Sometimes the hardest part of getting started with coding is to determine which is the best software to learn or use! The goal of this session is to provide a basic introduction to three commonly-used tools for data management and analysis and to provide examples of how they can be used for managing data, visualization, exploiting cloud resources, generating metadata, using or creating web services, manipulating XML documents, and facilitating reorganization of data.
A panel will provide brief overviews of R, Python, and Jupyter Notebooks, including examples of what they do best, drawn from real-world applications. Workshop attendees will be encouraged to participate in discussions of data challenges they have encountered and the relative merits of the different tools in meeting them. Participation in the session by coders experienced in one or more of the tools is encouraged, as is participation by those who have yet to use any of these very powerful tools.
NOTES:
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Session recording is here.Presenter: Colin Smith
Presentation Title: Getting Things Done with R
Slides: https://doi.org/10.6084/m9.figshare.9450371Presenter: John Porter
Presentation Title: Introduction to Python
Slides:https://doi.org/10.6084/m9.figshare.9450617Presenter: Stace Beaulieu
Presentation Title: How we are using Jupyter Notebooks in the Northeast U.S. Shelf (NES) LTER
Slides: https://doi.org/10.6084/m9.figshare.9450875Session Take-Aways- Python is more widely used than R.
- The visualization features incorporated into Jupyter Notebooks are really valuable for scientists and outside users.
- Trying to run R through Jupyter Notebooks can be a challenge but potentially the Jupyter Lab approach could help.
