GSMS Course Registration

All GSMS courses

Only GSMS PhD students (including BCN PhD students) can participate in these courses. Use your P-number to register (ask your Personnel Office).

For registration please go to Upcoming courses.

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
Beginners level, no prior knowledge of programming required, although elementary understanding of the script 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. Windows can be used, but with limitations. R (with R-studio) and Python3 (with Pycharm) can be preinstalled, or will be installed during the course.

 

 Compulsory literature

 

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

Homework

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

 

Exam
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

EC (without exam)

1

Course coordinator

  • Leonid Bystrykh, PhD

Language

English

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