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.
• Workshops by the Software Carpentry
• 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 (As the website had to be taken offline, we link to the Internet Archive).

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