# What to Expect 

The {{ 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 ocean model datasets, with a focus
on the Pangeo ecosystem of Python tools for big data geoscience.
The details of implementation will be what you work out via peer-learning (helping each other) in
your project group.

## 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 ocean model data using xarray.
* Opening CSV tabular data in Pandas and run tools to conduct matchups with in-situ data.
* 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 and TensorFlow for neural networks and packages to fit regression trees.

These are examples of the types of activities we will do at the {{hackweek}} in a
collaborative setting. Be aware that most of the project work will be within self-organized project
teams. Much of the hackweek will be spent running code (via notebooks),
writing code and talking about code. The mentors and organizers will provide links to tutorials and 
help trouble-shoot code, but much of the learning comes from working on a project together.

All tutorials will be in Python using the Pangeo ecosystem of tools for computing in the earth sciences.
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. Teams are 
welcome to do their project in R and our compute platform fully supports R for 
earth science computation. The HackWeek mentors/helpers are experienced in Python, R and Matlab.

## HackWeek Projects

A good hackweek project is a concrete idea that a team can flesh out in a week together. Not everyone needs to code. There is background research to do, data to find, and lots and lots of data wangling. A big part of the fun of hackweek is working together with a group with a diverse set of interests and skills. "I'll find some data." "I make some maps of our study area." "I'll figure out how to do a boosted regression tree." "I'll use that tutorial we were shown and get xyz model data for our region." etc, etc. It is messy, but through this process you'll learn new skills and also get to know your project team mates.

The project work is a combination of 

* fleshing out a science idea that is small enough on Monday brainstorming.
* dividing up into tasks so that everyone can participate.
* coding and data wrangling on Tuesday through Thursday.
* and then Friday, frantically putting a presentation on your project and results.

Checkout projects from other hackweeks to get an idea of projects done in earth science hackweeks

* [PACE HackWeek 2024 projects](https://pacehackweek.github.io/pace-2024/projects/list_of_projects.html)
* [OceanHackWeek projects](https://oceanhackweek.org/ohw24_proj_catalog_us/OHW_project_table.html)
* [Geosmart HackWeek 2024 projects](https://geosmart-2024.hackweek.io/projects/index.html#list-of-projects)
* [OceanHackWeek 2025 projects](https://oceanhackweek.org/ohw25/projects/projects_thisyear.html)