Data Science on The Carpentries

Integration and reuse of Library Carpentry content into curricula

Mon, 08 Mar 2021 00:00:00 +0000

This post originally appeared on the Library Carpentry website

By Jeff Oliver, Julie Goldman and Konrad Förstner

There is a growing need to teach students professional data handling skills. Luckily, nobody has to start from scratch for this. While the lessons of The Carpentries including the Library Carpentry lesson program are designed to be taught as defined combinations in two-day workshops, they can be reused in other contexts. In this blog post members of the Library Carpentry community describe how they include Library Carpentry lessons in academic curricula and use the training methods to teach computational skills to students.

Developing Data Skills at Macquarie University Library

Tue, 25 Jun 2019 00:00:00 +0000

This post originally appeared on the Library Carpentry website

By Grai Calvey, Fiona Jones and Heather Cooper

In 2016, Macquarie University delivered a Data Science and eResearch Platform Strategy. In response, the Library embarked on a series of initiatives including:

Developing workshops to improve the data skills of all our library staff
To grow confidence when engaging in data science practice, eResearch conversations and the support of researchers

Our three goals were to:

My Favourite Tool: R

Mon, 16 Oct 2017 00:00:00 +0000

This post originally appeared on the Software Carpentry website.

My favorite tool for data analysis is R.

I have used it for a few years now, I feel at ease when I need to work with tabular data. Using R makes my work enjoyable. I can clean my data in one place - RStudio - and then work with it creating new graphics. I also enjoy learning new things in R. Its constant development gives you the chance to learn a bit more every day. This is thanks to a huge collaborative community that supports you, with quick answers, with examples, and with new packages.

Toads in Vancouver: using Stencila to teach SQL and R at UBC

Fri, 29 Sep 2017 00:00:00 +0000

This post originally appeared on the Software Carpentry website.

One of Stencila’s goals is to create an easy way for people who don’t yet code to learn data science and statistics skills, and to feel comfortable trying out powerful scientific computing languages like R, Python and Julia. We’re doing that by providing interfaces that are similar to the word processors and spreadsheets that they already use. It’s a way for people to “dip their toe” into code - without having to dive into the daunting ocean of IDEs, text editors, packages, version control etc.