Training materials
Difficulty level: Intermediate
and Scientific topics: Toxicology or Transcriptomics or Software engineering or Computational biology
-
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 -
Learning pathway
Version control with Git
•• intermediateSoftware engineering Open science Version control -
Tutorial, Presentation
Materials from 'Introduction to High Performance Computing for Life Scientists' course
•• intermediateComputational biology HPC GPU parallel computing -
e-learning
High Performance Computing in Life Sciences
•• intermediateComputational biology High performance computing Data storage -
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