GEO9300 – Geophysical Data Science

Schedule, syllabus and examination date

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Teaching and exams spring 2022

At the MN Faculty teaching is in-person this spring, but there may still be cases of online or hybrid teaching.

Please see the semester page, Canvas or your regular channel of information for changes in teaching and form of examination.

Course content

Geophysical Data Science provides a foundation for statistical analysis and modelling of Earth System data sets. Students are initially provided with a review of core fundamental statistical concepts: probability, distribution, linear and multiple regression. Through the semester, course material will advance to cover important tools in climate, earth system modelling, and hydrologic analysis, including hypothesis testing and uncertainty analysis, stochastic processes, temporal and frequency domain analysis, principal component analysis and canonical correlation.

Learning outcome

After completing this course you will

  • have the competence to manage, conduct quality control, and analyze and classify time series of complex geophysical data
  • be able to statistically characterize and model geophysical data
  • conduct analysis of extreme events in the temporal and frequency domain
  • conduct multiple linear regression
  • evaluate Spatio-temporal variability
  • develop stochastic models and evaluate residuals from geophysical data
  • conduct basic time series analysis
  • perform basic kriging operations on spatial data
  • gain skills to manage large and complex data from earth system models, reanalysis datasets, satellite products, and heterogeneous observations
  • conduct an intensive data analysis project on your own data

Admission to the course

PhD candidates from the University of Oslo should apply for classes and register for examinations through Studentweb.

If a course has limited intake capacity, priority will be given to PhD candidates who follow an individual education plan where this particular course is included. Some national researchers’ schools may have specific rules for ranking applicants for courses with limited intake capacity.

PhD candidates who have been admitted to another higher education institution must apply for a position as a visiting student within a given deadline.

Overlapping courses


The course includes 2 x 2 hours of lectures per week and 3 hours of computer lab weekly providing data-processing and practical programming exercises. Compulsory assignments and the intensive data analysis project are graded and must be passed before the final written exam.

Attendance at the first lecture is compulsory. Students who fail to meet are considered to have withdrawn from the course unless they have previously given notice to the Student administration (

We reserve the right to change the teaching form and examination of the course in semesters where 5 or fewer students have been admitted.


  • Exercises, assignments and the data analysis project are examined and count 50% together towards the final grade.
  • The final written examination counts 50% towards the final grade.
  • Both the exercises, assignment, the data analysis project and the final written examination must be passed individually in order to pass the course.

When writing your exercises make sure to familiarize yourself with the rules for use of sources and citations. Breach of these rules may lead to suspicion of attempted cheating.

It is possible to take the exam up to 3 times. If you withdraw from the exam after the deadline or during the exam, this will be counted as an examination attempt.

It will also be counted as 1 of the 3 attempts to sit the exam for this course if you sit the exam for one of the following courses:

Examination support material

No examination support material is allowed.

Language of examination

Courses taught in English will only offer the exam paper in English.

You may write your examination paper in Norwegian, Swedish, Danish or English.

Grading scale

Grades are awarded on a pass/fail scale. Read more about the grading system.

Resit an examination

Students who can document a valid reason for absence from the regular examination are offered a postponed examination at the beginning of the next semester.

Re-scheduled examinations are not offered to students who withdraw during, or did not pass, the original examination.

Special examination arrangements, use of sources, explanations and appeals

See more about examinations at UiO

Last updated from FS (Common Student System) May 18, 2022 10:30:38 PM

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