The Doctoral Programme in Atmospheric Sciences is responsible for the course. The course is a part of discipline-specified studies module and it is optional.
The course is available for students in other degree programmes.
Any course focused on experimental design, data analyses, data management, programming can be beneficial albeit not required.
By completing the course, the student will learn practical skills handling and analyzing complex data-sets and problems. The student will also learn up-to-date data science issues.
The course can be completed at any stage of studies.
The course lasts from September to May and it is organized once a year.
The aim of the course is to demystify data management (collection, handling) and analyses used in forest and environmental sciences. The course is loosely based on the “guidance-group” structure, and addresses what we feel is a critical need, guidance with practical computing issues.
The course will have a participatory 'problem-focused' approach, where students will learn practical skills (on their own personal laptops) handling complex data-sets and problems. We will also invite a number of “data-experts” to give guided seminars (and code samples if appropriate) on up-to data science issues. These include:
- computer based data analyses methods using (mainly) open source tools
- web programming and interfacing (SMARTSMEAR)
- data and code repositories
- open data issues
- open source hardware (Raspberry Pi, Arduino, drones etc)
A guiding principle of the course is zero (programming) barrier to entry; coding experts and novices are both welcome. The information and tools (e.g. code, lectures, data-sets) we gather will be hosted online (e.g. moodle) as a practical reference source for students.
Reading package will be given during the course.
Practical sessions, problems prepared for practical sessions by students.
Active participation and 100/80% attendance is required.
Teaching language: English
There will be 2 x 1hr sessions per month; 1 practical session and 1 invited speaker (seminar).
Students must contribute to practical sessions by way of preparing a 'problem' for practical session. The problem can be an actual data (programming, analysis) issue from a student current research, or else a general topic of interest, and should be submitted in the 2 weeks prior to the practical session.