Training materials
Difficulty level: Intermediate
and Scientific topics: Data submission, annotation, and curation or Literature and language or Natural language processing or Software engineering or Transcriptomics
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Bioinformatics, Computational Biology, Computer Science, Programming, Coding, Education, Data Science, Transcriptomics, Machine Learning
R for Data Science
•• intermediateBioinformatics Computational biology Machine learning Transcriptomics Computational Biology Coding Programming Data Science Data Analysis Computer Science Machine Learning -
Presentation
FAIRtracks and Omnipy – FAIRtracks interoperability story
•• intermediateData submission, annotation, and curation Data identity and mapping Data quality management Data governance Workflows Data handling -
Learning pathway
Version control with Git
•• intermediateSoftware engineering Open science Version control -
hands-on tutorial
Bulk RNASeq analysis
•• intermediateTranscriptomics Gene expression Differential gene expression profiling Expression analysis Data analysis NGS RNASeq transcriptomics -
hands-on tutorial
Hands-on for 'Python - Coding Style' tutorial
•• intermediateSoftware engineering data-science jupyter-notebook -
hands-on tutorial
Hands-on for 'Virtual Environments For Software Development' tutorial
•• intermediateSoftware engineering data-science jupyter-notebook -
hands-on tutorial
Hands-on for 'Python - Type annotations' tutorial
•• intermediateSoftware engineering data-science jupyter-notebook -
hands-on tutorial
Hands-on for 'Conda Environments For Software Development' tutorial
•• intermediateSoftware engineering data-science conda jupyter-notebook -
hands-on tutorial
Hands-on for 'Python - Testing' tutorial
•• intermediateSoftware engineering data-science jupyter-notebook -
hands-on tutorial
Hands-on for 'Visualization of RNA-Seq results with Volcano Plot in R' tutorial
•• intermediateTranscriptomics transcriptomics interactive-tools