Training for Open Code is often aimed at the big picture of the research context, in other words: How do I make my research reproducible? Data and code are usually considered together. This often includes basic skills such as:

• Knowledge of an open programming language such as R
• Knowledge of software development and versioning (Git and so on)
• Knowledge of Open Source and licences
• Knowledge of how software is published in a citable manner and how software is cited

Specific training options that cover one or more of the aspects mentioned above are:

• The Turing Way “Guide for Reproducible Research”
• QuantEcon offers, among other things, introductions to quantitative economic research with Python
  
• Code Publishing Tutorial offered by LMU Open Science Center.
• Software Carpentry Workshops
• CodeRefinery offers training for the development of research software, including lessons for self-learning, and refers to third-party training programmes
• ZBW online seminar “Good Scientific Practice and Reproducible Research“ (German)
• NASA TOPS OS101 Module 4: Open Code

It is also advisable to find out what training opportunities your university or research institution offers, for example in programming or statistical data analysis.