This is a companion discussion topic for the original entry at:
Agile development is a well-established practice for modern software development that gained broad adoption as software became ubiquitous in the business world. As data science matures in the organization, perhaps we are at a similar crossroads. What can data science learn from the agile approach? I’ll share my experience as a data scientist in an agile product development group — what agile practices have proven most valuable, how R has enabled an agile approach, and where data science may need its own set of agile principles.
Elaine McVey - Data Science Lead
My current focus is on using simulation to help cities confidently design and operate algorithm-driven microtransit services.
I’m a data scientist and manager, with a graduate degree in statistics. I have particular interests in:
- using data and technology to help people make better decisions
- the role of data science in technology companies
- how best to integrate data science with product management and software development functions
- best practices for programming with data and creating reproducible analyses
- the use of data science and technology to uncover truth and be a force for good
- mentoring junior data scientists/aspiring data scientists
- the use of R and the R community