Data Structures

Data Structures

What are Data Structures?

Data structures are special formats for organizing, processing and storing data. There are several basic and advanced types of data structures created to organize data to suit specific purposes. Data structures enable the organization of information so that machines can better understand and draw conclusions. The quality of the algorithms we set up in the programs depends on the data structure design.


Why Are Data Structures Important?

Typical data types in computer programming languages, such as integers or characters, are often insufficient for data processing and use. Programs that process and produce information must understand how data should be organized to simplify data processing. Data structures make data easy to use by bringing together and organizing data in a logical way. So essentially, data structures act as a legalized model that defines the way existing data is organized.

Choosing the appropriate data structure for programs is just as important as using data structures. Using an inappropriate data structure may cause the algorithm to break down, and thus cause error-generating codes, as well as reducing the program's running speed. There are certain criteria that should not be ignored in order to avoid these situations and to choose the right data structure.

These criteria are as follows:

1. Determining what type of information will be retained.

2. Determine how this information will be used.

3. Determine where the data will be stored after it has been created.

4. Determine the best way to organize the data.

5. Determine which aspects of memory storage reservation management should be considered.


Where Are Data Structures Used?

Data structures play a critical role in algorithm design. It is often used to instantiate abstract data types. Here are some examples of where data structures are used:

  • -Data Storage
  • -Managing Resources and Services
  • -Data Exchange
  • -Arrangement
  • -Indexing
  • -Call
  • -Scaling


What Are the Properties of Data Structures?

Data structures are classified according to their properties;

Linear and Non-Linear Data Structures: This property describes whether data should be stored sequentially or irregularly, like a string.

Homogeneous and Heterogeneous Data structures: This property describes whether all data in a particular program are of the same type.

Static and Dynamic Data Structures: This feature describes how data structures are compiled. Static data structures have fixed size and memory location, while dynamic data structures have size and memory location that can shrink or expand depending on the situation.

What are Data Structure Types?


The type of data structure to be used in any given case will vary according to the operations that will be required. Some types of data structures;

-> Array:

Arrays store separate types of data together in contiguous memory locations. The location of each data can be easily found with an index. Ours can be of fixed or flexible length.

-> Stack:

Stacks store a data set where operations are applied in linear order. This order is either last-in, first-out (LIFO) or first-in, first-out (FIFO).

-> Queue:

Queue's store data set just like stacks, but the order of operation is first-in, first-out.

-> Linked List:

Each data or node in Linked Lists contains link information to the next data in the list.

-> Tree:

Trees store data sets in an abstract and hierarchical way. Each node is associated with child nodes or key values ​​linked to child nodes. In trees, the ancestor of all nodes is a single node.

-> Heap:

Heaps are tree-based structures where the associated key value of each parent node is greater than or equal to the key value of any of the child data's key values.

-> Graph:

Graphs store data sets non-linearly. These are used to represent real world systems such as computer networks.

-> Trie:

Tries are a data structure that stores data as data items that can be arranged in a visual graph.

-> Hash Table:

Hash table is an array set based on the key-value principle. Value reflects the value of the element we call as key.