Data Structures Every Developer Should Know

The power of modern computing is driving rapid developments in science, business and many aspects of everyday life. Much of this has to do with advancements in technologies based in automation and artificial intelligence (AI) like machine learning (ML) and its many subsets.

These technologies accomplish groundbreaking complex tasks. They can analyze quantities of data that would overwhelm computers of the past. However, the algorithms driving today’s technologies still rely on fundamental building blocks of applied computer programming like common data structures.

Data structures and related functions of collecting, organizing and manipulating data are central to computer information sciences (CIS). Reflecting this, Lamar University’s online Bachelor of Science (B.S.) in CIS program devotes an entire course to data structures. Other courses build on the applications of data structures every programmer should know, preparing graduates for success in CIS professions.

What Are Data Structures?

Towards Data Science describes data structures as “specialized means of organizing and storing data in computers in such a way that we can perform operations on the stored data more efficiently.” Well-organized data structures help computer programs access, process and analyze large amounts of data quickly.

Different types of data structures may be more effective when used for specific computer operations and tasks. Plus, certain data structures are more conducive to organizing particular kinds of data.

Here are five common data structures that are central to contemporary computing applications:

  1. Arrays

Arrays are linear, fixed-size data structures that organize data items of the same type assigned to memory locations. They allow for random data access according to a data item’s fixed location.

GeeksforGeeks suggests the simplified analogy of a staircase where a person is placed on each step. Someone could locate a person on the staircase by knowing the count of the step that person is on.

Arrays offer the benefit of quick access to the position and simple organization of data items of the same type. Arrays are limited due to their fixed size, but they are perfect for implementing more complex, flexible data structures.

  1. Linked Lists

Linked lists are also linear, sequential data structures. Each node (element) of a linked list includes a data item and reference or pointer to the next node in the sequence. A linked list will have a “head” as the first node and a “tail” as the last node.

A “singly linked list” is one-directional, with the tail being the endpoint of the sequence. In a “doubly linked list” structure, users can follow the sequence in both directions. Finally, a “circular linked list” connects the head and tail nodes.

Linked lists can organize more dynamic sets of data than simple arrays. Data nodes can be inserted or deleted more flexibly. Random access is limited because of the sequential nature of linked lists.

  1. Stacks

Stacks are also linear data structures built on arrays or linked lists. Data items (or data structures) are further structured in a set order and accessed either “Last In First Out” (LIFO) or “First In Last Out” (FILO).

The most often-used analogy for this data structure is rather obvious: a stack of plates. The top plate has been added last and can be removed first. Likewise, the bottom plate was added first and must be removed last. The undo/redo feature in computer programs is a straightforward application of this data structure.

  1. Trees

A tree structure organizes data hierarchically. The tree starts with a topmost “root” node. Further nodes, called “children,” branch out downward from the root. The node a child branches out from is its “parent.” Children may branch out into other nodes, forming subtrees. Nodes with no children of their own are called “leaves.”

Tree structures have many applications and make data easy to sort, organize, search and traverse. A hierarchical computer file organization system is an example of a widely used tree structure.

  1. Graphs

Non-linear graphs allow for the organization of networks of data that may be directly and indirectly related. Graph data structures are composed of nodes (or vertices) and edges, the lines or arcs that connect nodes.

Graphs can represent the complicated relationships between nodes. Each node may designate its own complex data structure. The evolution of social media networks and the advancement of GPS location route-planning are examples of modern applications of graph data structures.

These are vastly simplified descriptions of common data structures. Data structures provide the foundation for advanced computer operations and algorithms in the digital era, enabling today’s cutting-edge technologies. Developing expertise in data structures and their potential applications is key for computer programmers and numerous other CIS professionals.

Learn more about Lamar University’s online Bachelor of Science in Computer Information Sciences program.

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