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

TeSSHub makes use of some necessary cookies to provide its core functionality. Additionally, we make use of Google Analytics to discover how people are using TeSSHub 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
    • Python17
    • Python program17
    • Python script17
    • py17
    • Active learning1
    • Data rendering1
    • Data visualisation1
    • Ensembl learning1
    • Kernel methods1
    • Knowledge representation1
    • Machine learning1
    • Neural networks1
    • Open science1
    • Recommender system1
    • Reinforcement learning1
    • Supervised learning1
    • Unsupervised learning1
    • Show N_FILTERS more
    • Content provider
    • PaN Training23
    • Show N_FILTERS more
    • Keyword
    • Python19
    • Data science4
    • Shiny3
    • Artificial intelligence2
    • Computing1
    • Data management1
    • Data visualization1
    • Ewoks1
    • FAIR data1
    • Machine learning1
    • Materials Science1
    • Open Science1
    • Protein structure1
    • Reproducibility1
    • SIRIUS beamline1
    • Simulations1
    • Version control1
    • Workflows1
    • jupyter notebook1
    • labbook1
    • login required1
    • tutorial1
    • workflows1
    • Show N_FILTERS more
    • Competency level
    • Not specified
    • Beginner2
    • Show N_FILTERS more
    • Licence
    • MIT License
    • License Not Specified1648
    • Creative Commons Attribution Share Alike 4.0 International1596
    • Creative Commons Attribution 4.0 International1285
    • Creative Commons Attribution Non Commercial No Derivatives 4.0 International776
    • Creative Commons Attribution Non Commercial Share Alike 4.0 International670
    • Creative Commons Attribution Non Commercial 4.0 International354
    • Creative Commons Attribution No Derivatives 4.0 International246
    • Creative Commons Zero v1.0 Universal41
    • GNU General Public License v3.0 only3
    • Apache License 2.02
    • BSD 3-Clause "New" or "Revised" License2
    • Show N_FILTERS more
    • Target audience
    • Chemists1
    • Materials scientists1
    • Physicists1
    • software developers1
    • Show N_FILTERS more
    • Author
    • Sundeep Agarwal3
    • posit-dev3
    • Scott Reed2
    • @pydantic1
    • @rstudio @UBC-DSCI @UBC-MDS1
    • Alex Hall1
    • Aliaksandr V. Yakutovich1
    • Andres Ortega-Guerrero1
    • AstraZeneca1
    • Carlo A. Pignedoli1
    • Christopher J. Sewell1
    • Daniel Chen1
    • Daniel Hollas1
    • Deborah Prezzi1
    • Decathlon Digital - @dktunited1
    • Dou Du1
    • Edan Bainglass1
    • Elisa Molinari1
    • Giovanni Pizzi1
    • Google1
    • Gray Lab1
    • Greg Malcolm1
    • Guillaume Gautier1
    • Ifeanyi J. Onuorah1
    • Iurii Timrov1
    • Jake Vanderplas1
    • Junfeng Qiao1
    • Jusong Yu1
    • Lorenzo Bastonero1
    • Marnik Bercx1
    • Michael A. Hernández-Bertrán1
    • Miki Bonacci1
    • Nataliya Paulish1
    • Nicola Marzari1
    • Oleksii Trekhleb1
    • Peter N. O. Gillespie1
    • Pietro Bonfà1
    • Roberto De Renzi1
    • S.Lott1
    • Sebastiaan P. Huber1
    • Sergio Martínez Cuesta1
    • The Algorithms1
    • Timo Reents1
    • Uber1
    • Xing Wang1
    • chemillusion.com1
    • zemZemTrainingOrg1
    • Show N_FILTERS more
    • Contributor
    • Sundeep Agarwal3
    • posit-dev3
    • Scott Reed2
    • @pydantic1
    • @rstudio @UBC-DSCI @UBC-MDS1
    • Alex Hall1
    • AstraZeneca1
    • Daniel Chen1
    • Decathlon Digital - @dktunited1
    • Google1
    • Gray Lab1
    • Greg Malcolm1
    • Guillaume Gautier1
    • Jake Vanderplas1
    • Oleksii Trekhleb1
    • S.Lott1
    • Sergio Martínez Cuesta1
    • The Algorithms1
    • Uber1
    • chemillusion.com1
    • zemZemTrainingOrg1
    • Show N_FILTERS more
    • Resource type
    • jupyter notebook2
    • git1
    • service1
    • Show N_FILTERS more
    • Related resource
    • Documentation1
    • Show N_FILTERS more
    • Status
    • Active1
    • 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
  • Harvest using OAI-PMH

Email Subscription

Harvest using OAI-PMH

Exchange content using OAI-PMH

Use an OAI-PMH compatible tool to harvest metadata using the OAI-PMH endpoint. In particular, this endpoint can be used for exchanging content between different TeSS instances. See the TeSS documentation for more details.

Register training material

Competency level: Not specified

and Licence: MIT License

23 materials found
  • guilgautier/sdia-python

    Python script Python Version control
  • learnbyexample/100_page_python_intro

    Python script Python
  • learnbyexample/py_regular_expressions

    Python script Python
  • alexmojaki/futurecoder

    Python script Python
  • learnbyexample/practice_python_projects

    Python script Python
  • TheAlgorithms/Python

    Python script Python
  • jakevdp/PythonDataScienceHandbook

    Python script Data science Python
  • trekhleb/learn-python

    Python script Python
  • gregmalcolm/python_koans

    Python script Python
  • Learning Pathway: FAIR Data Management

    Open science Data management FAIR data Open Science Reproducibility
  • 1
  • 2
  • 3
Training eSupport System
[email protected]
Contribute
About TeSSHub
Browse Spaces
Funding & acknowledgements
Privacy
Cookie preferences
Version: 1.5.1
Source code
API documentation
Bioschemas testing tool

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