#Data Carpentry Python for Ecologists

Content Contributors: April Wright, Tracy Teal, Ethan White, Leah Wasser, John Gosset, Mariela Perignon

Data Carpentry’s aim is to teach researchers basic concepts, skills, and tools for working with data so that they can get more done in less time, and with less pain. The lessons below were designed for those interested in working with ecological data in Python.

Lessons:

• Short Introduction to Python
• Starting With Data
• Data Types and Format
• Merging Data

Data for this project can be downloaded from this repository at http://psrc.github.io/itm-tutorial-python/python-class.zip.

Requirements: Data Carpentry's teaching is hands-on, so participants are encouraged to bring in and use their own laptops to insure the proper setup of tools for an efficient workflow once you leave the workshop. (We will provide instructions on setting up the required software several days in advance, and the classroom will have computers with the software installed). There are no pre-requisites, and we will assume no prior knowledge about the tools. Participants are required to abide by Software Carpentry's Code of Conduct.

Twitter: #datacarpentry

@datacarpentry

Acknowledgements & Support

Data Carpentry is supported by the <a href=http://http://www.moore.org/>Gordon and Betty Moore Foundation and a partnership of several NSF-funded BIO Centers (NESCent, iPlant, iDigBio, BEACON and SESYNC) and Software Carpentry, and is sponsored by the Data Observation Network for Earth (DataONE). The structure and objectives of the curriculum as well as the teaching style are informed by Software Carpentry.