Jenny Palomino

Jenny 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

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:

Clean Code Syntax for Python: Introduction to PEP 8 Style Guide

Using a standard format and syntax when programming makes your code easier to read. Learn more about PEP 8, a set of guidelines for writing clean code in Python.

Introduction to Writing Clean Code and Literate Expressive Programming

Clean code refers to writing code that runs efficiently, is not redundant and is easy for anyone to understand. Learn about the characteristics and benefits of writing clean, expressive code in Python.

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.

Introduction to the Python Scientific Programming Language for Earth Data Science

Python is a free, open source programming language that can be used to work with scientific data. Learn about using Python to develop scientific workflows.

Format Text In Jupyter Notebook With Markdown

Markdown allows you to format text using simple, plain-text syntax and can be used to document code in a variety of tools, including Jupyter Notebook. Learn how to format text in Jupyter Notebook using Markdown.

Text File Formats for Earth Data Science

There are many text file formats that are useful for earth data science workflows including Markdown, text (.txt, .csv) files, and YAML (Yet Another Markup Language). Learn about these common text file formats for earth data science workflows.

Data Wrangling With Numpy Arrays

This lesson teaches you how to wrangle data (e.g. run multi-task functions, combine) with numpy arrays.

Data Wrangling With Pandas

This lesson teaches you how to wrangle data (e.g. subselect, update, and combine) with pandas dataframes.

Apply Functions to Numpy Arrays

This lesson teaches you how to apply functions to numpy arrays in Python.

Write Custom Functions

This lesson teaches you how to write custom functions in Python.

Intro to Functions

This lesson describes how functions are used in Python to write DRY and modular code.

Control Flow Using Conditional Statements

This lesson teaches you how to control the flow of your code using conditional statements.

Intro to Conditional Statements

This lesson describes the structure of conditional statements in Python and demonstrates how they are used for writing DRY code.

Automate Tasks With Loops

This lesson describes how to automate tasks with loops in Python.

Intro to Loops

This lesson describes the structure of loops in Python and how they are used to iteratively execute code.

Intro to DRY code

This lesson describes the DRY (i.e. Do Not Repeat Yourself) principle and lists key strategies for writing DRY code in Python.

Selections From Pandas Dataframes

This lesson walks you through using indexing to select data from pandas dataframes.

Manipulate and Plot Pandas Dataframes

This lesson walks you through describing, manipulating, and plotting pandas dataframes.

Import CSV Files Into Pandas Dataframes

This lesson walks you through importing tabular data from .csv files to pandas dataframes.

Intro to Pandas Dataframes

This lesson describes key characteristics of pandas dataframes, a data structure commonly used for scientific data.

Useful Jupyter Notebook Shortcuts

The Jupyter ecosystem contains many useful tools for working with Python including Jupyter Notebook, an interactive coding environment. Learn useful shortcuts in Jupyter Notebook that can help you complete your tasks quickly and efficiently.

Manage Jupyter Notebook Files

The Jupyter ecosystem contains many useful tools for working with Python including Jupyter Notebook, an interactive coding environment, and the Jupyter Notebook dashboard, which allows you to manage files and directories in your Jupyter environment. Learn how to manage Jupyter Notebook files including saving, renaming, deleting, moving, and downloading notebooks.

Manage Directories in Jupyter Notebook Dashboard

The Jupyter ecosystem contains many useful tools for working with Python including the Jupyter Notebook dashboard, which allows you to manage files and directories in your Jupyter environment. Learn how to create, rename, move, and delete directories using the Jupyter Notebook dashboard.

Code and Markdown Cells in Jupyter Notebook

The Jupyter ecosystem contains many useful tools for working with Python including Jupyter Notebook, an interactive coding environment. Learn how to work with cells, including Python code and Markdown text cells, in Jupyter Notebook.

Get Started With Jupyter Notebook For Python

The Jupyter ecosystem contains many useful tools for working with Python including Jupyter Notebook, an interactive coding environment. Learn how to launch and close Jupyter Notebook sessions and how to navigate the Jupyter Dashboard to create and open Jupyter Notebook files (.ipynb).

Introduction to Jupyter For Python

The Jupyter ecosystem contains many useful tools for working with Python including Jupyter Notebook, an interactive coding environment. Learn how the components and functionality of Jupyter Notebook can help you implement open reproducible science workflows.

Bash Commands to Manage Directories and Files

Bash or Shell is a command line tool that is used in open science to efficiently manipulate files and directories. Learn how to run useful Bash commands to access and manage directories and files on your computer.

What is a Working Directory and Other Science Project Management Terms Defined

A directory refers to a folder on a computer that has relationships to other folders. Learn about the key terms associated with files and directories in a science project.

Introduction to Bash (Shell) and Manipulating Files and Directores at the Command Line

Bash or Shell is a command line tool that is used in open science to efficiently manipulate files and directories. Learn how to use Bash to manipulate files in support of reproducible science.

Using Python Conda Environments to Create Reproducible Workflows and to Manage Dependencies: Everything That You Need to Know

A conda environment is a self contained Python environment that allows you to run differen versions of Python on your computer. Learn how to create conda environments to support open reproducible science.

How To Organize Your Project: Best Practices for Open Reproducible Science

Open reproducible science refers to developing workflows that others can easily understand and use. Learn about best practices for organizing open reproducible science projects including the use of machine readable names.

Tools For Open Reproducible Science

Key tools for open reproducible science include Shell (Bash), git and GitHub, Jupyter, and Python. Learn how these tools help you implement open reproducible science workflows.

What Is Open Reproducible Science

Open reproducible science refers to developing workflows that others can easily understand and use. It enables you to build on others' work rather than starting from scratch. Learn about the importance and benefits of open reproducible science.

How to Process Many Files in Python - Manipulate Directories, Filenames and Strings

When automating workflows, it is helpful to be able to programmatically check for and create directories and to parse directory and file names to extract information. Learn how to manipulate directories and strings using Python.

Data Workflow Best Practices - Things to Consider When Processing Data

Identifying aspects of a workflow that can be modularized can help you design data workflows. Learn best practices for designing efficient data workflows.

How Do You Create a Data Workflow - Design and Develop a Workflow For NDVI Over Time

Designing and developing data workflows can complete your work more efficiently by allowing you to repeat and automate data tasks. Learn how to design and develop efficient workflows to automate data analyses in Python.

Introduction to Open Source Software - What Is It and How Can You Help?

Open source means that you can view and contribute to software code like packages you use in Python. Learn about the ways that you can contribute without being an expert progammer.

Undo Local Changes With Git

A version control system allows you to track and manage changes to your files. Learn how to undo changes in git after they have been added or committed to version control.

Git Commands for Version Control

A version control system allows you to track and manage changes to your files. Learn how to get started with version control using Git.

Copy (Fork) and Download (Clone) GitHub Repositories

GitHub.com can be used to store and access files in the cloud to share with others or simply as a backup of your local files. Learn how to create a copy of files on GitHub (fork) and to download files from GitHub to your computer (clone).

What Is Version Control

A version control system allows you to track and manage changes to your files. Learn benefits of version control for scientific workflows and how git and GitHub.com support version control.

Activity on Dry Code

This activity provides an opportunity to practice writing DRY code using loops, conditional statements, and functions.

Guided Activity on Git/Github.com For Collaboration

This lesson teaches you how to collaborate with others in a project, including tasks such as notifying others that an assigned task has been completed.

Guided Activity on Undo Changes in Git

This lesson teaches you how to undo changes in Git after they have been added or committed.

Activity Data Structures

This activity provides an opportunity to practice working with commonly used Python data structures for scientific data: lists, numpy arrays, and pandas dataframes.

Manipulate, Summarize and Plot Numpy Arrays

This lesson walks you through manipulating, summarizing and plotting numpy arrays.

Import Text Data Into Numpy Arrays

This lesson walks you through importing text data from .txt and .csv files into numpy arrays.

Intro to Numpy Arrays

This lesson describes the key characteristics of a commonly used data structure in Python for scientific data: numpy arrays.

Guided Activity to Submit Pull Requests

This lesson teaches you how to submit pull requests on Github.com to suggest changes to another repository.

Guided Activity on Version Control with Git/GitHub

This lesson teaches you how to implement version control using Git and GitHub.

Markdown in Jupyter Notebook

This lesson teaches you how to add Markdown to Jupyter Notebook files.

Manage Jupyter Notebook Files

This lesson teaches you how to manage your Jupyter Notebook files and directories.

What Is Version Control

This lesson reviews the process and benefits of version control and how Git and GitHub support version control.

Plot Data in Python with Matplotlib

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.

Import Python Packages

Python packages are organized directories of code that provide functionality such as plotting data. Learn how to write Python Code to import packages.

Python Lists

This lesson walks you through creating and editing Python lists.

Variables in Python

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.

Crop a Spatial Raster Dataset Using a Shapefile in Python

This lesson covers how to crop a raster dataset and export it as a new raster in Python

How to Dissolve Polygons Using Geopandas: GIS 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.

How to Reproject Vector Data in Python Using Geopandas - GIS in Python

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.

Set Up Your Conda Python Environment

Conda environments allow you to easily manage the Python package installations on your computer. Learn how to install a conda environment.

Setup Git, Bash, and Conda on Your Computer

Learn how to install Git, GitBash (a version of command line Bash) and the Miniconda Python distribution on your computer.

Setup Your Earth Analytics Python, Git, Bash Environment On Your Computer

There are several core tools that are required to work with data. These include Shell/Bash, Git/Github and Python. Learn how to set all of these tools up on your computer so you can work with different types of data using open science workflows.

The Jupyter Notebook Interface

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.

Get Files From GitHub

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). You will also learn how to to update your forked repository with changes made in the original Github repository.

Intro to Shell

This lesson walks you through using Bash/Shell to navigate and manage files and directories on your computer.

What Is Open Reproducible Science

This lesson reviews the importance and benefits of open reproducible science.

GEOG 4463 & 5463 - Earth Analytics Bootcamp: Final Project

GEOG 4463 & 5463 - Earth Analytics Bootcamp: Homework 4

GEOG 4463 & 5463 - Earth Analytics Bootcamp: Homework 3

GEOG 4463 & 5463 - Earth Analytics Bootcamp: Reference on PEP 8 Style Guide

GEOG 4463 & 5463 - Earth Analytics Bootcamp: Homework 2

GEOG 4463 & 5463 - Earth Analytics Bootcamp: Homework 1

GEOG 4463 & 5463 - Earth Analytics Bootcamp: Pre-Course Checklist

GEOG 4463 & 5463 - Earth Analytics Bootcamp: Pre-Course Learning Resources

GEOG 4463 & 5463 - Earth Analytics Bootcamp: August 2018 Syllabus

Analyze Sentiments Using Twitter Data and Tweepy in Python

One common way to analyze Twitter data is to analyze attitudes (i.e. sentiment) in the tweet text. Learn how to analyze sentiments in Twitter data using Python.

Analyze Co-occurrence and Networks of Words Using Twitter Data and Tweepy in Python

One common way to analyze Twitter data is to identify the co-occurrence and networks of words in Tweets. Learn how to analyze word co-occurrence (i.e. bigrams) and networks of words using Python.

Analyze Word Frequency Counts Using Twitter Data and Tweepy in Python

One common way to analyze Twitter data is to calculate word frequencies to understand how often words are used in tweets on a particular topic. To complete any analysis, you need to first prepare the data. Learn how to clean Twitter data and calculate word frequencies using Python.

Programmatically Accessing Geospatial Data Using APIs

This lesson walks through the process of retrieving and manipulating surface water data housed in the Colorado Information Warehouse. These data are stored in JSON format with spatial x, y information that support mapping.

Introduction to Working With the JSON data structure in Python

This lesson introduces how to work with the JSON data structure using Python using the JSON and Pandas libraries to create and convert JSON objects.

How to Join Attributes From One Shapefile to Another in Open Source Python Using Geopandas: GIS in Python

In this lesson you review how to perform spatial joins in python. A spatial join is when you assign attributes from one shapefile to another based upon it's spatial location.

Work With Datetime Format in Python - Time Series Data

This lesson covers how to deal with dates in Python. It reviews how to convert a field containing dates as strings to a datetime object that Python can understand and plot efficiently. This tutorial also covers how to handle missing data values in Python.

Interactive Maps in Python

This lesson covers creating interactive maps with Python in Jupyter Notebook.

Work with MODIS Remote Sensing Data in Python

MODIS is a satellite remote sensing instrument that collects data daily across the globe at 250-500 m resolution. Learn how to import, clean up and plot MODIS data in Python.

Data tutorials

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