Upcoming courses

Field Specific & Interdisciplinary Subjects

Course: Python in biology for beginners and advanced

The course will include exercises on python applications and packages dedicated to processing data often used in biology and medical applications. The preliminary plan is to cover the following subjects:

1. Using data tables, importing transforming, exporting data.

2. Use statistics (scipy, numpy). Scipy is a big library of all kinds of statistical functions and distributions (numpy)

3. Graphic tools (Matplotlib) . All kinds of scientific illustrations, like scatter, line, barchart, boxplot, heatmaps etc.

4. Basics with images, pixels analysis, image analysis (PIL Image). Useful for some cytological elements of analysis, like colocalization of red, green dots on the image, count cells from microscopic image.

5. DNA strings, motifs, codes (Biopython). Basic manipulation with different DNA files, finding motifs, generating random DNA, encoding/decoding algorithms.
6. Data clustering, multi-dimensional scaling (sklearn). How to make PCA, MDS, t-SNE and other plots of this kind using python. Brief check of clustering options.

Intended for:
PhD students in biology, medical biology, medicine or farmacy- those who professionally connected to biology. Students should understand a concept of programming and be ready to learn the programming language. Prior skills of programming are not required. Post-docs can also apply. Students should be willing to learn programming


Assumed pre-knowledge
General knowledge of (genome) biology is required. No prior knowledge of programming required, although elementary understanding of the script structure, the meaning, and some skills of writing will be an advantage. 


Equipment Students should have personal laptop with administrative rights (able to install software and packages) with 8 GM RAM, preferably Mac or Linux. Windows10 can be used, but with limitations. It is preferable to come with preinstalled Python3 (with Pycharm or Spyder or other IDE). Installation can be done from official python page or using anaconda. In exceptional cases it might be installed at the first meeting of the course


 Compulsory literature


Books are optional. Students will be referred to online documentation of each package


Expect weekly homework on discussed subjects. This will also form the basis to pass the course


100% participation required, final assignment required



Course schedule:

4 March  09:00-12:00

11 March  09:00-12:00

18 March  09:00-12:00

25 March  09:00-12:00

1 April  09:00-12:00

8 April  09:00-12:00

15 April  09:00-12:00


March 4–April 15, 2020



EC (without exam)


Location address

to be announced

Course coordinator

  • Leonid Bystrykh, PhD





Deadline for registration

February 18, 2020


Sorry, the registration period for this event is over.

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