Martha Morrissey

Martha Morrissey has contributed to the materials listed below.

Course Lessons

Course lessons are developed as a part of a course curriculum. They teach specific learning objectives associated with data and scientific programming. Martha Morrissey has contributed to the following lessons:

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).

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.

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 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.

GIS in Python: Introduction to Vector Format Spatial Data - Points, Lines and Polygons

This lesson introduces what vector data are and how to open vector data stored in shapefile format in Python.

Subtract Raster Data in Python Using Numpy and Rasterio

Sometimes you need to manipulate multiple rasters to create a new raster output data set in Python. Learn how to create a CHM by subtracting an elevation raster dataset from a surface model dataset in Python.

Open, Plot and Explore Lidar Data in Raster Format with Python

This lesson introduces the raster geotiff file format - which is often used to store lidar raster data. You will learn the 3 key spatial attributes of a raster dataset including Coordinate reference system, spatial extent and resolution.

Text Editors for the Command Line and Scientific Programming

Text editors can be used to edit code and for commit messages in git. Learn about features to look for in a text editor and how to change your default text editor at the command line.

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.

How to Access and Use Shell to Set Up a Working Directory

This tutorial walks you through how access the shell through terminal, use basic commands in the terminal for file organization, and set up a working directory for the course.

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.

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.

Get and Work With Twitter Data in Python Using Tweepy

You can use the Twitter RESTful API to access tweet data from Twitter. Learn how to use tweepy to download and work with twitter social media data in Python.

Work With Twitter Social Media Data in Python - An Introduction

This lesson will discuss some of the challenges associated with working with social media data in science. These challenges include working with non standard text, large volumes of data, API limitations, and geolocation issues.

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.

Introduction to the JSON data structure

This lesson covers the JSON data structure. JSON is a powerful text based format that supports hierarchical data structures. It is the core structure used to create GeoJSON which is a spatial version of JSON that can be used to create maps. JSON is preferred for use over CSV files for data structures, as it has been proven to be more efficient - particulary as data size becomes large.

Introduction to APIs

In this module, you learn various ways to access, download and work with data programmatically. These methods include downloading text files directly from a website onto your computer and into Python, reading in data stored in text format from a website into a DataFrame in Python, and finally, accessing subsets of particular data using REST API calls in Python.

Handle missing spatial attribute data Python: GIS in Python

This lesson introduces what vector data are and how to open vector data stored in shapefile format in Python.

Clip a spatial vector layer in python using shapely & geopandas: GIS in Python

In this lesson you review how to clip a vector data layer in python using geopandas and shapely.

GIS in Python: Reproject Vector Data.

In this lesson we cover how to reproject a vector dataset in `Python` using the `to_crs()` `Geopandas` function.

For Loops in Python Refresher

In this lesson you will review the basics of for loops in Python.

Get Help with Python

This tutorial covers ways to get help when you are stuck in Python.

Customize matplotlib plots in Python - earth analytics - data science for scientists

Matplotlib is one of the most commonly used plotting library in Python. This lesson covers how to create a plot using matplotlib and how to customize matplotlib plot colors and label axes in Python.

About data types in Python - Data Science for scientists 101

This tutorial introduces numpy arrays in Python. It also introduces the differences between strings, numbers and boolean values (True / False) in Python.

Objects and variables in Python

This tutorial introduces the Python programming language. It is designed for someone who has not used Python before. You will work with precipitation and stream discharge data for Boulder County.

Get to Know Python & Jupyter Notebooks

This tutorial introduces the Python scientific programming language. It is designed for someone who has not used Python before. You will work with precipitation and stream discharge data for Boulder County in Python but also learn the basics of working with python.

Customize Matplotlibe Dates Ticks on the x-axis in Python

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

Resample or Summarize Time Series Data in Python With Pandas - Hourly to Daily Summary

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.

Subset Time Series By Dates Python Using Pandas

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

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.

Reproject Raster Data Python

This lesson teaches you how to reproject raster data using rasterio.

Classify and Plot Raster Data in Python

This lesson presents how to classify a raster dataset and export it as a new raster in Python.

Subtract One Raster from Another and Export a New Geotiff in Python

Often you need to process two raster datasets together to create a new raster output. You then want to save that output as a new file. Learn how to subtract rasters and create a new geotiff file using open source Python.

About the Geotiff (.tif) Raster File Format: Raster Data in Python

This lesson introduces the geotiff file format. Further it introduces the concept of metadata - or data about the data. Metadata describe key characteristics of a data set. For spatial data these characteristics including CRS, resolution and spatial extent. Here you learn about the use of tif tags or metadata embedded within a geotiff file as they can be used to explore data programatically.

Plot Histograms of Raster Values in Python

This lesson introduces the raster geotiff file format - which is often used to store lidar raster data. You cover the 3 key spatial attributes of a raster dataset including Coordinate reference system, spatial extent and resolution.

Spatial Raster Metadata: CRS, Resolution, and Extent in Python

This lesson introduces the raster meta data. You will learn about CRS, resolution, and spatial extent.

Open, Plot and Explore Lidar Data in Raster Format with Python

This lesson introduces the raster geotiff file format - which is often used to store lidar raster data. You will learn the 3 key spatial attributes of a raster dataset including Coordinate reference system, spatial extent and resolution.

Interactive Maps in Python

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

Explore Precipitation and Stream Flow Data Using Interactive Plots: The 2013 Colorado Floods

Practice interpreting data on plots that show rainfall (precipitation) and stream flow (discharge) as it changes over time.

Create Data Driven Reports using Jupyter Notebooks | 2013 Colorado Flood Data

Connecting data to analysis and outputs is an important part of open reproducible science. In this lesson you will explore that value of a well documented workflow.

Use Google Earth Time Series Images to View Flood Impacts

Learn how to use the time series feature in Google Earth to view before and after images of a location.

How to Install Python Packages and Then Use Then in Python

The first step in using packages in Python is to install them on your computer. Learn how to install and and import python packages.

Introduction to the Jupyter Notebook Interface

The Jupyter Notebook is an interactive coding environment that allows you to combine code, documentation and outputs. Learn how to use the Jupyter notebook interface.

Introduction to Markdown

Markdown is a syntax that is used to format text in text files, Jupyter Notebooks and even R Markdown files. Learn how to format text using Markdown.

Why Expressive File and Folder Names Matter: File Organization For Reproducible Science

Learn why self-explanatory file and directory names are important when setting up a science project on your computer. This lesson provides a broad overview of file organization principles.

Jupyter Notebooks - An Important Part of the Open Science Toolbox

Jupyter Notebooks are a tool you can use to combine code, documentation and outputs in the same file. Learn how how to use Jupyter Notebooks for reproducible open science work.

Data tutorials

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