Future of work

Technological advances have created entirely new disciplines, providing both research and job opportunities. However, at the same time, these advances are redefining and blurring the lines between existing fields. As this continues, we should expect the future of work to evolve in unexpected ways. 

After bringing together statisticians, computer scientists, and data scientists for a workshop titled Challenges for Statistics in the Era of Data Science, the Harvard Data Science Review summarized how the worlds of statistics and data science are increasingly in conversation with each other.  

The article explores the origins of both worlds, the unique reasons for their development, their journeys as research disciplines, and the current crossroads they face as technology brings them closer together than ever before. 

By charting this history, the authors explore the approaches to training, education, and application that have shaped the cultures of each discipline. By doing so, they hope to address areas of disagreement and develop a more comprehensive, collaborative approach to each field. The article explores issues related to modelling, hypothesis testing, uncertainty quantification, and statistical inference.  

While these issues may seem theoretical, they have concrete implications for the future workforce of statisticians and data scientists. Given that these roles greatly influence data collection and the infrastructure of information used by many different sectors to power tools used for critical decision-making, the issues must be addressed. The authors suggest that a more collaborative approach to training this future workforce will allow the strengths of both disciplines to build upon each other for a more complementary and robust approach to data science and statistics.