Nathan Korinek

Nathan Korinek has contributed to the materials listed below. Nathan is a software developer with the Earth Analytics Education Initiative at Earth Lab

Course Lessons

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 in Python Exercise

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.

Introduction to List Comprehensions in Python: Write More Efficient Loops

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.

Activity: Plot Spatial Raster Data in Python

Practice your skills creating maps of raster and vector data using open source Python.

Activity: Plot Time Series Data Using Pandas in Open Source Python

Practice your skills plotting time series data stored in Pandas Data Frames in Python.

File Formats Exercise

Complete these exercises to practice the skills you learned in the file formats chapters.

Introduction to Spatial Vector Data File Formats in Open Source Python

Vector data is one of the two most common spatial data types. Learn to work with vector data for earth data science.

Use Raster 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.

Spatial Data Formats 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.

Open and Use MODIS Data in HDF4 format in Open Source Python

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.

Introduction to the HDF4 Data Format - Explore H4 Files Using HDFView

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.

Find and Download MODIS Data From the USGS Earth Explorer Website

Learn how to find and download MODIS data from the USGS Earth Explorer website.

Practice Opening and Plotting Landsat Data in Python Using Rasterio

A set of activities for you to practice your skills using Landsat Data in Open Source Python.

Open and Crop Landsat Remote Sensing 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.

Summary Activity for Time Series Data

An activity to practice all of the skills you just learned in .

Create Data Workflows with Loops

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.

Use the OS and Glob Python Packages to Manipulate File Paths

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.

Working Directories, Absolute and Relative Paths and Other Science Project Management Terms Defined

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.

Python Fundamentals Exercise

Complete these exercises to practice the skills you learned in the Python fundamentals chapters.

Basic Operators in Python

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.

Lists 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 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.

Use Tabular Data for Earth Data Science

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.

How To Setup Git Locally On Your Computer

Learn how to setup git locally on your computer.

Learn to Use NAIP Multiband Remote Sensing Images in Python

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.

Overlay Raster and Vector Spatial Data in A Matplotlib Plot Using Extents in Python

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.

Customize Matplotlib Raster Maps in Python

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.

Interactive Maps 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.

Plot Spatial Raster Data 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.

Reproject Raster Data 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.

Test Your Skills: Open Raster Data Using RioXarray In Open Source Python

Challenge your skills. Practice opening, cleaning and plotting raster data in Python

Use Regression Analysis to Explore Data Relationships & Bad Data

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.

Compare Lidar to Measured Tree Height

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.

Extract Raster Values at Point Locations in Python

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.

Compare Lidar With Human Measured Tree Heights - Remote Sensing Uncertainty

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.

Data tutorials

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