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# Skills Refresher | ||
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Our hackweeks focus on applied, hands-on learning, with participants engaging in extended periods of small-group work. Our tutorials are designed to offer a broad snapshot of data science tools to support your applied investigations. Due to the relatively short duration of our events, we are not able to provide comprehensive, in-depth training in fundamental tools. Rather, our goal is to inform you about the types of tools we think are best suited to working with your datasets, leaving details of implementation to be supported through peer-learning and office hours. | ||
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## Typical Workflows and Tools | ||
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Here are a few specific scenarios of how hackweek participants typically engage | ||
with data science tools during an event: | ||
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* Connecting to a [Jupyter Notebook](https://jupyter.org/) environment and using | ||
the command line to pull lesson content for a morning of tutorial training. | ||
* Modifying a text file, committing it to Git and pushing changes to GitHub, for | ||
others on your team to view and edit. | ||
* Opening CSV tabular data in Pandas and using filtering functions to remove | ||
outliers. | ||
* Accessing a cloud-hosted remote sensing image using Rasterio and plotting it | ||
on an interactive map. | ||
* Exploring multi-dimensional climate grids using xarray. | ||
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These are examples of the types of activities we do at a hackweek in a | ||
collaborative setting. We invite you to reflect on your comfort level with tasks | ||
such as these so that you can arrive at the hackweek with a clarity on where to | ||
dedicate your energy. If wish to focus more energy on learning and implementing | ||
new tools, we will support you with helpers and office hours, and you may have a | ||
bit less time for applied group work. If you are already proficient in a lot of | ||
tools you may find you can dedicate more energy to applied project work, which | ||
we support through facilitated group activities. | ||
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## Software Carpentry Session | ||
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We strongly suggest that participants who are just beginning to learn Python and | ||
collaborative data science tools to complete our | ||
recorded ([Software Carpentry Training](./skills-refresher/swc)) in advance of the hackweek. You | ||
may choose whichever topics you'd like to brush up on or learn. | ||
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## Project Pythia | ||
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Another excellent resource for training and skills development is the [Project Pythia](https://projectpythia.org/) organization. Their lessons and galleries are developed specifically for the geoscience community. For a good skills refesher, we encourage you to work through their [Pythia Foundations](https://foundations.projectpythia.org/landing-page.html) notebooks. |
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# Supplemental Material | ||
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## Python | ||
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Dive deeper into how [Python is managed and installed](../reference/supplemental/python) on | ||
the JupyterHub and how you can install that on your personal machine. | ||
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## Conda | ||
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A basic intro to manage Python environments using the | ||
[conda package manager](../reference/supplemental/conda) |
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