ABSTRACT
Quality assurance in data science does not get enough attention. It should be an integral part of the data science team setup and culture. There will be resistance on personal (emotions), social (team culture) and organizational (lack of resources) levels. Code review alone is not enough. It is necessary to reproduce (some of) the results independently. It is also necessary to ensure that nothing important gets lost or misinterpreted when the results are communicated.
