Online
Apr 26 - May 2, 2022
Afternoon lessons, 1pm start
Instructors: Tom Saunders, Matt Plummer, André Bellvé, Laura Duntsch, Yvette Wharton, Martin Feller
Helpers: Aimee van der Reis, Sridevi Bhamidipati, Laura Armstrong, Dharani Sontam, Nick Young, Anita Kean, Chris Seal, Mike Laverick, Noel Zeng
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: This training will take place online. The instructors will provide you with the information you will need to connect to this meeting.
When: Apr 26 - May 2, 2022.
Requirements: Participants must have access to a computer 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).
Accessibility: We are dedicated to providing a positive and accessible learning environment for all. Please notify the instructors in advance of the workshop if you require any accommodations or if there is anything we can do to make this workshop more accessible to you.
Contact: Please email tom.saunders@auckland.ac.nz for more information.
Roles: To learn more about the roles at the workshop (who will be doing what), refer to our Workshop FAQ.
Everyone who participates in Carpentries activities is required to conform to the Code of Conduct. This document also outlines how to report an incident if needed.
Please be sure to complete these surveys before and after the workshop.
Before | Pre-workshop survey |
13:00 | Welcome |
13:10 | Organising Data in Spreadsheets |
14:20 | Break |
14:30 | Organising Data in Spreadsheets |
15:30 | Finish |
13:00 | Cleaning Data with OpenRefine |
14:00 | Break |
14:10 | Cleaning Data with OpenRefine |
15:00 | Break |
15:10 | Cleaning Data with OpenRefine |
15:45 | Finish |
13:00 | Analysing and Visualising Data with R: Day 1 |
14:20 | Break |
14:30 | Analysing and Visualising Data with R: Day 1 |
15:30 | Break |
15:40 | Analysing and Visualising Data with R: Day 1 |
17:00 | Finish |
13:00 | Analysing and Visualising Data with R: Day 2 |
14:20 | Break |
14:30 | Analysing and Visualising Data with R: Day 2 |
15:30 | Break |
15:40 | Analysing and Visualising Data with R: Day 2 |
16:50 | Finish |
13:00 | Managing Data with SQL |
14:20 | Break |
14:30 | Managing Data with SQL |
15:30 | Break |
15:40 | Managing Data with SQL |
16:40 | Finish |
End | Post-workshop survey |
To participate in a Data Carpentry workshop, you will need access to software as 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.
If you haven't used Zoom before, go to the official website to download and install the Zoom client for your computer.
Like other Carpentries workshops, you will be learning by "coding along" with the Instructors. To do this, you will need to have both the window for the tool you will be learning about (a terminal, RStudio, your web browser, etc..) and the window for the Zoom video conference client open. In order to see both at once, we recommend using one of the following set up options:
The setup instructions for the Data Carpentry Social Sciences workshops (with R) can be found at the workshop overview site. Please also see this setup page for the SQL lesson.