Data Structure | Types | Operations

A data structure is a set of rules for organizing data in a computer to be utilized efficiently. Data structures allow programmers to develop algorithms that can be used to solve complex problems. Data structures are at the core of computer science, providing a way for computers to store and retrieve data efficiently.

When first starting your computer programming career as a software engineer, understanding which data structure to use for a given problem is typically one of the most challenging concepts to grasp.

Using the wrong data structure is common, which can lead to inefficient code that is difficult to debug. Whether using JavaScript, Python, or any other programming language, you will need to know which data structures are best suited for different tasks.

Different data types

To best understand data structures, starting at the primitive form is imperative. These are called data types.

If data structures are the building blocks of programs, data items are the bricks those structures are made of.

Here is a list of some of the different data types:

1. ‍Integer: A whole number that can be positive, negative, or zero. Examples of integers include -100, 0, and 100.‍
2. ‍Floating point: A real number that can contain a fractional component. Floating-point numbers represent currency values. Examples of floating-point numbers include 12.50 and -0.01.
3. ‍Character: A single letter, number, or symbol. Examples of characters include "A," "b," and "&."
4. ‍String: A sequence of one or more characters. Strings commonly store text data such as names, addresses, and product descriptions.
5. ‍Pointers: A pointer is a data type that stores the address of another value. Pointers store the addresses of dynamically allocated memory and can also create linked lists and other data structures.
6. ‍Boolean: A data type that can have one of two values: true or false.

Characteristics of data structures

The following attributes commonly characterize data structures:

Linear or nonlinear: Linear data structures traverse the data elements sequentially, while nonlinear data structures allow random access to the data.

Static or dynamic: Static data structures have a fixed size, structure, and memory, while dynamic data structures can grow and shrink as needed.

Heterogeneous or homogeneous: Heterogeneous data structures store dissimilar types of information, while homogeneous data structures store similar types of information.

Types of data structures

The data structure type used in a particular scenario is determined by the types of operations required or the sorts of algorithms needed.

Structures can be broken down into two main categories, linear and nonlinear. The following data structure types are available:

Common linear data structures:

• ‍Array: An array is a data structure that stores a sequence of elements. Arrays typically store data in memory, but they can also represent relationships between pieces of data.

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• ‍Stacks: A stack is a data structure that stores a sequence of elements, with the most recently added element at the top of the stack.
  
  A stack is a data structure that organizes several pieces of information in the order in which operations are applied to them. The order is Last in, First out – LIFO, which is the same as First in, Last out – FILO.

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• ‍Queue: A queue is a data structure that stores a sequence of elements, with the most recently added element at the back of the queue.
  
  A queue is a data structure that organizes several pieces of information in the order in which operations are applied to them. The order is First in, First out–

FIFO, which is the same as Last in, Last out – LILO.

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• ‍Linked list: A linked list (also known as a list) is a linear collection of data elements of any kind, called nodes, with each node having a value and linking to the next in the chain.
  
  There are different linked lists, such as a singly-linked list, circular linked list, and doubly-linked list.

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The nonlinear data structures:

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• ‍Graph: A graph is a data structure representing relationships between data pieces. Graphs often represent social networks and roadmaps.

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• ‍Tree: A tree data structure stores a hierarchy of information. Trees are commonly used to represent the structure of XML documents and file systems.
  
  Many different tree types exist, including binary, B-trees, and AVL trees.

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• ‍Heap: A heap is a data structure that stores a sequence of elements. Heaps implement priority queues.

• ‍Hash table: A hash table is a data structure that stores a mapping of keys to values. Hash tables are commonly used to implement associative arrays and lookup tables.

How are data structures used?

Data structures can be implemented using different programming languages. Some languages, such as C++ and Java, provide built-in support for common data structures. Other languages, such as Python and Ruby, do not have built-in support for data structures but offer libraries that implement them.

Common use cases for data structures:

Managing resources and services: Data structures can be used to keep track of resources and services in a computer system. For example, hash tables can use the IP addresses of all the computers on a network to store data.

Maintaining information about users: Data structures can store information about a software application's users. For example, you can use a linked list to store the names and contact information of all the users of a social networking site.

Storing data from sensors: Data structures can store data like temperature sensors or motion detectors. For example, you can use an array to store the temperatures recorded by a thermometer over time.

Indexing: Indexing is a way of accessing data. Indexing is often used to speed up searches. For example, you can use a binary search tree to index the words in a dictionary so that you can quickly find the definition of a word.

Searching: Data structures can be used to search for data. For example, a binary search tree can quickly find a person's contact information in an extensive database.

Data structure operations

Next, we will look at some of the most common data structure operations.

Data structure operations are the methods used to manipulate the data in a data structure. The most common data structure operations are:

• ‍Traversal
  
  ‍Traversal operations are used to visit each node in a data structure in a specific order. This technique is typically employed for printing, searching, displaying, and reading the data stored in a data structure.

• ‍Insertion
  
  ‍Insertion operations add new data elements to a data structure. You can do this at the data structure's beginning, middle, or end.

• ‍Deletion
  
  ‍Deletion operations remove data elements from a data structure. These operations are typically performed on nodes that are no longer needed.

• ‍Search
  
  ‍Search operations are used to find a specific data element in a data structure. These operations typically employ a compare function to determine if two data elements are equal.

• ‍Sort
  
  ‍Sort operations are used to arrange the data elements in a data structure in a specific order. This can be done using various sorting algorithms, such as insertion sort, bubble sort, merge sort, and quick sort.

• ‍Merge
  
  ‍Merge operations are used to combine two data structures into one. This operation is typically used when two data structures need to be combined into a single structure.

• ‍Copy
  
  ‍Copy operations are used to create a duplicate of a data structure. This can be done by copying each element in the original data structure to the new one.

How to choose a data structure

Now that we have gone over the basics of data structures, it is time for you to learn when to use each type of data structure. The choice of data structure depends on the following factors:

The operations that will be performed: The choice of data structure should be based on the operations performed. For example, you should use a linked list if you need to perform insertions and deletions. If you need to perform indexing, then you should use an array.

The time complexity of the operations: The choice of data structure should be based on the time complexity of the operations that will be performed. For example, if you need to perform searches frequently, you should use a binary search tree.

The space complexity of the operations: The choice of data structure should be based on the space complexity of the operations that will be performed. For example, if you need to store a lot of data, you should use an array.

Memory usage: The choice of data structure should be based on the amount of memory used. For example, if you need to store a lot of data in memory, you should use a linked list.

Getting started with data structures

Data structures are the building blocks of algorithms. To understand algorithms, you must first understand data structures.

Data structures are how data is organized in memory. They define the relationship between pieces of data and enable efficient access and modification of that data.

If you want to know about data structures in-depth, we recommend reading a book such as "Introduction to Algorithms" by Cormen, Leiserson, Rivest, and Stein.

Finally, when you are ready to start implementing data structures, we recommend using a language such as Java or C++. These languages will allow you to implement data structures in any way you want.

We hope this article has helped you understand data structures and how they can be used to solve problems. If you have any questions on how you can get started with data structures, feel free to reach out.