Team members may be evaluated using software metrics such as the amount of code written or number of bug tickets closed. In analytics, it’s more important for individuals to be able to formulate problems well, to prototype solutions quickly, to make reasonable assumptions in the face of ill-structured problems, to design experiments that represent good investments, and to analyze results. In building a data science team, these qualities, rather than traditional software engineering expertise, are skills that should be sought.
Qualifying software engineering projects vs data science ones
