Training eSupport System
  • Log In
    • Log in with LS Login
    • Login
    • Register
  • About
  • Events
  • Materials
  • e-Learning
  • Workflows
  • Collections
  • Learning paths
  • Directory
    • Providers
    • Nodes
    • Spaces

TeSS makes use of some necessary cookies to provide its core functionality. Additionally, we make use of Google Analytics to discover how people are using TeSS in order to help us improve the service. To opt out of this, choose the "Allow necessary cookies" option.

See our Privacy Policy for more information.

You can modify your cookie preferences at any time here, or from the link in the footer.

Allow necessary cookies Allow all cookies
  1. Home
  2. Materials

Filter

  • Sort

  • Filter Clear filters

    • Date added
    • In the last 24 hours
    • In the last 1 week
    • In the last 1 month
    • Scientific topic
    • Python program
    • Chromosome walking7
    • Clone verification7
    • Comparative transcriptomics7
    • DNA-Seq7
    • DNase-Seq7
    • High throughput sequencing7
    • High-throughput sequencing7
    • NGS7
    • NGS data analysis7
    • Next gen sequencing7
    • Next generation sequencing7
    • Panels7
    • Primer walking7
    • Sanger sequencing7
    • Sequencing7
    • Targeted next-generation sequencing panels7
    • Transcriptome7
    • Transcriptomics7
    • MicroRNA sequencing5
    • RNA sequencing5
    • RNA-Seq5
    • RNA-Seq analysis5
    • Small RNA sequencing5
    • Small RNA-Seq5
    • Small-Seq5
    • Transcriptome profiling5
    • WTSS5
    • Whole transcriptome shotgun sequencing5
    • miRNA-seq5
    • Active learning4
    • Bayesian methods4
    • Biostatistics4
    • Descriptive statistics4
    • Ensembl learning4
    • Gaussian processes4
    • Inferential statistics4
    • Kernel methods4
    • Knowledge representation4
    • Machine learning4
    • Markov processes4
    • Multivariate statistics4
    • Neural networks4
    • Probabilistic graphical model4
    • Probability4
    • Recommender system4
    • Reinforcement learning4
    • Single-cell genomics4
    • Single-cell sequencing4
    • Statistics4
    • Statistics and probability4
    • Supervised learning4
    • Unsupervised learning4
    • Genome annotation3
    • Exomes2
    • Genomes2
    • Genomics2
    • Integrative omics2
    • Multi-omics2
    • Multiomics2
    • Pan-omics2
    • Panomics2
    • Personal genomics2
    • Python2
    • Python script2
    • Synthetic genomics2
    • Variant pattern analysis2
    • Viral genomics2
    • Whole genomes2
    • py2
    • ChIP-exo1
    • ChIP-seq1
    • ChIP-sequencing1
    • Chip Seq1
    • Chip sequencing1
    • Chip-sequencing1
    • Comparative genomics1
    • Data rendering1
    • Data visualisation1
    • Enrichment1
    • Enrichment analysis1
    • Functional enrichment1
    • Functional genome annotation1
    • Metagenome annotation1
    • Network1
    • Over-representation analysis1
    • Pathway1
    • Pathway or network1
    • Pipelines1
    • R1
    • R program1
    • R script1
    • Software integration1
    • Structural genome annotation1
    • Tool integration1
    • Tool interoperability1
    • Workflows1
    • Show N_FILTERS more
    • Content provider
    • Glittr.org2
    • Show N_FILTERS more
    • Keyword
    • Python2
    • Data science1
    • Show N_FILTERS more
    • Competency level
    • Not specified2
    • Show N_FILTERS more
    • Licence
    • Creative Commons Attribution 4.0 International
    • MIT License1
    • Show N_FILTERS more
    • Author
    • SIB Swiss Institute of Bioinformatics
    • Data Science in Practice1
    • Kevin Heavey1
    • Show N_FILTERS more
    • Contributor
    • Robin Engler2
    • Wandrille D.2
    • Diana Marek1
    • Orlin Topalov1
    • SIB Git course1
    • Show N_FILTERS more
    • Node
    • Switzerland2
    • Show N_FILTERS more
  • Show materials from all spaces
  • Show disabled materials
  • Show materials with broken links
  • Show archived materials

Training materials

  • Subscribe via email

Email Subscription

Register training material

Scientific topics: Python program

and Licence: Creative Commons Attribution 4.0 International

and Authors: SIB Swiss Institute of Bioinformatics

2 materials found
  • sib-swiss/intermediate-python-training

    ELIXIR node event
    Python script Python Data science
  • sib-swiss/first-steps-with-python-training

    ELIXIR node event
    Python script Python
Training eSupport System
[email protected]
Contribute
About TeSS
Browse Spaces
Funding & acknowledgements
Privacy
Cookie preferences
Version: 1.5.1
Source code
API documentation
Bioschemas testing tool

TeSS has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 676559.