BIO-8027 Scientific Programming with Python in the life sciences - 10 stp

• Introduction to computing and Python
• The command line, Interactive shell, Scripts
• Basics, variables, string handling
• Functions & control flow
• Object-Oriented Programming
• File in- and output
• Error handling
• Libraries and foreign code
• Commonly used packages
• Jupyter Notebooks
  • Data handling with Pandas and SciPy
  • Plotting with Matplotlib and Seaborn
  • Sequence analysis with Biopython
  • Text search with Regular Expressions
  • Generally useful packages
• Using Blast with own code
• Best practices: effective and efficient coding
• Maintainable coding, testing, and debugging
• Resources for Python programmers 

Who can apply as a singular course student:

•  PhD student enrolled at another institution than UiT. PhD students must upload a document from their university stating that there are registered PhD students. This group of applicants does not have to prove English proficiency and are exempt from semester fee.
• Holders of a master´s degree of five years or 3+2 years (or equivalent) may be admitted. These applicants must upload a Master´s Diploma with Diploma Supplement / English translation of the diploma. Applicants from listed countries must document proficiency in English. To find out if this applies to you, see the following list: Proficiency in English must be documented - list of countries. For more information on accepted English proficiency tests and scores, as well as exemptions from the English proficiency tests, please see the following document:  Proficiency in english - PhD level studies
  

The course will be arranged with a maximum of 20, and minimum of 3 students.
If more than 20 applicants, priority will be given as follows:
1. Participants admitted to the PhD programme at UiT
2. Participants in the Associate Professor programme (Førstelektorprogrammet)
3. PhD candidates from other universities
4. People with a minimum of a Masters degree (or equivalent), who have not been admitted to a PhD programme.

Knowledge

• Understand the core principles of the Python programming language
• Apply common scientific packages in Python
• Understand common strategies to solve problems
• Apply strategies to familiarize themselves with new techniques and tools •Understand criteria for good documentation
• Understand the need for maintenance of code
• Understand factors that make code efficient, maintainable, and clean
• Know how to find resources for further study and skill development

Skills:

• Dissect larger data sets
• Isolate and solve complex problems
• Identify core challenges of data analysis tasks
• Build and manage larger data analysis projects
• Develop programming-based problem solving skills •Reflect on own thinking and engineering
• Understand and extend existing code
• Build simple data analysis pipelines
• Explain created code
• Demonstrate an understanding of testing

Competences:

• Rephrase scientific problems as computational problems
• Automate everyday tasks
• Plan computational work
• Define coding problems of appropriate difficulty
• Build logical and systematic thinking

Homework project where the participants will be required to create a short bioinformatics pipeline to analyze a larger data set and document the pipeline accordingly. Primary evaluation criteria are functionality and reproducibility of the pipeline as well as code documentation. Coding practices in light of readability, maintainability and scientific quality are also considered.
Grading for the homework exam is pass/fail. Participants have 4 weeks to complete homework project.

Work requirement:

• Actively participate in at least 80% of the sessions.
• Completion of data analyses problems

Re-sit exam:

There will be a re-sit examination for students that did not pass the previous ordinary examination.