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

The first week introduces the participants to basic computation in Python. It includes all the basics necessary to get started writing working Python code. Programming concepts and techniques in Python are introduced with plentiful exercises gleaned, as far as possible, from the scientific praxis. After the first week the participants will have a good understanding of general computation in Python. They will have also completed some simpler projects. The second week then further introduces students to the most common aspects and tasks of scientific coding. Participants learn to use many of Python’s scientific packages in realistic settings. Exercises again will mostly be taken from the life sciences. Lastly, students shortly learn about the most important good coding practices. These include needs for documentation and maintainability, as well as techniques for quality assurance. The more detailed sections of the course are:

• 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

The course consist of 2 weeks active participation and ~ 3 week full-time (120 hours) working on the project. Course includes ca. 40 hours of lectures and ca. 40 hours computer practical and 20 hours of course preparation.

Home exam 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.

Participants have 4 weeks to complete home exam.