# Data Structures and Algorithms

Data Structures and Algorithm

Data Structures are the programmatic method of storing data in order for it to be used efficiently. Almost every enterprise application makes use of various types of data structures in some way. This tutorial will provide you with a solid understanding of Data Structures, which is required to comprehend the complexity of enterprise-level applications and the need for algorithms and data structures.

Why Data Structures?

There are three common problems that applications face nowadays as they become more complex and data-rich.

• Data Search – Consider a store that has a 1 million(106) item inventory. If the application is to search for an item, it must search through 1 million(106) items each time, which slows down the search. Search will become slower as data grows.
• Processor speed Although the processor speed is very high, it is limited when the data grows to billion records.
• Multiple requests Because thousands of users can search data on a web server at the same time, even the fastest server can fail while searching the data.

Data structures come to the rescue in solving the aforementioned issues. Data can be organized in a data structure so that not all items must be searched, and the required data can be found almost instantly.

Prerequisites of Data Structure

Data structure is a method of organizing data so that it can be used efficiently. The following are the fundamental terms of a data structure.

The basics of C programming language is a mandate for data structures.

Interface  – Each data structure has its own interface. The set of operations that a data structure supports is represented by the interface. An interface only provides a list of supported operations, the types of parameters they can accept, and the types of operations they can return.

Implementation –  A data structure’s internal representation is provided by implementation. The implementation also defines the algorithms used in the data structure’s operations.

Characteristics of Data Structure

• Correctness – Data structure implementation must correctly implement its interface.
• Time Complexity The running time or execution time of data structure operations should be as short as possible.
• Space Complexity – The memory usage of a data structure operation should be as small as possible in terms of space complexity.

Significances of Data Structure

There are three common problems that applications face nowadays as they become more complex and data-rich.

• Data Search – Consider a store that has a 1 million(106) item inventory. If the application is to search for an item, it must search through 1 million(106) items each time, which slows down the search. Search will become slower as data grows.
• Processor Speed – Although the processor speed is very high, it is limited when the data grows to billion records.
• Multiple Requests – Multiple requests Because thousands of users can search data on a web server at the same time, even the fastest server can fail while searching the data.

Data structures come to the rescue in solving the aforementioned issues. Data can be organised in a data structure so that not all items must be searched, and the required data can be found almost instantly.

Execution Time Cases

There are three cases that are commonly used to compare the execution time of various data structures in a relative manner.

• Worst Case : This is the scenario in which a specific data structure operation takes the longest time possible. If the worst-case time of an operation is (n), then the operation will not take more than (n) time, where (n) represents the function of n.
• Average Case : This is a scenario that depicts the average execution time of a data structure operation. If an operation takes (n) time to execute, then m operations will take (n) time to execute.
• Best Case  : This is the scenario depicting the shortest possible execution time of a data structure operation. If an operation takes (n) time to execute, the actual operation may take as much time as the random number with the highest value as (n).

Basic Terminology

• Data –  are values or a set of values.
• Item of Data – A data item is a single unit of value.
• Group Items – are data items that have been subdivided into sub-items.
• Data items – that cannot be divided are referred to as Elementary Items.
• Attribute and Entity –  An entity is something that has attributes or properties that can be assigned values.
• Entity Set –  An entity set is made up of entities with similar attributes.
• Field -A field is a single elementary unit of information that represents an entity’s attribute.
• A record –  is a collection of a given entity’s field values.
• A file – is a collection of records representing the entities in a given entity set.
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