Making
    • Apply and evaluate tools and method for Correlation and Regression
    • Apply and evaluate different approaches of Design of Experiments
    • Use different methods of Predictive modelling and text mining in order to uncover relationships in data


    Content

    The course addresses the following themes
    through workshops and practical applications:
    • Statistical thinking in problem definition and problem solving
    • Exploratory data analysis and graphical analysis of data
    • Quality methods
    • Fact-based decision making
    • Correlation and regression (multivariate)
    • Modern methods for design of experiments
    • Predictive and prescriptive modelling and optimization.

    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

    Kursen innehåller en praktisk halvdags workshop varannan vecka, med stöd av inspelade föreläsningar, webbseminarier, övningar och inlämningar.
    Den huvudsakliga undervisningsplattformen är den statistiska plattformen JMP Pro. Den praktiska träningen och problemlösningen kommer att stödjas av utvald teori från böcker och tidskrifter.

    Literature

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

    A selection of journal articles and book sections will be used as references, and selection of webinars

    Examination including compulsory elements

    • Theory/tool practices: Three quizzes in total added together, max 30p
    • Tool application and testing in projects (or side tasks): max 10p
    • Reflective summary on the overall general procedure: max 10p
    • Approved PIP (presentation format)
    • Approved solution of individual DOE-problem (based on simulation)
    Grading 3 (20p), 4 (30p) and 5 (40p)

    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.

    The course syllabus contains changes

    • Change made on course round in programme overview:
      • 2023-10-09: Removed [MPPEN, Year 1 rule V] Course round 1 removed by UOL
      • 2023-10-09: Removed [MPAEM, Year 1 rule V] Course round 1 removed by UOL
      • 2023-10-09: Removed [TRACKS, Year 1 rule V] Course round 1 removed by UOL
    • Changes to course rounds:
      • 2023-10-09: Cancelled Changed to cancelled by UOL
        [Course round 1] Cancelled