Decision and Data Science Applications (MOD900)
Course description for study year 2024-2025. Please note that changes may occur.
Facts
Course code
MOD900
Version
1
Credits (ECTS)
10
Semester tution start
Spring, Autumn
Number of semesters
1
Exam semester
Spring, Autumn
Language of instruction
English
Content
The purpose of this course is to broaden the PhD Fellow's knowledge beyond the main PhD dissertation topic. The PhD Fellow will, on his/her own accord, work through the curriculum with the intent of developing a broad and solid understanding of decision and data science as it applies to energy resources. The candidate will also do independent research in the topic area.
Learning outcome
Knowledge:
- Upon completion of the course the PhD candidate should understand the fundamental principles of decision and data science as applied to energy resource engineering as well as understand how principles applies within research. The candidate should have a deep and broad understanding of how to apply these principles to support energy resources related decision making.
Skills:
- Skills needed to build a good basic decision model and to use it in generating powerful insights into the decision situation
- Be able to apply and construct decision models and to use the most important elements in decision analysis relevant to engineering type decision-making in the face of uncertainty.
General competence:
- The PhD candidate should understand fundamental logical principles and analyses and be able to communicate their choices and recommendations clearly.
Required prerequisite knowledge
None
Exam
| Form of assessment | Weight | Duration | Marks | Aid |
|---|---|---|---|---|
| Portfolio | 1/1 | Passed / Not Passed |
Portfolio assessment:
• Achieve a grade of B or better in one of the courses MOD500, MOD550 or PET685
• Pass a project related to the course to be determined by the PhD student’s advisor
Coursework requirements
Achieve a grade B or better in MOD500, MOD550 or PET685
Course teacher(s)
Course coordinator:
Reidar Brumer BratvoldHead of Department:
Alejandro Escalona VarelaMethod of work
Lectures and independent work
Open for
PhD Students
Course assessment
There must be an early dialogue between the course supervisor, the student union representative and the students. The purpose is feedback from the students for changes and adjustments in the course for the current semester.In addition, a digital subject evaluation must be carried out at least every three years. Its purpose is to gather the students experiences with the course.