Software CarpentrySoftware Carpentry has contributed to the materials listed below.
Course lessons are developed as a part of a course curriculum. They teach specific learning objectives associated with data and scientific programming. Software Carpentry has contributed to the following lessons:
This lesson teaches you how to wrangle data (e.g. run multi-task functions, combine) with numpy arrays.
This lesson teaches you how to wrangle data (e.g. subselect, update, and combine) with pandas dataframes.
This lesson teaches you how to write custom functions in Python.
This lesson describes how functions are used in Python to write DRY and modular code.
This lesson describes the DRY (i.e. Do Not Repeat Yourself) principle and lists key strategies for writing DRY code in Python.
This lesson walks you through using indexing to select data from pandas dataframes.
This lesson walks you through describing, manipulating, and plotting pandas dataframes.
This lesson walks you through importing tabular data from .csv files to pandas dataframes.
This lesson describes key characteristics of pandas dataframes, a data structure commonly used for scientific data.
This lesson walks you through manipulating, summarizing and plotting numpy arrays.
This lesson walks you through importing text data from .txt and .csv files into numpy arrays.
This lesson reviews the process and benefits of version control and how Git and GitHub support version control.
Python packages are organized directories of code that provide functionality such as plotting data. Learn how to write Python Code to import packages.
This lesson walks you through creating and editing Python lists.
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.
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.
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.
This lesson walks you through using Bash/Shell to navigate and manage files and directories on your computer.
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.
In this lesson you will review the basics of for loops in Python.
This tutorial walks you through how to install and import python packages.
Learn how to fork a repository using the GitHub website.
Learn what version control is, and how Git and GitHub are used in a typical version control workflow.
Learn how to work with function arguments in the R programming language..
This lesson introduces the function environment and documenting functions in R. When you run a function intermediate variables are not stored in the global environment. This not only saves memory on your computer but also keeps our environment clean, reducing the risk of conflicting variables.
Learn how to write a function in the R programming language.
This lesson will cover the basic principles of using functions and why they are important.
Learn how to create a well-organized working directory to store your course data.
Learn what a package is in R and how to install packages to work with your data.
Learn how to work with R using the RStudio application.
Learn how to download and install R and RStudio on your computer.