Nathan KorinekNathan Korinek has contributed to the materials listed below. Nathan is a software developer with the Earth Analytics Education Initiative at Earth Lab
Course lessons are developed as a part of a course curriculum. They teach specific learning objectives associated with data and scientific programming. Nathan Korinek has contributed to the following lessons:
Practice your skills creating maps of raster and vector data using open source Python.
Practice your skills plotting time series data stored in Pandas Data Frames in Python.
Complete these exercises to practice the skills you learned in the file formats chapters.
Vector data is one of the two most common spatial data types. Learn to work with vector data for earth data science.
Raster data is one of the two most common spatial data types. Learn to work with raster data for earth data science.
Two of the major spatial data formats used in earth data science are vector and raster data. Learn about these two common spatial data formats for earth data science workflows in this chapter.
Learn how to find and download MODIS data from the USGS Earth Explorer website.
A set of activities for you to practice your skills using Landsat Data in Open Source Python.
Learn how to open up and create a stack of Landsat images and crop them to a certain extent using open source Python.
An activity to practice all of the skills you just learned in .
The os and glob packages are very useful tools in Python for accessing files and directories and for creating lists of paths to files and directories, respectively. Learn how to manipulate and parse file and directory paths using os and glob.
A directory refers to a folder on a computer that has relationships to other folders. Learn about directories, files, and paths, as they relate to creating reproducible science projects.
Complete these exercises to practice the skills you learned in the Python fundamentals chapters.
Operators are symbols in Python that carry out a specific computation, or operation, such as arithmetic calculations. Learn how to use basic operators in Python.
A Python list is a data structure that stores a collection of values in a specified order (or sequence) and is mutable (or changeable). Learn how to create and work with lists in Python.
Variables store data (i.e. information) that you want to re-use in your code (e.g. single numeric value, path to a directory or file). Learn how to to create and work with variables in Python.
Tabular data is common in all analytical work, most commonly seen as .txt and .csv files. Learn to work with tabular data for earth data science in this lesson.
When plotting raster and vector data together, the extent of the plot needs to be defined for the data to overlay with each other correctly. Learn how to define plotting extents for Python Matplotlib Plots.
When plotting rasters, you often want to overlay two rasters, add a legend, or make the raster interactive. Learn how to make a map of raster data that has these attributes using Python.
Sometimes you will work with multiple rasters that are not in the same projections, and thus, need to reproject the rasters, so they are in the same coordinate reference system. Learn how to reproject raster data in Python using Rasterio.
Challenge your skills. Practice opening, cleaning and plotting raster data in Python