SMUA4300 Advanced Research Methods Emneplan

Upon completing the course, the student will have the following outcomes.

Knowledge:

Upon successfully completion of the course, the student:

• has advanced knowledge of spatial data handling
• has advanced knowledge of spatial analysis techniques, including a good understanding of predictive algorithms and geo-statistics, relevant to spatial & urban analysis
• has advanced knowledge of how to implement artificial intelligence (AI) and machine learning (ML) in urban analytics
• has advanced knowledge of data analysis (analytical techniques) and visualization using geospatial tools, primarily within urban analytics

Skills:

Upon successfully completion of the course, the student:

• is an experienced user and have a broad understanding of Geographical Information Systems (GIS)/Geospatial tools as well as of methods and theory in spatial analysis
• has hands-on experience with relevant techniques, algorithms and scripting in GIS/Geospatial tools
• has hand-on experience with relevant programming and scripting environments for use in data science, especially within urban analytics and visualization of urban data
• has knowledge of how to test and implement urban theory using spatial data and urban analytics

General competence:

Upon successfully completion of the course, the student:

• has broad overview of both the challenges and the tools of urban analytics
• is able to design approaches and utilize tools for analysing and visualizing urban data
• is able to extend his/her knowledge and skills in programming/scripting, analysing, managing and visualizing data both in urban analytics and in other area
• is able to use a relevant theoretical framework in applied studies
• can create and communicate findings in ways that are relevant to stakeholders

None

This course features 10 weeks lectures with 10 parallel lab sessions to provide theoretical content and preliminary hands-on experience. The lab sessions will be preceded with one or two weeks of (primarily technical) preparatory sessions.

All individual assignments must be approved.

Lab assignments must be handed in

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

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

2) None

Graded scale A-F.

1) Two internal examiners.

External examiners are used regularly.

John Östh; Email: john.osth@oslomet.no

None.