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DTSTAMP:20260616T143555Z
UID:da1043b2-5fcc-4f7e-9c66-0f4eaef2f7c9
DTSTART:20201019T090000Z
DTEND:20201023T170000Z
DESCRIPTION:Educators:\nRenato Alves (HD-HuB)\n\nDate:\n19-10-2020 - 23-10-
 2020\n09:00-18:00\n\nLocation:\nOnline\n\nContents:\nComputation is an int
 egral part of today's research as data has grown too large or too complex 
 to be analysed by hand. An ever-growing fraction of science is performed c
 omputationally and many wet-lab biologists spend part of their time on the
  computer. Many scientists struggle with this aspect of research as they h
 ave not been properly trained in the necessary set of skills. The result i
 s that too much time is spent using inefficient tools when progress could 
 be faster. This course provides training in several key tools\, with a foc
 us on good development practices that encourage efficient and reproducible
  research computing.\n\nTopics covered include:\n\n    Introduction to Pyt
 hon scripting\n    Introduction to the Unix shell and usage of cluster res
 ources\n    Version control with Git and Github\n    Analysis pipeline man
 agement\n    Scientific Python &amp\; working with biological data\n    Li
 terate programming with Jupyter notebooks\n\nLearning goals:\nThis course 
 aims to teach software writing skills and best practices to researchers in
  biology who wish to analyse data\, and to introduce a toolset that can he
 lp them in their work. The goal is to enable them to be more productive an
 d to make their science better and more reproducible.\n\nPrerequisites:\nT
 his is a course for researchers in the life sciences who are using compute
 rs for their analyses\, even if not full time. The target student will be 
 familiar with some command line/programmatic computer usage\, will want to
  become more confident using these tools efficiently and reproducibly. A t
 arget student will have written a for loop in some language before\, but w
 ill not know what git is (or at least not be very comfortable using git).\
 n\nKeywords:\nProgramming\; Command Line\; Version Control\; Bioinformatic
 s\; Data Analysis\; Cluster Computing\n\nTools:\nPython\; Bash\; Unix/Linu
 x\; Git\; GitHub\; SnakeMake\; Biopython\; Pandas\; Numpy\; SciPy\; Matplo
 tlib
LOCATION:Heidelberg
SUMMARY:Computing Skills for Reproducible Research: Software Carpentry Cour
 se 2020
URL;VALUE=URI:https://www.denbi.de/training/789-software-carpentry-course-2
 020
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