EPN-V2

SMUA4300 Advanced Research Methods Course description

Course name in Norwegian
Advanced Research Methods
Weight
5.0 ECTS
Year of study
2026/2027
Course history
Curriculum
FALL 2026
Schedule
  • Introduction

    Much of the work in students´ scientific or practical work both during their studies and later in their professional life will require a good understanding of research methods and analytical tools and techniques. This course will provide the knowledge and skills necessary for planning and conducting engineering research, for processing data and for analysing results, with special focus upon the most common statistical techniques.

    Language of Instruction: English

  • Recommended preliminary courses

    None

  • Required preliminary courses

    None.

  • Learning outcomes

    After completing the course, the student is expected to have achieved the following learning outcomes defined in terms of knowledge, skills and general competence:

    Knowledge:

    The student has advanced knowledge of:

    • research methods, especially in engineering research
    • statistical and analytical techniques, including knowledge of the most common methods used in statistical analysis and visualisation of the results
    • designing experiments, formulating research questions, preparing data and interpreting analysis results
    • how relevant statistical techniques relate to each other and where they are used

    Skills:

    The student has:

    • required skills in setting up sound hypotheses and research questions, and in finding and preparing relevant data
    • required skills in identifying which statistical and analytical techniques are to be used and how and where they should be used
    • hands-on experience with some of the most common tools for statistical analysis

    General competence:

    The student:

    • has broad overview of computational tools and techniques used in statistical analysis relevant for engineering and planning
    • has an overview of the terminology related to statistical analysis and data science.
    • is able to design experiments for successful engineering research, analyses and critical interpretation of results
    • can extend his/her knowledge and skills in programming/scripting, analysing, managing and visualizing data
  • Teaching and learning methods

    This course features lectures and labs that provide both theoretical and practical content and hands-on experience.

  • Course requirements

    The following coursework requirements must be approved in order for the student to take the exam:

    • one mandatory homework in allocated time and get it approved.

    Students who fail to meet the coursework requirements can be given up to one re-submission opportunity. (more details in Canvas).

  • Assessment

    Individual written exam under supervision (4 hours)

    The exam can be appealed.

  • Permitted exam materials and equipment

    Textbook, syllabus and own notes are permitted aids.

  • Grading scale

    Grade scale A to F.

  • Examiners

    Two internal examiners.

     External examiners are used regularly.

  • Course contact person

    Hanna Vangen og Kristin Aarland