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This page provides information and links to these two Python courses:

run by the Graduate School of Life Sciences of the University of Cambridge, UK.

Course description

Since February 2017, the original two days course, taught over many years (on GitHub since 2013), has been rewritten into two separate courses running over four days.

  • Introduction to solving biological problems with Python: This course provides a practical introduction to the writing of Python programs for the complete novice. Participants are lead through the core aspects of Python illustrated by a series of example programs. Upon completion of the course, attentive participants will be able to write simple Python programs and customise more complex code to fit their needs.

  • Data science in Python: This course covers concepts and strategies for working more effectively with Python with the aim of writing reusable code. We will be using Python libraries to explore data in files, creating functions and modules to write reusable code, manipulating data using Pandas, visualising data with Matplotlib and working with biological data using BioPython.

Course materials

The course materials are available as Jupyter notebook files, one for each session of the course. Jupyter notebooks allow you to interactively run python code in your browser and if you install Jupyter on your own machine, you can then run the notebooks and experiment with the example code. A quick installation guide is also available in our introductory course repository on GitHub in the README.md file.

The notebook and example data files as well as scripts used in both courses are available to download from our course's repositories:

Static renderings of the notebooks (that do not support interactively running the examples) are also available (using the Jupyter notebook viewer) as a service from GitHub.

Web-based collaborative editor is used during some courses, allowing everyone to simultaneously ask questions by editing a text document, and see all participants' edits in real-time:

Course schedule

Course objectives

  • Introduction to solving biological problems with Python
    • Edit and run Python code
    • Write file-processing python programs that produce output to the terminal and/or external files
    • Create stand-alone python programs to process biological data
    • Know how to develop your skills in Python after the course
    • During this course you will learn about:
      • Core concepts about Python syntax: data types, variables, blocks and indentation, writing code in file
      • Different ways to control program flow using loops and conditional tests
      • Reading from and writing to files
  • Data science in Python
    • Writing reusable code, using functions and libraries
    • Acquiring a working knowledge of key concepts which are prerequisites for advanced programming in Python like writing classes to build objects
    • During this course you will learn about:
      • Using Python libraries to explore data in files
      • Creating functions and modules to write reusable code
      • Manipulating data using Pandas
      • Visualising data with Matplotlib
      • Working with biological data using BioPython

Exercise solutions

Example solutions to all of the exercises from the course materials are available from the course repository in the solutions folder. The solution scripts are named sequentially for each session.

Note that these solutions are just examples, and there are many 'correct' solutions to these exercises. If you spot any issues or bugs with the solutions, or indeed any of the course materials, please let us know (pull requests are welcome!)

Presenters

  • Current presenters:
    • Mukarram Hossain, University of Cambridge
    • Samuel Lewis, University of Cambridge
    • Sebastian Mueller, University of Cambridge
    • Anne Pajon, CRUK-CI, University of Cambridge
    • Fabio Puddu, Gurdon Institute, University of Cambridge
    • Cristian Riccio, University of Cambridge
  • Previous presenters:
    • Gabor Bunkoczi, CIMR, University of Cambridge
    • Tom Carroll, MRC Clinical Sciences Centre, Imperial College London
    • Tomás Di Domenico, University of Cambridge
    • Mareike Herzog, University of Cambridge
    • Maire Lawlor, Sanger Institute
    • David Molnar, University of Cambridge
    • Sergio Martinez Cuesta, CRUK-CI, University of Cambridge
    • James Morris, CRUK-CI, University of Cambridge
    • Graham Ritchie, Usher Institute of Population Health Sciences & Informatics, University of Edinburgh
    • Inês de Santiago, CRUK-CI, University of Cambridge
    • Tim Stevens, MRC Laboratory of Molecular Biology