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SUMMARY:Quick and clean: advanced Python for data science in biology - ONLINE
DESCRIPTION:National course open for PhD students\, postdocs\, researchers and other employees in need of Advanced Python skills within all Swedish universities. \n\n\n\nImportant dates\n\n\n\nApplication is open!Application closes: April 15\, 2021Confirmation to accepted students: April 21\, 2021For questions about the course\, please contact Ashfaq Ali (ashfaq.ali@nbis.se)\, Sergiu Netotea (sergiu.netotea@nbis.se)\n\n\n\nCourse fee\n\n\n\nThe 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. \n\n\n\n*Please note that NBIS cannot invoice individuals \n\n\n\nThe 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.  \n\n\n\nParticipants will have an opportunity to learn the following topics \n\n\n\nGeneral 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 machinesLearning 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!\n\n\n\nLearning Outcomes\n\n\n\nAt the end of the course the course participants will have achieved following objectives \n\n\n\nGeneral knowledge about computational workflow using pythonHave knowledge about computer architecture and use of python for efficient computingKnowledge about python libraries for machine learning\, deep learning and statistical learning and their applicationsAbility to apply advanced python libraries in own research field\n\n\n\nWorkshop organization\n\n\n\nWe 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. \n\n\n\nYou 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. \n\n\n\nImportant to Know\n\n\n\nThe 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. \n\n\n\nFor the sessions on analyses of your own data\, students are encouraged to send their topic of interest before the start of the course.   \n\n\n\nEntry requirements\n\n\n\nRequired for being able to follow the course and complete the computer exercises \n\n\n\nA 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)\n\n\n\nDesirable to have \n\n\n\nYou 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.\n\n\n\nDue 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. \n\n\n\nGithub page (older version)
URL:https://www.scilifelab.se/event/quick-and-clean-advanced-python-for-data-science-in-biology-online/
CATEGORIES:Course
LOCATION:https://www.scilifelab.se/event/quick-and-clean-advanced-python-for-data-science-in-biology-online/
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