Objectives and Content
The overall goal of the course is to be a direct contributor to calculations and analyses to be included in the student's MSc or PhD thesis. A secondary goal is that the student learns to do basic calculations and analyses with the software programming language Python.
The teaching is focused on exercises using Python software. Through applications the students are presented to basic concepts and problems of data analysis in general (variables, significance, confidence, hypothesis testing, p-value, statistical test methods, model choice, experimental design, etc.) The course includes time series analysis as well as analysis on spatial data. The weighting of these topics will depend on the students¿ background . At the end of the course the students deliver a term paper. Here, it is recommended that the students are using data from their own MSc or PhD work.
On completion of the course the student should have the following learning outcomes defined in terms of knowledge, skills and general competence:
- can elaborate on fundamental terms and problems in data analysis
- can discuss which techniques are suitable on different types of data
- can show insight with respect to the functionality of Python software
- can do basic computations and analyses using the Python software
- can summarize observations, data, and methodological principles orally and in writing
- can interpret and make decisions based on results from the computations and analyses
- can communicate the importance of applying advanced methods and software, within a specialized work environment and in a general context
- can apply and combine different types of computer programs to solve a complex task
- can demonstrate ability to function well individually and in a team
Credit Reduction due to Course Overlap
The course represents an extension of GEOV301 (5 credit points), which is obsolete after Spring 2017.
Access to the Course
Access to the course requires admission to a Master's programme of The Faculty of Mathematics and Natural Sciences.
Teaching and learning methods
The teaching is arranged in the form of collective working sessions (not traditional lectures) and a seminar.
Compulsory Assignments and Attendance
Mandatory participation in collective working sessions (at least 75% of the allotted time)
Mandatory participation at seminar
Forms of Assessment
The following forms of assessment are used in the course:
¿ Evaluation of delivered exercises, with feedback
¿ Term paper
The final assessment yields the grades passed/not passed
Spring. Assessment is only given in the teaching semester.
The reading list will be available within June 1st for the autumn semester and December 1st for the spring semester.
The course will be evaluated by the students in accordance with the quality assurance system at UiB and the department.
The Programme Committee is responsible for the content, structure and quality of the study programme and courses.
The course coordinator and administrative contact person can be found on Mitt UiB, or you may contact firstname.lastname@example.org
The Faculty for Mathematics and Natural Sciences, Department of Earth Science has the administrative responsibility for the course and program.
The student coordinator can be contacted here:
Type of assessment: Protifolio assessment
- Withdrawal deadline
Exam part: Exercises
Exam part: Term paper