July 12-13, 2018
9:00 am - 4:30 pm
Instructors: Joel Ostblom, Francis Nguyen, Madeleine Bonsma-Fisher, Sara Mahallati
Helpers: Madeleine Bonsma-Fisher, Elliott Sales de Andrade, Joel Ostblom, Ahmed Hasan, Kathy Chung
Data Carpentry develops and teaches workshops on the fundamental data skills needed to conduct research. Its target audience is researchers who have little to no prior computational experience, and its lessons are domain specific, building on learners' existing knowledge to enable them to quickly apply skills learned to their own research. Participants will be encouraged to help one another and to apply what they have learned to their own research problems.
For more information on what we teach and why, please see our paper "Good Enough Practices for Scientific Computing".
Who: The course is aimed at graduate students and other researchers. You don't need to have any previous knowledge of the tools that will be presented at the workshop.
Where: MADLab, Gerstein Science Information Centre, Room B112. Get directions with OpenStreetMap or Google Maps.
When: July 12-13, 2018. Add to your Google Calendar.
Requirements: Participants must bring a laptop with a Mac, Linux, or Windows operating system (not a tablet, Chromebook, etc.) that they have administrative privileges on. They should have a few specific software packages installed (listed below). They are also required to abide by Data Carpentry's Code of Conduct.
Accessibility: We are committed to making this workshop accessible to everybody. The workshop organizers have checked that:
Materials will be provided in advance of the workshop and large-print handouts are available if needed by notifying the organizers in advance. If we can help making learning easier for you (e.g. sign-language interpreters, lactation facilities) please get in touch (using contact details below) and we will attempt to provide them.
Contact: Please email m.bonsma@mail.utoronto.ca or uoftcoders@gmail.com for more information.
Please be sure to complete these surveys before and after the workshop.
| Before starting | Pre-workshop survey |
| Morning part 1 | Intro to programming in Python |
| Morning part 2 | Data organization and cleaning |
| Afternoon | Introduction to Python for data analysis |
We will use this collaborative document for chatting, taking notes, and sharing URLs and bits of code.
We will be following the lesson below, modified from Data Carpentry material.
To participate in a Data Carpentry workshop, you will need access to the software described below. In addition, you will need an up-to-date web browser.
We maintain a list of common issues that occur during installation as a reference for instructors that may be useful on the Configuration Problems and Solutions wiki page.
Python is a popular language for research computing, and great for general-purpose programming as well. Installing all of its research packages individually can be a bit difficult, so we strongly recommend Anaconda, an all-in-one installer that includes everything we will be using during the workshop.
Please make sure you install Python version 3.x (e.g., 3.6 is fine).
We will teach Python using JupyterLab, 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).
Once you have installed Python via Anaconda, test that everything's working by launching JupyterLab. You can do this either by opening the Anaconda Navigator and selecting JupyterLab from the menu, or by opening a terminal or the Anaconda prompt, then typing `jupyter-lab` (or `jupyter lab`) into the prompt and hitting Enter.
bash Anaconda3-and then press tab. The name of the file you just downloaded should appear. If it does not, navigate to the folder where you downloaded the file, for example with:
cd DownloadsThen, try again.
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).