Understanding Data Structuring

A solid understanding of data structuring is essential for anyone who has to deal with large quantities of data. Data structuring is the process of organizing data in a way that it can be used and accessed by software programs. There are a myriad of ways to organize data, but some of the most common are dictionaries, lists, and linked lists. Each has its own strengths and weaknesses, therefore it is essential to know each one is best suited to your specific needs.

Linear Data Structures

Linear data structures vary depending on the programming language they are written in. They may differ in memory allocation, internal structure, and how basic operations like traversal, retrieval, insertion and deletion are handled. A good example of linear data structures is an array that keeps its elements in multiple memory locations that are contiguous, where each element is accessible through an index that begins at zero. These are good for algorithms which require sequential data access. They are also useful to store lists (like dates or addresses) and for performing math calculations.

Non-Linear Data Structures

A tree is an non-linear structure which organizes data into the form of a hierarchy. Each record in a tree has a parent-child relationship that connects it to one or more records below. Each child's record can contain data or links to other records in the tree. One good example of the tree is a family tree, which has its principal piece of information at the top and branches out into grandchildren, children, etc.

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