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    DataCamp
    DataCamp: The Easiest Way to Learn Data Science Online
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    Remote sensing data processing – NEON airborne data
    Concepts of remote sensing and data logistics, and NEON remote sensing data are introduced. Students learn how to apply spatial data processing and visualization skills using R coding program to...
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    Get Started With GIS in Open Source Python Workshop
    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...
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    Writing Clean Code in R Workshop
    When working with data, you often spend the most amount of time cleaning your data. Learn how to write more efficient code using the tidyverse in R.
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    Working with spreadsheet-style data in Python with pandas and seaborn
    This 4-hour participatory live-coding workshop takes learners through the basics of programming in Python via the Jupyter Lab interface and culminates with exploration and visualization of...
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    Learn Biological Sciences and Physics through scientific process, creation, experimentation and analysis
    Microbes remain attached and grow on various surfaces whether living or non-living. These surfaces provide differential growth substances for microbes to remain attached or grow. The differential...
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    Earth Analytics in R Course
    Earth analytics is an advanced, multidisciplinary course that addresses major questions in Earth science and teaches students to use the analytical tools necessary to undertake exploration of...
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    Earth Analytics Bootcamp Course
    The Earth Analytics Bootcamp is a three-week introductory-level course taught by instructors in Earth Lab and is a part of the Professional Certificate in Earth Data Analytics - Foundations at CU...
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    Introduction to Earth Data Science Textbook
    Introduction to Earth Data Science is an online textbook for anyone new to open reproducible science and the Python programming language. There are no prerequisites for this material, and no prior...
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    Exploring the population dynamics of wintering bald eagles through long-term data using R
    The purpose of this modification was to take the analyses that were done in Excel and have the students perform them using R. Students were not given any R code for this activity, as R code was...
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    Coding Club: A Positive Peer-Learning Community
    Free and self-paced tutorials and courses for learning to code out of the University of Edinburgh
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    Climate Change’s Impact on Sea Level Rise in a Coastal Marsh Over 20 Years
    In this lesson, students will learn about increasing sea level rise and the direct impacts coastal marshes are facing as a result. Students will learn the importance of coastal marshes, tidal...
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    Environmental Drivers of Ecosystem Carbon Fluxes from Minutes to Years
    Students build on fundamental concepts of ecosystem production and carbon cycling, combining this knowledge with open long-term data from ecological and meteorological networks to uncover the...
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    Understanding COVID-19 Biology to Design a Vaccine - Ethics supplement
    The adaptation to Understanding COVID-19 Biology to Design a Vaccine entails two ethics supplements. The first supplement addresses the use of off-label drugs in the pandemic (or other situation)....
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    What is Data Science - Introduction to Data Science
    Data Science is the technology that goes behind handling and working with data in the 21st century. Data Science concepts have proved to be the pinnacle in the last couple of years with the onset...
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    Introduction to Python for Data Science Course
    Free Introduction to Python for Data Science Course at DataCamp
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    Earth Analytics in Python Course
    Earth analytics is an intermediate, multidisciplinary course that addresses major questions in Earth science and teaches students to use the analytical tools necessary to undertake exploration of...
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    Why, when, and how to cite sources of biodiversity specimen data
    This short video and assessment explain why we as scientists should cite our data sources and how we can do this for sources of biodiversity specimen data.
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    Large Datasets in R - Plant Phenology & Temperature Data from NEON
    This module series covers how to import, manipulate, format and plot time series data stored in .csv format in R. Originally designed to teach researchers to use NEON plant phenology and air...
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    Building Biodiversity Datasets for Invasive Species
    A module guiding students through the process of mapping biodiversity data and building a biodiversity dataset to study the spread and impacts of the invasive aquatic plant species, European...
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