problems sparse data choose the right algorithms solutions choose the right data structures tree hash choose the right tools handling large data general tips figure overview of data structures often applied in data science when working with large data figure example of sparse matrixalmost everything is other values are the exception in sparse matrix tweets most of them probably have fewer than wordsbut together they might have hundreds or thousands of distinct words in the on text mining we'll go through the process of cutting text documents into words and storing them as vectors but for now imagine what you' get if every word was converted to binary variablewith " representing "present in this tweet,and " meaning "not present in this tweet this would result in sparse data indeed the resulting large matrix can cause memory problems even though it contains little information luckilydata like this can be stored compacted in the case of figure it could look like thisdata [( , , )row column holds the value support for working with sparse matrices is growing in python many algorithms now support or return sparse matrices tree structures trees are class of data structure that allows you to retrieve information much faster than scanning through table tree always has root value and subtrees of childreneach with its childrenand so on simple examples would be your own family tree or