Overview

Python is an open-source and general-purpose scripting language which runs on all major operating systems. It was designed to be easily read and written with comparatively simple syntax. Over the recent years Python has become a programming language of choice for bioinformatics and data analysis, and in particular for applications that make use of machine learning or deep-learning.
However, these applications usually require a good mastering of a few modules (such as numpy, or pandas) that can go beyond basic Python commands.
This 1-day course will introduce modules and recipes to unlock the potential of Python for day-to-day data exploration and analysis of real-life data-sets.

Topics that will be covered in this course include:
* Parsing, transforming, and exploring data using Pandas
* Performing statistical simulation and testing with Numpy/Scipy
* Representing data in an efficient and impactful manner using Seaborn
* Speeding-up your Python code with Numba and more

Audience

This course is addressed to life scientists, bioinformaticians and researchers who are familiar with writing Python code and core Python elements, and would like to explore further data wrangling and exploration tasks.

Learning outcomes

At the end of this course, the participants are expected to:
* Parse any tabulated data set in a couple of lines
* Summarize and perform quality control on their data
* Filter, sub-sample or aggregate specific parts of their data-set
* Generate clear visual representations to explore data and communicate their findings
* Implement best-practice as well as recipes to speed-up key bottlenecks in Python programs

Prerequisites

Knowledge / competencies

The course is targeted to life scientists, bioinformaticians, and researchers who are already familiar with the Python programming language and who have basic knowledge on statistics. The competences and knowledge levels required correspond to those taught in courses such as: First Steps with Python in Life Sciences, Introduction to statistics with Python and Introduction to statistics with R.
Test your skills with Python and statistics with the quiz here, before applying. We recommend 4 out of 6 correct answers.

Technical

You are required to use your own laptop, with a recent Python 3 version.
The following modules should be installed on your computer (using conda for example):
* Jupyter Notebook
* Pandas
* Seaborn
* Numba

Application

The registration fees for academics are 60 CHF and 300 CHF for for-profit companies.While participants are registered on a first come, first served basis, exceptions may be made to ensure diversity and equity, which may increase the time before your registration is confirmed.

Applications will close as soon as the places will be filled up. Deadline for free-of-charge cancellation is set to 18/05/2022. Cancellation after this date will not be reimbursed. Please note that participation in SIB courses is subject to our general conditions.

You will be informed by email of your registration confirmation. Upon reception of the confirmation email, participants will be asked to confirm attendance by paying the fees within 5 days.

Venue and Time

This course will be held in Bern.

The course will start at 9:00 and end around 17:00.

Precise information will be provided to the participants in due time.

Additional information

Coordination: Diana Marek

We will recommend 0.25 ECTS credits for this course (given a passed exam at the end of the course).

You are welcome to register to the SIB courses mailing list to be informed of all future courses and workshops, as well as all important deadlines using the form here.

Please note that participation in SIB courses is subject to our general conditions.

SIB abides by the ELIXIR Code of Conduct. Participants of SIB courses are also required to abide by the same code.

For more information, please contact training@sib.swiss.