SMUA4300 Advanced Research Methods Emneplan

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 analysis 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 and data-driven computational techniques as some of the most common tools used in these areas.

None

No formal requirements over and above the admission requirements.

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 libraries and tools used in statistical analysis and visualisation of the results
• designing experiments, preparing data and interpreting analysis results
• how relevant statistical and computational techniques relate to each other and where they are used

Skills:

The student has:

• required skills in setting up sound experiments, 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 computational techniques and libraries as well as related tools for statistical analysis
• hands-on experience with relevant tools for use in analyses

General competence:

The student:

• has broad overview of the computational tools and techniques used in analysis and engineering research, including statistical techniques and techniques related to data science and machine learning
• 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

This course features 3 optional preparatory weeks and 9 weeks of lectures that provide both theoretical and practical content and hands-on experience. The students will be given one mandatory project task to work in groups during the semester.

The preparatory weeks are optional and is for supporting the students who need to build up or renew Python/R programming skills, also using tools like Notebooks for presenting with embedded code. Notebooks will be used widely in lectures, exercises and in the mandatory project.

Students are required to complete one mandatory project assignment 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).

1) A final course report in predefined areas prepared in groups of 2 (or more) students, approx. 15 - 20 pages (excluding appendices, but including code and calculations), weighted 60%.

2) Oral presentation and examination of the report, (in the same group as part 1) 15 minutes + 5 minutes Q&A, weighted 40%.

Both assessment parts must be awarded a pass grade (E or better) to pass the course. 

Assessment parts: 1) can be appealed, 2) cannot be appealed

1) All aids are permitted, as long as the rules for source referencing are complied with.

2) None

Graded scale A-F.

Two internal examiners.

 External examiners are used regularly.

Lena Magnusson Turner