Software Carpentry: University of Washington

General Information

Software Carpentry's mission is to help scientists and engineers get more research done in less time and with less pain by teaching them basic lab skills for scientific computing. This hands-on workshop will cover basic concepts and tools, including program design, version control, data management, and task automation. Participants will be encouraged to help one another and to apply what they have learned to their own research problems. The goal of the workshop is for participants to acquire skills to:

enable automation of repetitive tasks
work with structured data
improve the reproducibility of your research
version control code for reuse and collaboration.

This workshop is supported by the UW eScience Institute. Priority will thus be given to UW-affiliated students, staff and faculty. We have a UW Software Carpentry email list that you can join to keep up with local plans, to get advice, ask questions, etc. after the workshop is over.

For more information on what we teach and why, please see our paper "Best Practices for Scientific Computing".

Who: The course is aimed at graduate students, staff, faculty and other researchers at UW. No previous experience with programming is required. If you do have experience in the topics in the syllabus and want to help, send us an email. The instructors are UW students, staff, faculty and other active researchers. We use the #swcuw hashtag on Twitter during class.

Where: WRF Data Science Studio, Physics/Astronomy Tower (6th Floor), University of Washington, Seattle, WA. Get directions with OpenStreetMap or Google Maps.

Requirements: Participants must bring a laptop with a few specific software packages installed (listed below). They are also required to abide by Software Carpentry's Code of Conduct.

Contact: Please mail bmarwick@uw.edu for more information.

Schedule

We are running two full concurrent sessions, one in each room. There are two key differences between the sessions. First is that one session will teach programming with Python and the other session will teach programming with R, all the other class content will be the same.

As a rough guide to choosing which language to learn, Python might be best for you if you're working in the natural or physical sciences, and if you're in the social sciences and humanities then R might be more valuable.

The second difference between the two sessions is that the instructors in the Python session mostly come from the natural and physical sciences, while the instructors in the R session mostly come from the social sciences and humanities. These is simply a convenient way to organise the lessons, and of course you're welcome to join whichever session you think will benefit you the most. The choice is completely up to you.

Day 1

09:00	Automating tasks with the Unix shell
10:30	Coffee break
12:00	Lunch break
13:00	Intro to Python or R
14:30	Coffee break
16:00	Wrap-up

Day 2

09:00	Building programs with Python or R
10:30	Coffee break
12:00	Lunch break
13:00	Version control with Git
14:30	Coffee break
16:00	Wrap-up

Syllabus

The Unix Shell

Files and directories: pwd, cd, ls, mkdir, ...
History and tab completion
Pipes and redirection
Looping over files
Creating and running shell scripts
Finding things: grep, find, ...
Reference...

Programming in Python

Using libraries
Working with arrays
Reading and plotting data
Creating and using functions
Loops and conditionals: for, if, else, ...
Defensive programming
Using Python from the command line
Reference...

Programming in R

Working with vectors and data frames
Reading and plotting data
Creating and using functions
Loops and conditionals: for, if, else
Using R from the command line
Reference...

Version Control with Git

Creating a repository
Recording changes to files: add, commit, ...
Viewing changes: status, diff, ...
Ignoring files
Working on the web: clone, pull, push, ...
Resolving conflicts
Open licenses
Where to host work, and why
Reference...

Setup

To participate in a Software Carpentry workshop, you will need access to the software described below. In addition, you will need an up-to-date web browser. Once you are done installing the software listed below, please go to this page, which has instructions on how to test that everything was installed correctly.

If you haven't already, please register for a free account at GitHub. If you have an edu email, you can register for a free educational account which has some features usually only found in paid accounts. We will use this service as part of the lesson on version control.

The datasets used in the lessons can be downloaded from here (right-click -> save as): shell lesson data, R and Python lesson data.

Text Editor

When you're writing code, it's nice to have a text editor that is optimized for writing code, with features like automatic color-coding of key words. The default text editor on Mac OS X and Linux is usually set to Vim, which is not famous for being intuitive. if you accidentally find yourself stuck in it, try typing the escape key, followed by ':q!' (colon, lower-case 'q', exclamation mark), then hitting Return to return to the shell.

Windows

nano is the editor installed by the Software Carpentry Installer, it is a basic editor integrated into the lesson material.

Notepad++ is a popular free code editor for Windows. Be aware that you must add its installation directory to your system path in order to launch it from the command line (or have other tools like Git launch it for you). Please ask your instructor to help you do this.

Mac OS X

We recommend Text Wrangler or Sublime Text. In a pinch, you can use nano, which should be pre-installed.

Linux

Kate is one option for Linux users. In a pinch, you can use nano, which should be pre-installed.

The Bash Shell

Bash is a commonly-used shell that gives you the power to do simple tasks more quickly.

Windows

Install Git for Windows by downloading and running the installer. This will provide you with both Git and Bash in the Git Bash program.

Software Carpentry Windows Installer

It installs and configures nano (Among other things)

This installer requires an active internet connection.

After installing Git Bash:

Download the Software Carpentry Windows installer.
Double click on the file to run it.

Mac OS X

The default shell in all versions of Mac OS X is bash, so no need to install anything. You access bash from the Terminal (found in /Applications/Utilities). You may want to keep Terminal in your dock for this workshop.

Linux

The default shell is usually bash, but if your machine is set up differently you can run it by opening a terminal and typing bash. There is no need to install anything.

Git

Git is a version control system that lets you track who made changes to what when and has options for easily updating a shared or public version of your code on github.com. You will need a supported web browser (current versions of Chrome, Firefox or Safari, or Internet Explorer version 9 or above).

Windows

Git should be installed on your computer as part of your Bash install (described above).

Mac OS X

For OS X 10.8 and higher, install Git for Mac by downloading and running the installer. After installing Git, there will not be anything in your /Applications folder, as Git is a command line program. For older versions of OS X (10.5-10.7) use the most recent available installer for your OS available here. Use the Leopard installer for 10.5 and the Snow Leopard installer for 10.6-10.7.

Linux

If Git is not already available on your machine you can try to install it via your distro's package manager. For Debian/Ubuntu run sudo apt-get install git and for Fedora run sudo yum install git.

Python

Python is a popular language for scientific computing, and great for general-purpose programming as well. Installing all of its scientific packages individually can be a bit difficult, so we recommend an all-in-one installer.

Regardless of how you choose to install it, please make sure you install Python version 2.x and not version 3.x (e.g., 2.7 is fine but not 3.4). Python 3 introduced changes that will break some of the code we teach during the workshop.

We will teach Python using the IPython notebook, a programming environment that runs in a web browser. For this to work you will need a reasonably up-to-date browser. The current versions of the Chrome, Safari and Firefox browsers are all supported (some older browsers, including Internet Explorer version 9 and below, are not).

Windows

Download and install Anaconda.
Download the default Python 2 installer (do not follow the link to version 3). Use all of the defaults for installation except make sure to check Make Anaconda the default Python.

Mac OS X

Download and install Anaconda.
Download the default Python 2 installer (do not follow the link to version 3). Use all of the defaults for installation.

Linux

We recommend the all-in-one scientific Python installer Anaconda. (Installation requires using the shell and if you aren't comfortable doing the installation yourself just download the installer and we'll help you at the workshop.)

Download the installer that matches your operating system and save it in your home folder. Download the default Python 2 installer (do not follow the link to version 3).
Open a terminal window.
Type
```
bash Anaconda-
```
and then press tab. The name of the file you just downloaded should appear.
Press enter. You will follow the text-only prompts. When there is a colon at the bottom of the screen press the down arrow to move down through the text. Type yes and press enter to approve the license. Press enter to approve the default location for the files. Type yes and press enter to prepend Anaconda to your PATH (this makes the Anaconda distribution the default Python).

R

R is a programming language that is especially powerful for data exploration, visualization, and statistical analysis. To interact with R, we use RStudio.

Windows

Install R by downloading and running this .exe file from CRAN. Also, please install the RStudio IDE.

Mac OS X

Install R by downloading and running this .pkg file from CRAN. Also, please install the RStudio IDE.

Linux

You can download the binary files for your distribution from CRAN. Or you can use your package manager (e.g. for Debian/Ubuntu run sudo apt-get install r-base and for Fedora run sudo yum install R). Also, please install the RStudio IDE.