  Big data are data on a massive scale in terms of volume, intensity, and
complexity that exceed the capacity of standard software tools. They present
opportunities as well as challenges to statisticians. The role of computational
statisticians in scientific discovery from big data analyses has been
under-recognized even by peer statisticians. This article reviews recent
methodological and software developments in statistics that address the big
data challenges. Methodologies are grouped into three classes:
subsampling-based, divide and conquer, and sequential updating for stream data.
Software review focuses on the open source R and R packages, covering recent
tools that help break the barriers of computer memory and computing power. Some
of the tools are illustrated in a case study with a logistic regression for the
chance of airline delay.
