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.
About the speaker
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