FBB3110 Computational Python 5.0 credits

• Python and its relationship to other programming languages
• Programming environments for Python
• Version control with git
• Software testing with pytest
• Data science libraries: numpy, scipy, pandas, matplotlib
• Interfaces to compiled languages
• Relational and document-oriented databases with Python
• Methods in Python for concurrent programming: threading, multiprocessing, and asynchronous methods

After having completed the course the student will be able to

• write programs in Python to solve problems in computational science
• apply best practices in programming with respect to version control and testing
• choose and apply data-science libraries relevant for their problem domain
• account for and apply established programming ethical guidelines

The course follows a model established by the Swedish e-Science Education (SeSE), of which it is part. It consists of three parts, a preparation phase, a full week of lectures and exercises, and a project phase. A project presentation will normally consist of a general background to the scientific area and a demonstration of how a specific problem have been addressed with skills obtained in the course.

Eligible for studies at the third-cycle level.

Basic proficiency with a command-line interface (e.g. Unix)

If the course is discontinued, students may request to be examined during the following two academic years.

• PRO1 - Project assignment, 5.0 credits, grading scale: P, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

The examination is done in terms of a project the student chooses that is relevant for their research area.

At least 80 % presence

Approved written project report and oral presentation in group.

Olav Vahtras

• All members of a group are responsible for the group's work.
• In any assessment, every student shall honestly disclose any help received and sources used.
• In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

Further information about the course can be found on the Course web at the link below. Information on the Course web will later be moved to this site.

Target audience: PhD student of coputational sciences

Number of students: 20

It is an on-campus course assuming that the pandemic situation does not require online.