  The need for new methods to deal with big data is a common theme in most
scientific fields, although its definition tends to vary with the context.
Statistical ideas are an essential part of this, and as a partial response, a
thematic program on statistical inference, learning, and models in big data was
held in 2015 in Canada, under the general direction of the Canadian Statistical
Sciences Institute, with major funding from, and most activities located at,
the Fields Institute for Research in Mathematical Sciences. This paper gives an
overview of the topics covered, describing challenges and strategies that seem
common to many different areas of application, and including some examples of
applications to make these challenges and strategies more concrete.
