As part of the EXCELERATE ELIXIR-Carpentries agreement, ELIXIR-IT will host two Carpentries workshops in February 2019. Space is limited and it will likely fill quickly. Course attendance is free of charge.
1) ELIXIR Software Carpentry on February 11-12, 2019 at the at the IGB-CNR of Naples, Italy. More information at the workshop webpage. Here is the registration link. Should you have any question, do not hesitate to contact the ELIXIR-IIB Training Team (firstname.lastname@example.org) and/or the local organiser Dr. Vincenza Colonna (email@example.com).
The workshop is aimed at biologists with little or no prior computational experience and is focused on Reproducible Scientific Analysis. The curriculum will include: The Unix Shell, R for Reproducible Scientific Analysis, Version Control with Git. For instance, after attending this workshop you will be able to: automate repetitive tasks, capture small data manipulation steps that are normally not recorded to make research reproducible, R data and plot manipulation, use a few easy-to-remember commands to do most day-to-day version control tasks.
2) ELIXIR Data Carpentry on February 14-15, 2019 at the University of Milano-Bicocca, Milan, Italy. More information at the workshop webpage. Here is the registration link. Should you have any question, do not hesitate to contact the ELIXIR-IIB Training Team (firstname.lastname@example.org) and/or the local organiser Dr. Gianluca Della Vedova (email@example.com).
The workshop is at people from any area with little or no prior computational experience who need to develop skills for data organisation in spreadsheets, data cleaning with OpenRefine, data analysis and visualization in Python, data management with SQL.
The Carpentries are a fiscally sponsored project of Community Initiatives. They teach skills that are immediately useful for researchers, using lessons and datasets that allow researchers to quickly apply what they’ve learned to their own work. The target audience is learners who have little to no prior computational experience, and the instructors put a priority on creating a friendly environment to empower researchers and enable data-driven discovery. Even those with some experience will benefit, as the goal is to teach not only how to do analyses, but how to manage the process to make it as automated and reproducible as possible.