This course will provide researchers with computational tools to address their research questions in new or more efficient and reproducible ways. The course is aimed at novices and no previous programming knowledge is required. The course follows the Software Carpentry teaching concept and lessons.
Course content

Software Carpentry aims to help researchers get their work done in less time and with less pain by teaching them basic research computing skills. This course will cover the following lessons:

UNIX Shell: Learn to  use the terminal to handle a large amount of files more efficiently and other useful skills which can support basic programming skills.
R for reproducible scientific analysis: R is normally described as a useful programming language used as a tool for statistical analyses, but can also do much more. *
Plotting and Programming in Python: Python is the most ubiquitous programming languages of today and is a typical first language taught for beginners. *
Version Control with Git: Share files with your peers more efficiently, and learn to "travel back in time" to previous versions of your own code with Git.
Using Databases and SQL: With SQL, you will learn to handle and index large amounts of data.
Research Data Management (RDM): We are all dealing with data in our research. This module is about organizing, storing, and sharing research data effectively, with a focus on Findability, Accessibility, Interoperability, and Reproducibility (FAIR).