# What to Expect 

The PACE hackweek will 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.

## Typical Workflows and Tools

Here are a few specific scenarios of how hackweek participants will engage
with data science tools:

* Connecting to a [Jupyter Notebook](https://jupyter.org/) environment and
  accessing content for tutorial training.
* Accessing cloud-hosted remote sensing data using earthaccess and plotting it
 using matplotlib.
* Exploring multi-dimensional remote sensing data using xarray.
* Opening CSV tabular data in Pandas and run tools to conduct satellite matchups.
* Modifying code, committing it to Git and pushing changes to GitHub, for
  others on your team to view and edit.
* Exploring methods for high performance computing such as using Dask and parallelization
* Preparing datasets for machine learning tools, including PyTorch for neural networks

These are examples of the types of activities we will do at the PACE 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 mentors, 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.


All tutorials and project work will assume participants are somewhat familiar with Python-based
computing in the geosciences. For participants wishing to brush up on their skills before
the event, we recommend viewing the resources as described on the 
[Pythia Foundations](https://foundations.projectpythia.org/landing-page.html) website.