Introduction, course material

Welcome to Python Programming for Biologists! [PPB Feb-Mar 2015]The course begins this Thursday (5th Feb, 2015) 10:00AM at Konorski Hall, 2nd floor – Nencki Institute of Experimental Biology.You are required to bring a laptop to the class. Please install Anaconda Scientific Python Distribution (Python 2.7 version) on your machine if you have not done it yet. This software is available for free. (Download from here:https://store.continuum.io/cshop/anaconda/) We shall use spyder development environment for this course.

Chaitanya
Szymon

Introduction, course material

Course commencement

Python Programming for Biologists course will introduce basics of programing, and will give you pointers on how to make a computer to do work for you. This course is designed to be of practical merit to biologists, we will cover a range of biology oriented/inspired problems. Some of these examples might already have specialized tools that are popular and in use. Taking this course will NOT necessarily equip you to replace such specialized tools, but will show you some of their inner workings.

The idea of the course is to give you a taste of programming, and not necessarily to address your specific research problem.

The course will last 8 weeks, with one 1.5-hour class every Thursday, at Nencki Institute of Experimental Biology (Konorski Hall, 2nd floor), Warsaw at 10AM. The classes will be hands-on programming sessions, and we plan weekly homework reading and programming assignments.

Preliminary list of topics we plan to cover:

  • Basics of Python, using Python console.
  • Reading and analysis of data from a text (CSV) file.
  • Basics of plotting, bar plots, scatter plots, histograms, legends, labels, titles.
  • Writing functions, handling multiple datasets.
  • Loading, manipulating and storing data in spreadsheets.
  • Basics of statistical analysis.
  • Manipulating text data.
  • Introduction to algorithms, basic image processing.
  • Handling errors in Python.
  • Approaching new problems, using Python help.
  • Good programing practices. Version control.

Planned examples include:

  • handling CSV files, .mat files, spreadsheets (data organizing/processing),
  • spatial location of an animal in a plain field (image processing),
  • analyzing lyrics of popular songs (string search/manipulation),
  • analysis of bird songs (audio processing, spike trains),
  • grid cell activity (advanced plotting),
  • and more.

Chaitanya Chintaluri
Szymon Łęski

Laboratory of Neuroinformatics,
Nencki Institute of Experimental Biology.

Course commencement