Classical data values are single points in p-dimensional space; symbolic data values are hypercubes (broadly defined) in p-dimensional space (and/or a cartesian product of p distributions). While some datasets, be they small or large in size, naturally consist of symbolic data, many symbolic datasets result from the aggregation of large or extremely large classical datasets into smaller more managably sized datasets, with the aggregation criteria typically grounded on basic scientific questions of interest. Unlike classical data, symbolic data have internal variation and structure which must be taken into account when analysing the dataset. In this paper, we review briefly types of symbolic data, how they might be analysed and how such analysis differs from a traditional classical analysis.