Jenny Palomino

In this lesson you will learn how to parse a JSON file containing twitter data to better understand the 2013 Colorado Floods using Open Source Python tools.

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.

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.

When you plot time series data using the matplotlib package in Python, you often want to customize the date format that is presented on the plot. Learn how to customize the date format on time series plots created using matplotlib.

Sometimes you need to take time series data collected at a higher resolution (for instance many times a day) and summarize it to a daily, weekly or even monthly value. This process is called resampling in Python and can be done using pandas dataframes. Learn how to resample time series data in Python with Pandas.

Sometimes you have data over a longer time span than you need for your analysis or plot. Learn how to subset your data using a begin and end date in Python.

Python provides a datetime object for storing and working with dates. Learn how you can convert columns in a pandas dataframe containing dates and times as strings into datetime objects for more efficient analysis and plotting.

Python provides a datetime object for storing and working with dates. Learn how you can convert columns in a pandas dataframe containing dates and times as strings into datetime objects for more efficient analysis and plotting.

A function is a reusable block of code that performs a specific task. Learn how to write functions that can take multiple as well as optional parameters in Python to eliminate repetition and improve efficiency in your code.

A function is a reusable block of code that performs a specific task. Learn how to write functions in Python to eliminate repetition and improve efficiency in your code.

A function is a reusable block of code that performs a specific task. Learn how functions can be used to write efficient and DRY (Do Not Repeat Yourself), code in Python.

Loops can be used to automate data tasks in Python by iteratively executing the same code on multiple data structures. Learn how to automate data tasks in Python using data structures such as lists, numpy arrays, and pandas dataframes.

Loops can help reduce repetition in code by iteratively executing the same code on a range or list of values. Learn about the basic types of loops in Python and how they can be used to write Do Not Repeat Yourself, or DRY, code in Python.

Conditional statements in Python can be written to check for alternative conditions or combinations of multiple conditions. Learn how to write conditional statements in Python that choose betweeen alternative conditions or check for combinations of conditions before executing code.

Conditional statements help you to control the flow of code by executing code only when certain conditions are met. Learn about the structure of conditional statements in Python and how they can be used to write Do Not Repeat Yourself, or DRY, code in Python.

A pull request allows anyone to suggest changes to a repository on GitHub that can be easily reviewed by others. Learn how to submit pull requests on GitHub.com to suggest changes to a GitHub repository.

GitHub.com can be used to store and access files in the cloud using GitHub repositories. Learn how to submit pull requests on GitHub.com to suggest changes to a GitHub repository.

An issue is a GitHub project management tool that allows anyone to identify and discuss potential changes to a repo. Learn how to create and manage GitHub issues to support collaborative open reproducible science projects.

When you are working on a forked GitHub repository you will need to update your files frequently. Learn how to update your GitHub fork using a reverse pull request.

A pull request allows anyone to suggest changes to a repository on GitHub that can be easily reviewed by others. Learn how to submit pull requests on GitHub.com to suggest changes to a GitHub repository.

GitHub is a website that supports git-based version control and collaborative project management. Learn how to use git and GitHub to collaborate on projects in support of open reproducible science.

Pandas dataframes are a commonly used scientific data structure in Python that store tabular data using rows and columns with headers. Learn how to use indexing and filtering to select data from pandas dataframes.

Pandas dataframes are a commonly used scientific data structure in Python that store tabular data using rows and columns with headers. Learn how to run calculations and summary statistics (such as mean or maximum) on columns in pandas dataframes.

Pandas dataframes are a commonly used scientific data structure in Python that store tabular data using rows and columns with headers. Learn how to import text data from .csv files into numpy arrays.

Pandas dataframes are a commonly used scientific data structure in Python that store tabular data using rows and columns with headers. Learn about the key characteristics of pandas dataframes that make them a useful data structure for storing and working with labeled scientific datasets.

Numpy arrays are an efficient data structure for working with scientific data in Python. Learn how to use indexing to slice (or select) data from one-dimensional and two-dimensional numpy arrays.

Numpy arrays are an efficient data structure for working with scientific data in Python. Learn how to run calculations and summary statistics (such as mean or maximum) on one-dimensional and two-dimensional numpy arrays.

Numpy arrays are an efficient data structure for working with scientific data in Python. Learn how to import text data from .txt and .csv files into numpy arrays.

Numpy arrays are a commonly used scientific data structure in Python that store data as a grid, or a matrix. Learn about the key characteristics of numpy arrays that make them an efficient data structure for storing and working with large scientific datasets.

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.

Manually constructed files paths will often not run on computers with different operating systems. Learn how to construct file paths in Python that will work on Mac, Linux and Windows, in support of open reproducible science.

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.

Packages in Python provide pre-built functionality that adds to the functionality available in base Python. Learn how to install packages in Python using conda environments.

A conda environment is a self contained Python environment that allows you to run different versions of Python (with different installed packages) on your computer. Learn how to conda environments can you help manage Python packages and dependencies.

The Python programming language provides many packages and libraries for working with scientific data. Learn about key Python packages for earth data science.

Matplotlib is the most commonly used plotting library in Python. Learn how to customize the colors, symbols, and labels on your plots using matplotlib.

Matplotlib is the most commonly used plotting library in Python. Learn how to create plots using the matplotlib object oriented approach.

DRY (Do Not Repeat Yourself) code supports reproducibility by removing repetition and making code easier to read. Learn about key strategies to write DRY code in Python.

Expressive variable names refer to function and variable names that describe what the variable contains or what the function does. Using expressive names makes your code easier to understand. Learn how to create expressive names for objects in your Python code.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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).

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 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.

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.

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.

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.

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.

A spatial join is when you assign attributes from one shapefile to another based upon its spatial location. Learn how to perform spatial joins in Python.

Designing and developing data workflows can help you 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.

Pseudcode can help you design data workflows through listing out the individual steps of workflow in plain language, so the focus is on the overall data process, rather than on the specific code needed. Learn best practices for writing pseudocode for data workflows.

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

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.

Practice your skills setting up git locally, committing changes to files and pushing and pulling files to GitHub.com

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.

A version control system allows you to track and manage changes to your files. Learn how to use some basic Git commands including add, commit and push.

Learn how to setup git locally on your computer.

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).

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.

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

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.

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

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.

There are a suite of powerful open source python libraries that can be used to work with spatial data. Learn how to use geopandas, rasterio and matplotlib to plot and manipulate spatial data in Python.

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

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

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.

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.

One way to analyze Twitter data is to analyze attitudes (or sentiment) in the tweet text. Learn how to analyze sentiments in Twitter data using open source 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.

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.

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.

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.

API's allow you to automate access and downloading data in your code to support open reproducible science. Learn how how to use API's to download data from the internet using open source python.