Jenny PalominoJenny Palomino has contributed to the materials listed below. Jenny is an earth data science course developer and instructor 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. Jenny Palomino has contributed to the following lessons:
This lesson teaches you how to control the flow of your code using conditional statements.
This lesson describes the structure of conditional statements in Python and demonstrates how they are used for writing DRY code.
This lesson describes how to automate tasks with loops in Python.
This lesson describes the structure of loops in Python and how they are used to iteratively execute code.
This lesson describes the DRY (i.e. Do Not Repeat Yourself) principle and lists key strategies for writing DRY code in Python.
This lesson walks you through using indexing to select data from pandas dataframes.
This lesson walks you through describing, manipulating, and plotting pandas dataframes.
This lesson walks you through importing tabular data from .csv files to pandas dataframes.
This lesson describes key characteristics of pandas dataframes, a data structure commonly used for scientific data.
This activity provides an opportunity to practice working with commonly used Python data structures for scientific data: lists, numpy arrays, and pandas dataframes.
This lesson walks you through manipulating, summarizing and plotting numpy arrays.
This lesson walks you through importing text data from .txt and .csv files into numpy arrays.
This lesson describes the key characteristics of a commonly used data structure in Python for scientific data: numpy arrays.
This lesson teaches you how to submit pull requests on Github.com to suggest changes to another repository.
This lesson teaches you how to implement version control using Git and GitHub.
This lesson teaches you how to add Markdown to Jupyter Notebook files.
This lesson teaches you how to manage your Jupyter Notebook files and directories.
This lesson reviews the process and benefits of version control and how Git and GitHub support version control.
Matplotlib is one of the most commonly used packages for plotting in Python. This lesson covers how to create a plot and customize plot colors and label axes using matplotlib.
Python packages are organized directories of code that provide functionality such as plotting data. Learn how to write Python Code to import packages.
This lesson walks you through creating and editing Python lists.
Variables store data (i.e. information) that you want to re-use in your code (e.g. a single value, list of values, path to a directory, filename). Learn how to write Python Code to work with variables.
This lesson covers how to crop a raster dataset and export it as a new raster in Python
In this lesson you review how to dissolve polygons in python. A spatial join is when you assign attributes from one shapefile to another based upon its spatial location.
Sometimes two shapefiles do not line up properly even if they cover the same area because they are in different coordinate reference systems. Learn how to reproject vector data in Python using geopandas to ensure your data line up.
Jupyter Notebooks is an interactive environment where you can write and run code and also add text that describes your workflow using Markdown. Learn how to use Jupyter Notebook to run Python Code and Markdown Text.
GitHub can be used to store and access files. Learn how to create a copy of files on GitHub (forking) and to use the Terminal to download the copy to your computer (cloning).
This lesson walks you through using Bash/Shell to navigate and manage files and directories on your computer.
This lesson reviews the importance and benefits of open reproducible science.