Computation for Biologists
Course infoSchedule
Course moduleNWI-BM066A
Credits (ECTS)6
Category03 (Advanced)
Language of instructionEnglish
Offered byRadboud University; Faculty of Science; BioWetenschappen;
PreviousNext 1
prof. dr. G.J.C. Veenstra
Other course modules lecturer
prof. dr. G.J.C. Veenstra
Other course modules lecturer
prof. dr. G.J.C. Veenstra
Other course modules lecturer
Contactperson for the course
prof. dr. G.J.C. Veenstra
Other course modules lecturer
dr. T. Yu
Other course modules lecturer
Academic year2023
KW1  (04/09/2023 to 05/11/2023)
Starting block
Course mode
Registration using OSIRISYes
Course open to students from other facultiesYes
Waiting listNo
Placement procedure-

At the end of this course you can:

  • analyze a biological question and implement an integrative computational analysis to test a biological hypothesis;
  • write a simple Python program to read and process big, text-based data files;
  • design and implement simple algorithms to gain biological insights from data;
  • use Python to perform exploratory analysis of high-dimensional data;
  • visualize complex biological data and analysis results at the genomic level to understand biological systems.

Technological advances in the fields of genomics and proteomics have accelerated the ease and speed of data collection. High-throughput instruments, such as DNA sequencers and mass spectrometers, generate large amounts of biological measurements. This has brought the goal of understanding gene regulation within a living cell at the systems levels much closer. However, to integrate and analyze these various big data sets, a quantitative approach to biology is needed.

In this course you will learn to break down a problem in logical parts that are appropriate for the data to be analyzed. In this course, you will apply the Python programming language and the Pandas data analysis framework in a biological context, focusing mostly on (epi)genomic, proteomic and metabolomic data. The course will deal with a complete view of data analysis, from problem analysis and reading and processing raw data files, to interpretation and visualization within the biological context.


  • Programming in Python
  • Jupyter notebooks
  • Lists & dictionaries
  • Control flow & loops
  • Files
  • Scripts
  • Debugging
  • Functions and modules
  • The pandas module
  • Visualization

Instructional Modes
  • Lecture
  • Self-study
  • Computer practical
Problem analysis: Intermediate (master level year 1)
Coding: Basic

For students without coding experience: The course is suitable for students without prior coding experience. It starts with basic coding exercises, aiming to teach basic coding skills. A few weeks into the course, the pace picks up.
Students with coding experience: The first few weeks appear easy, but appearances can be misleading. After a few weeks the pace picks up and students may wish to spend serious time on biological problem analysis, which requires analytical skills and biological insight, in addition to basic programming skills.
Presumed foreknowledge
  • Knowledge of the principles of genome architecture and gene regulation (NWI-BB064B or Lodish 7th edition chpt 5-7 or equivalent)
  • Basic statistics (NWI-MOL028 or equivalent)
  • Practical knowledge of Linux and the Bash shell and the ability to use the command-line to manipulate common genomics data files (NWI-BB086 or the on-line edX course “Introduction to Linux” or equivalent).
  • Test information
    - (computer) exam, 100%
    Required materials
    Access to lecture slides and online coursebook will be provided during the course (via Brightspace)

    Instructional modes
    computer practical
    Attendance MandatoryYes

    Attendance MandatoryYes

    Test weight1
    Test typeExam
    OpportunitiesBlock KW1, Block KW2