Data Carpentry Lessons
We facilitate and develop lessons for Data Carpentry workshops. These lessons are distributed under the CC-BY license and are free for re-use or adaptation, with attribution. We’ve had people use the lessons in courses, to build new lessons, or use them for self-guided learning.
Data Carpentry workshops are domain-specific, so that we are teaching researchers the skills most relevant to their domain and using examples from their type of work. Therefore we have several types of workshops and curriculum is organized by domain.
Curriculum Advisors are part of a team that provides the oversight, vision, and leadership for a particular set of lessons. More information about the role of the Curriculum Advisory Committee can be found in the Carpentries Handbook.
Astronomy
The Foundations of Astronomical Data Science curriculum covers a range of core concepts necessary to efficiently study the ever-growing datasets developed in modern astronomy. This curriculum teaches learners to perform database operations (SQL queries, joins, filtering) and to create publication-quality data visualisations. This curriculum assumes some prior knowledge of Python and exposure to the Bash shell, equivalent to that taught in a Software Carpentry workshop.
Lessons
| Lesson | Site | Repository | Reference | Instructor Notes | Maintainers |
|---|---|---|---|---|---|
| Foundations of Astronomical Data Science | Ralf Kotulla, Catherine Martlin, Dimitrios Theodorakis |
Ecology
This workshop uses a tabular ecology dataset from the Portal Project Teaching Database and teaches data cleaning, management, analysis, and visualization. There are no pre-requisites, and the materials assume no prior knowledge about the tools. We use a single dataset throughout the workshop to model the data management and analysis workflow that a researcher would use.
The Ecology workshop can be taught using R or Python as the base language.
Lessons in English
Lecciones en español
| Lección | Sitio web | Repositorio | Referencia | Guía del instructor | Mantenedor(es) |
|---|---|---|---|---|---|
| Análisis y visualización de datos usando Python (Beta) | Irene Ramos Pérez, Agustina Pesce, Vini Salazar, Heladia Salgado |
Genomics
The focus of this workshop is on working with genomics data, and data management and analysis for genomics research, including best practices for organization of bioinformatics projects and data, use of command line utilities, use of command line tools to analyze sequence quality and perform variant calling, and connecting to and using cloud computing.
More information about hosting and teaching a Genomics workshop can be found on our FAQ page.
Interested in teaching these materials? We have an onboarding video and accompanying slides available to prepare Instructors to teach these lessons. After watching this video, please contact team@carpentries.org so that we can record your status as an onboarded Instructor. Instructors who have completed onboarding will be given priority status for teaching at Centrally-Organised Data Carpentry Genomics workshops.
Please note that workshop materials for working with Genomics data in R in “alpha” development. These lessons are available for review and for informal teaching experiences, but are not yet part of The Carpentries’ official lesson offerings.
Lessons
Lessons in Development
| Lesson | Site | Repository | Reference | Instructor Notes | Maintainers |
|---|---|---|---|---|---|
| Data Analysis and Visualization in R *beta* | Yuka Takemon, Jason Williams, Naupaka Zimmerman |
Geospatial
This workshop is co-developed with the National Ecological Observatory Network (NEON). It focuses on working with geospatial data - managing and understanding spatial data formats, understanding coordinate reference systems, and working with raster and vector data in R for analysis and visualization.
Join the geospatial curriculum email list to get updates and be involved in conversations about this curriculum.
Interested in teaching these materials? We have an onboarding video and accompanying slides available to prepare Instructors to teach these lessons. After watching this video, please contact team@carpentries.org so that we can record your status as an onboarded Instructor. Instructors who have completed onboarding will be given priority status for teaching at Centrally-Organised Data Carpentry Geospatial workshops.
Image processing
This workshop uses Python and a variety of example images to teach the foundational concepts of image processing, and the skills needed to programmatically extract information from image data. The current version of the curriculum was developed from material originally created by Dr. Tessa Durham Brooks and Dr. Mark Meysenburg at Doane College, Nebraska, USA, with support from an NSF iUSE grant. Further development of the curriculum was supported by a grant from the Sloan Foundation.
Join the image processing curriculum email list and/or the dc-image-processing channel on The Carpentries Slack workspace to get updates and be involved in conversations about this curriculum.
| Lesson | Site | Repository | Reference | Instructor Notes | Maintainers |
|---|---|---|---|---|---|
| Image Processing with Python | Jacob Deppen, Toby Hodges, Kimberly Meechan, Ulf Schiller |
Social Science
This workshop uses a tabular interview dataset from the SAFI Teaching Database and teaches data cleaning, management, analysis and visualization. There are no pre-requisites, and the materials assume no prior knowledge about the tools. We use a single dataset throughout the workshop to model the data management and analysis workflow that a researcher would use.
The Social Sciences workshop can be taught using R as the base language. Interested in teaching these materials? We have an onboarding video and accompanying slides available to prepare Instructors to teach these lessons. After watching this video, please contact team@carpentries.org so that we can record your status as an onboarded Instructor. Instructors who have completed onboarding will be given priority status for teaching at Centrally-Organised Data Carpentry Social Sciences workshops.
Please note that workshop materials for working with Social Science data in Python and SQL are under development.
Lessons
Lessons in development
| Lesson | Site | Repository | Reference | Instructor Notes | Maintainers |
|---|---|---|---|---|---|
| Data Analysis and Visualization with Python for Social Scientists *alpha* | |||||
| Data Management with SQL for Social Scientists *alpha* |
Materials in Early Development
These materials are in early stages of development, and have not yet been incorporated into the official Data Carpentry lesson offerings. If you are interested in being involved in developing one of these lessons, see the information under each lesson description. If you are interested in developing a different curriculum, using The Carpentries lesson templates and pedagogical model, see our Curriculum Development Handbook for information about how to get started. If you are interested in contributing to the development of Data Carpentry lessons in general, visit the Help Wanted page on the Carpentries website to find a list of issues in need of attention.
Economics Curriculum
A Data Carpentry curriculum for Economics is being developed by Dr. Miklos Koren at Central European University. These materials are being piloted locally. Development for these lessons has been supported by a grant from the Sloan Foundation.
Other curricula
If you are interested in developing other lessons, please visit The Carpentries Incubator.
Semester materials
Biology Semester-long Course
The Biology Semester-long Course was developed and piloted at the University of Florida in Fall 2015. Course materials include readings, lectures, exercises, and assignments that expand on the material presented at workshops focusing on SQL and R. The course is accessible to:
Community-contributed materials
Python for Atmosphere and Ocean Scientists
This lesson in The Carpentries Lab has been peer-reviewed and published in JOSE, and is ready to be taught by any certified Carpentries instructor (some experience with the netCDF file format and xarray Python library is useful). It is aimed at learners with some prior experience of Python and the Unix Shell, who want to learn how to work with with raster or “gridded” data in Python. As a community-developed lesson, it is currently only available for self-organised workshops. If you have questions about the lesson, please contact the Maintainers listed on the lesson README.