SMUA4300 Advanced Research Methods Course description

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

All aids are permitted as long as rules for source referencing are followed. Use of Ai must be specified

None.

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

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

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).

Geographic Information Systems (GIS) is a system of hardware, software, and procedures designed to support the capture, management, manipulation, analysis, modeling and display of spatially referenced data for solving complex planning and management problems. GIS applications use both spatial information (maps) and databases to perform analytical studies.

This course, including both lectures and practices, will cover the fundamental theories and methods of GIS. A series of seminars will enable the students to make practical use of GIS with hands-on experience.

In this course, the students will learn to edit, organize and manipulate spatial data in meaningful ways to solve spatial problems, using ESRI ArcGIS software as well as other GIS- and data manipulation software

GIS technology has broad applications in natural and social sciences, humanities, environmental studies, engineering, and management. Examples include: Urban and Regional Planning, Community and Economic Planning and Development, Housing Studies, Transit and Transportation Issues, Land Use, Historic and Archeological Studies, Agriculture and Forestry, Wildlife Habitat Study, Crime Analysis and Policing, Emergency Management and Public Works Utilities, Census and Demographic Studies, Public Health, Contagious Disease Monitoring, and Business uses including Marketing and Advertising. This course will introduce a few selected cases of GIS application in different disciplines.

Language of instruction: English

Upon completing the course, the student should have the following outcomes, defined in terms of knowledge, skills, and general competence.

Knowledge:

Upon successfully completion of the course, the student has gained sufficient knowledge to:

• describe the components of a Geographic Information Systems (GIS)
• explain GIS data structures and models, including vector and raster data
• quantify and analyze the spatial distribution of phenomena and provide meaningful analysis of spatial attributes.

Skills:

Upon successfully completion of the course, the student can:

• conduct GIS analysis by combining different spatial data operations such as distance calculation, overlay and buffer analysis, to address geospatial problems and/or research questions
• demonstrate proficiency in the use of GIS tools to create maps that are fit-for-purpose and effectively convey the information they are intended to.
• implement advanced GIS functions or analyses

General competence:

Upon successfully completion of the course, the student is able to:

• critically evaluate available sources for data in a GIS
• reflect on the spatial impact of decision-making and on the potential for using large spatial datasets for in-depth multi-faceted analytics
• demonstrate confidence in undertaking new (unfamiliar) analysis using GIS, troubleshoot problems in GIS, and seek help from software/website help menus and the GIS community to solve problems
• communicate results in a powerful and effective way.

The course is delivered through seminars and hands-on computer workshops.

Teaching will be organized in topic-blocks with practical and theoretical parts in each block.

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

• Three lab assignments must be handed in on Canvas

Students who fail to meet the coursework requirements can be given up to one re-submission opportunity.

Portfolio assessment consisting of:

• An individual GIS- analysis from a chosen urban area
• An individual or group in-depth report, approx. 10-15 pages for single students, for groups the page count depends on composition and task. Group size is dependent on complexity of the task, but a median group size of two with up to five participants are anticipated. In group work, the students’ different contributions must be reflected in the submitted work as the members are assessed individually. Students may write the report alone as well.
• Oral presentation

Each student's work will be assessed together as a portfolio with one overall individual grade at the end of the semester.

The exam may be appealed. Upon appeal, the written work will be reassessed, and the student must complete a new oral presentation/exam.

In the case of a group examination, the right to appeal is individual. This means that each group member may only submit an appeal on their own behalf. A student who submits an appeal must complete the oral presentation individually. Failure to attend will result in the grade "did not attend" for the entire course.