Quick and clean: advanced Python for data science in biology – ONLINE
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Quick and clean: advanced Python for data science in biology – ONLINE
May 3, 2021 @ 08:00 – May 7, 2021 @ 17:00 CEST
National course open for PhD students, postdocs, researchers and other employees in need of Advanced Python skills within all Swedish universities.
Important dates
- Application is open!
- Application closes: April 15, 2021
- Confirmation to accepted students: April 21, 2021
- For questions about the course, please contact Ashfaq Ali (ashfaq.ali@nbis.se), Sergiu Netotea (sergiu.netotea@nbis.se)
Course fee
The course is free of charge but a no show fee course fee of 2000 SEK will be invoiced to accepted participants who failed to attend the course after accepting to participate.
*Please note that NBIS cannot invoice individuals
The main aim of this course is to introduce students to the so-called Zen of Python for quick and clean application of python in data science. The workshop is structured around based on the industry way of classifying big data jobs: data analytics, data science, data engineering.
Participants will have an opportunity to learn the following topics
- General overview of computer choke points for various architectures together with a fast paced tutorial on advanced language concepts.
- Scientific computing, statistics, visualization and data mining, via libraries such as numpy, pandas, statmodels and several other “science stack” libraries.
- Programming with focus on how to perform machine learning, deep learning, statistical learning and pattern recognition using python, via scikit-learn, tensorflow, pymc3 and other more exotic libraries.
- Engineering the computing infrastructure and Python’s role in it. How to run Python on clouds and GPU machines
- Learning how Python can be used to organize your workflow with efficiency and reproducibility in mind.
- Application to research themes where you will either pick one real ‘omics subject from a given task list or you will use Python in your project under our assistance. This is a great time to solidify your knowledge by applying it to your own research scope!
Learning Outcomes
At the end of the course the course participants will have achieved following objectives
- General knowledge about computational workflow using python
- Have knowledge about computer architecture and use of python for efficient computing
- Knowledge about python libraries for machine learning, deep learning and statistical learning and their applications
- Ability to apply advanced python libraries in own research field
Workshop organization
We aim for a balance between lecturing and exercise in Jupyter notebooks (jupyter.org) which is used for taking notes, self study, hands on tasks and interaction. Considering that the course is online, lectures will be delivered via zoom links and exercises will be carried out in zoom breakout rooms with the help of teaching assistants. Course session leaders will be available to answer theoretical and practical questions. Questions are welcome at any time.
You will be asked to prepare your laptop a week before the course starts. We will also use a slack channel for communication, posting links or code tips.
Important to Know
The workshop covers some of the basic concepts of python programming and each session will have advanced material on the topic that may test the limits of the participant’s knowledge of python and computers. Difficulties during learning are expected and are part of the course design.
For the sessions on analyses of your own data, students are encouraged to send their topic of interest before the start of the course.
Entry requirements
Required for being able to follow the course and complete the computer exercises
- A computer with any OS.
- Python, R or any other computer language basic knowledge.
- Basic skills handling your own computer.
- For those interested in tasks involving cloud computing, access to Amazon AWS is required. (user configuration)
Desirable to have
- You have bioinformatics or systems biology background, statistical and machine learning skills.
- Have Linux on your laptop, or access to a Linux server.
- You did programming before (not just courses) and can handle the command line.
- Have a good idea for a task you want to achieve on the fourth day.
Due to limited space the course can accommodate a maximum of 20 participants. If we receive more applications, participants will be selected based on several criteria including entry requirements, motivation to attend the course as well as gender and geographical balance.
Github page (older version)
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