When: 8-12 May 2023, 10 AM — 3 PM each day
Where: UiO, Oslo
Credits: 2 ECTS
Lecturer: Jonathan Lilly / Planetary Science Institute
Organizer: Joe LaCasce / UiO
Max no. of participants: 14
Registration deadline: 9 April 2023
This course introduces students to essential tools for analyzing any type of dataset from oceanography, atmospheric science, or climate. The centerpiece, called “distributional data analysis”, is a simple yet powerful method for delving into a potentially large, multivariate dataset by examining its statistics in two-dimensional slices. Elementary statistics, data organization and manipulation techniques in Python or Matlab, and data visualization strategies are all addressed. The course also provides innovative training in the mental factors of curiosity, imagination, and objectivity that are essential for scientists.
Students apply these techniques to a dataset of their own choosing using either Matlab or Python, and learn further through homework problems and group exercises. The course is highly interactive and customized to the student’s needs and experience. Frequent one-on-one meetings with the instructor in which the students get feedback and suggestions will help them delve into their particular dataset.
Outcomes: At the end of the course, students will be well-prepared to begin efficiently analyzing any dataset they might encounter, while avoiding common pitfalls. Students gain practical experience through hands-on demonstrations and exercises.
Learning modules/structure: There will be roughly 2.5 hours of lectures and a two-hour lab session each day. Students are expected to bring a dataset of any type that they would like to analyze for a course project. Multivariate datasets are encouraged. Model output is also acceptable.
Please be advised that the course will be a full-time activity, as there will also be homework and other activities outside of class time. Students should plan to clear their schedules during that time to the extent possible. Planning to keep up with other major responsibilities during the course time (e.g. research deadlines or teaching) is not realistic.
Course notes, lab exercises, and associated code are all available online at http://www.jmlilly.net/course/index.html.