Data Carpentry

Data Carpentry 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. Data Carpentry has contributed to the following lessons:

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 Anaconda Python Environment

This tutorial walks you through installing a conda environment designed for this class.

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.

Get Help with Python

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

Write Clean Python Code - Expressive programming 101

This lesson covers the basics of clean coding meaning that we ensure that the code that we write is easy for someone else to understand. We will briefly cover style guides, consistent spacing, literate object naming best practices.

Work with tabular spreadsheet data in Python

About.

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.

Introduction to Markdown

This tutorial walks you through how to format text using Markdown.

Time Series Data: Work with Dates in R

Times series data can be manipulated efficiently in R. Learn how to work with, plot and subset data with dates in R.

Plot Data and Customize Plots with ggplot Plots in R - Earth Analytics - Data Science for Scientists

Learn how to plot data and customize your plots using ggplot in R.

How to Address Missing Values in R

Missing data in R can be caused by issues in data collection and / or processing and presents challenges in data analysis. Learn how to address missing data values in R.

How to Import, Work with and Plot Spreadsheet (Tabular) Data in R

Learn how to import and plot data in R using the read_csv & qplot / ggplot functions.

Understand the Vector Data Type in R and Classes Including Strings, Numbers and Logicals - Data Science for Scientists 101

This tutorial introduces vectors in R. It also introduces the differences between strings, numbers and logical or boolean values (True / False) in R.

Creating Variables in R and the String vs Numeric Data Type or Class - Data Science for Scientists 101

This lesson covers creating variables or objects in R. It also introduces some of the basic data types or classes including strings and numbers. This lesson is designed for someone who has not used R before.

The Syntax of the R Scientific Programming Language - Data Science for Scientists 101

This lesson introduces the basic syntax associated with the R scientific programming language. You will learn about assignment operators (<-), comments and basic functions that are available to use in R to perform basic tasks including head(), qplot() to quickly plot data and others. This lesson is designed for someone who has not used R before. You will work with precipitation and stream discharge data for Boulder County.

Get Help with R - Data Science for Scientists 101

This tutorial covers ways to get help when you are not sure how to perform a task in R.

Write Clean Code - Expressive or Literate Programming in R - Data Science for Scientists 101

This lesson covers the basics of clean coding meaning that you ensure that the code that you write is easy for someone else to understand. The lesson will briefly cover style guides, consistent spacing, literate object naming best practices.

Install & Use Packages in R

Learn what a package is in R and how to install packages to work with your data.

Get to Know RStudio

Learn how to work with R using the RStudio application.

Install & Set Up R and RStudio on Your Computer

Learn how to download and install R and RStudio on your computer.

Data tutorials

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