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:
Loops can be used to automate data tasks in Python by iteratively executing the same code on multiple data structures. Practice using loops to automate certain functionality in Python.
A list comprehensions in Python is a type of loop that is often faster than traditional loops. Learn how to create list comprehensions to automate data tasks in Python.
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.
MODIS is remote sensing data that is stored in the HDF4 file format. Learn how to open and use MODIS data in HDF4 form in Open Source Python.
MODIS is remote sensing data that is stored in the HDF4 file format. Learn how to view and explore HDF4 files (and their metadata) using the free HDF viewer provided by the HDF group.
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 .
Loops can be an important part of creating a data workflow in Python. Use loops to go from raw data to a finished project more effeciently.
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.
Learn how to setup git locally on your computer.
Learn how to open up a multi-band raster layer or image stored in .tiff format in Python using Rasterio. Learn how to plot histograms of raster values and how to plot 3 band RGB and color infrared or false color images.
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.
Sometimes you want to customize the colorbar and range of values plotted in a raster map. Learn how to create breaks to plot rasters in Python.
Folium is a Python package that can be used to create interactive maps in Jupyter Notebook. Learn how to create interactive maps with raster overlays in Python using Folium.
Layer a raster dataset over a hillshade in Python to create a beautiful basemap that represents topography.
A hillshade is a representation of the earth's surface as it would look with shade and shadows from the sun. Learn how to overlay raster data on top of a hillshade in Python.
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
You often want to understand the relationships between two different types of data. Learn how to use regression to determine whether there is a relationship between two variables.
To explore uncertainty in remote sensing data, it is helpful to compare ground-based measurements and data that are collected via airborne instruments or satellites. Learn how to create scatter plots that compare values across two datasets.
For many scientific analyses, it is helpful to be able to select raster pixels based on their relationship to a vector dataset (e.g. locations, boundaries). Learn how to extract data from a raster dataset using a vector dataset.
Uncertainty quantifies a range of values within which a measurement value could be within, considering a specified level of confidence. Learn about the types of uncertainty that you can expect when working with tree height data both derived from lidar remote sensing and human measurements and learn about sources of error including systematic vs. random error.