The researcher identifies several problems, including poor software development practices, gaps in statistical and computational knowledge, and a tendency to favor exaggeration over scientific rigor.
The warning highlights the need for increased responsibility and scientific rigor in projects. Researchers recommend forced exploratory data analysis (EDA) to guide feature development and model selection. Additionally, encouraging the use of understandable AI techniques can provide deeper insights into how algorithms work than basic accuracy measurements.
To mitigate these issues, the researcher advocates holding data scientists to the same standards as software engineers, emphasizing the importance of code reviews, documentation, and sound architectural decisions.
Establishing these norms is critical to preserving the integrity and reproducibility of data science results as the field continues to evolve, the expert writes.
Source: Ferra
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