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

Svensk titel

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