Course plan
Course plans for individual modules
Swedish title
P0 Grundläggande vetenskaplig datorprogrammering i python för VA-teknik
Points
2 Hp/2 ECTS
Suitable for
PhD students who have not had a computer programming course in their basic education
Intended learning outcomes
Upon completing this course, you should be able to:
- Identify and work with different data types;
- Annotate codes;
- Write and carry out functions;
- Carry out calculations using Booleans, conditional statements and loops.
Course format
Self-study from provided materials and exercises; a few submissions and feedback.
Assessment
The course is assessed on a pass-fail scale based on completion of exercises and active participation in the help-sessions.
Teacher
Ico Broekhuizen, Senior lecturer
Examinator
Annelie Hedström, professor
Swedish title
Modul II, P1 Analys av kontinuerliga data i VA-teknik
Points
2 Hp/2 ECTS
Suitable for
PhD students who have basic Python skills
Intended learning outcomes
Upon completing this course, you should be able to:
- Import (parts of) Python modules and manage the namespace;
- Efficiently manipulate data and carry out calculations using Numpy and Pandas;
- Create and format figures using Matplotlib.
Course format
Intensive course during 2 days in Luleå where you get lessons and work in a computer room with your exercises. Assignments are finalized at home after the meeting in Luleå.
Assessment
The course is assessed on a pass-fail scale based on completion of exercises and active participation in the help-sessions.
Teacher
Ico Broekhuizen, Senior lecturer
Examinator
Annelie Hedström, professor
Swedish title
Analysis of environmental quality data using R
Points
2 Hp/2 ECTS
Suitable for
PhD students who have previously taken a programming course in any language.
Intended learning outcomes
Upon completing this course, you should be able to:
- Open files, carry out calculations on matrices and write files in R
- Select, implement in R and interpret appropriate statistical hypothesis tests
- Make and export graphs using ggplot
- Analyze censored data (data sets with many non-quantified values) with the NADA package
Course format
Intensive course during 2 days in Luleå where you get lessons and work in a computer room with your exercises. Assignments are finalized at home after the meeting in Luleå.
Assessment
The course is assessed on a pass-fail scale based on completion of exercises and active participation in the lectures and help-sessions.
Teacher
Kelsey Flanagan, senior lecturer
Examinator
Annelie Hedström, professor
Swedish title
Tillämpning av datorprogrammering för VA-teknik
Points
1 Hp/1 ECTS
Suitable for
PhD students who have completed at least one doctoral course on scientific programming in Python or R for Urban Water Research.
Intended learning outcomes
Upon completing this course, you should be able to:
- Use official Python or R documentation and online forums to seek and learn how to use additional functions when necessary
- Write a well-annotated code which can be used for data analysis or modeling within his or her own research
Course format
The student writes a code to be used in his or her own research under the supervision of one of the teachers.
Assessment
The course is assessed on a pass-fail scale based on evaluation of the code produced.
Teacher
Kelsey Flanagan or Ico Broekhuizen
Examinator
Annelie Hedström