2018/01/31 by Iain Carmichael, J. S. Marron · 1 citation
Computer Science · Decision Sciences · Engineering · Mathematics · #Big data #Business statistics #Computer science #Data Analysis with R #Data analysis #Data mining #Data science #Engineering #Exploratory analysis #Exploratory data analysis #Mathematics #Scientific Computing and Data Management #Statistics #Statistics Education and Methodologies #Statistics education #Task (project management) #Term (time) #stat.OT
paper · pdf · doi:10.1007/s42081-018-0009-3
arxiv created 2018/05/01 · openalex publication_date 2018/05/14 · arxiv updated 2018/10/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Data science is the business of learning from data, which is traditionally the business of statistics. Data science, however, is often understood as a broader, task-driven and computationally-oriented version of statistics. Both the term data science and the broader idea it conveys have origins in statistics and are a reaction to a narrower view of data analysis. Expanding upon the views of a number of statisticians, this paper encourages a big-tent view of data analysis. We examine how evolving approaches to modern data analysis relate to the existing discipline of statistics (e.g. exploratory analysis, machine learning, reproducibility, computation, communication and the role of theory). Finally, we discuss what these trends mean for the future of statistics by highlighting promising directions for communication, education and research.