This course is an introduction to the major categories of datasets likely to be encountered by environmental data science professionals, as well as common online repositories and access methods including API servers and cloud computing platforms. All coding exercises make use of the Python programming environment.
By the end of the course, students should be able to:
- Locate Environmental Datasets on major cloud computing platforms (Google Earth Engine) and API servers
- Read In Datasets from these platforms onto a local machine
- Perform Quality Control as needed, including infilling or interpolation of missing data
- Subset/Aggregate Data according to the needs of a given application (spatial/temporal averaging, upsampling, etc)
- Visualize Data including through creation of georeferenced maps, time series, and other basic metrics
- Describe Data Pros/Cons: which datasets are most appropriate for a given application, and why?
- Work with Peers to create use case examples for datasets, and communicate to the rest of the class.
The following are some suggested instructions for setting up this repository. You can also feel free to fork or clone to your local machine!
Setup on the Bren Taylor Server
- Log into Taylor and create a session.
The Taylor server is located at http://taylor.bren.ucsb.edu. You must either be on campus or connected to the UCSB VPN in order to log in.
Log in using your Bren username and password, and you will find yourself in the "RStudio Workbench" screen. Click the blue "New Session" icon to create a session: make sure to select either "JupyterLab" or "Jupyter Notebook" from the drop-down menu. I recommend JupyterLab since the interface has more flexibility!
- Fork the class repository to your Github account
Fork the repository, so that you have your own copy of everything to work with. This can be accomplished by clicking the "Fork" button in the upper-right hand corner of the class repo page.
- Clone the repository to your home directory on Taylor
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Open a Terminal window: either using a Terminal application on your computer or via JupyterLab. In JupyterLab, you can open a terminal by choosing "File -> New -> Terminal".
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Type git clone and the the url for your forked copy of this repository.