Best Practices in Project Management and Quality for Statisticians and Data Scientists — Professional Development Continuing Education Course
ASA, Section on Statistical Consulting
If you work on analysis projects using real-life data, you face a variety of challenges. Many of these challenges are not strictly statistical yet are often unique to projects in applied statistics and data science. They include issues with the quality of source data, difficulties in planning and scoping due to the unknowns, challenges with project expectations and timelines, balancing replicability and reproducibility with project fluidity, among others.
In this workshop, we present best practices and methodologies to address key challenges in the non-statistical aspects of our work: project management and delivery, project quality, and data quality. We discuss the application of project management best practices to statisticians and data scientists and how it relates to the quality of projects. We then translate broadly recognized quality ideas to statistical practice itself. Finally, we provide an overview of industry practices in data quality and management and present a methodology for ensuring the quality of data used for analysis.
The workshop is software-agnostic and no specific background is assumed. Participants are encouraged to “bring” his/her own projects (data, computing environment, scripts, etc.) to use as case studies for themselves while following along (project details and data need not be shared).