  Science of science has become a popular topic that attracts great attentions
from the research community. The development of data analytics technologies and
the readily available scholarly data enable the exploration of data-driven
prediction, which plays a pivotal role in finding the trend of scientific
impact. In this paper, we analyse methods and applications in data-driven
prediction in the science of science, and discuss their significance. First, we
introduce the background and review the current state of the science of
science. Second, we review data-driven prediction based on paper citation
count, and investigate research issues in this area. Then, we discuss methods
to predict scholar impact, and we analyse different approaches to promote the
scholarly collaboration in the collaboration network. This paper also discusses
open issues and existing challenges, and suggests potential research
directions.
