An Overview of Data Structures and Algorithms for Beginners
If we want to perform any kind of operation using data then the data structure is extremely necessary for us because it can help us gather and organize data in a specific form. With the help of data structure, you will be able to exhibit elements of the data by creating a relationship between them.
For example, we have data consisting of the player’s name (Bairstow) and their age (25). In this example, the name of the player will be considered string type of data and his age will be considered integer type of data. Similarly, we can create a database, file, or structure where we can collect and organize the name and ages of players.
To put it in more simple terms,a data structure is used to store structured data so that you can conduct multiple operations on that data very easily. The data structure should be designed in a way to reduce the complexity and increase the efficiency of the data.
What is an algorithm?
An algorithm on the other hand is a set of written order, logic, or instruction that can help in attaining a specific prearranged task. Algorithms cannot be considered a complete program or code but the logic or solution of a problem. This solution can be demonstrated using a flowchart, as an informal high-level description, or as a pseudocode.
Algorithms are a very popular term that you may have come across while studying mathematics or computer programming. It is a step-by-step process of conducting certain calculations or solving problems. Algorithms have pre-built rudimentary languages and that is why they can be used in more than one programming language. It has multiple use cases such as automated reasoning, data processing, solving mathematical problems, etc.
A good algorithm is very advanced in the phrase of time and space. Nevertheless, different types of algorithms require separate algorithms. Suppose, you are trying to cook a new dish but for that, you need to read the instructions. After reading the instructions you will be able to follow the steps to complete the recipe and your food will be ready.
Similarly, the work of an algorithm is to help in managing the task and get the normal output as a result. It is language-dependent and that is why it can only follow simple instructions.
Different types of algorithms
There are a total of seven types of Algorithms and all of them are discussed below.
Brute force Algorithm
It is a type of algorithm that primarily tries out each and every chance until it can receive an acceptable result. It can be considered the most simple and fundamental type of algorithm. Brute force algorithm is utilized to evaluate the best or perfect solution among all the possible solutions.
When you are unable to find a proper answer to an issue then you can use brute force algorithms to search for a complying but not best solution. This particular type of algorithm can provide us with the first approach we can take after observing any problem.
As the name suggests this kind of algorithm is very much dependent upon recursion. In this case, when an issue arrives, it is analyzed by distributing it into subproblems and then it gets straightened out with the aid of primary conditions.
Firstly, it solves the primary case in a justified way and then reappears with a more simple solution or approach. A recursive algorithm is used to tackle problems that can be further divided into simple problems of the same.
Dynamic Programming Algorithm
Dynamic Programming Algorithm has another name, which is memoization technique because this technique stores the recently evaluated algorithm so that instead of trying to figure the solutions repeatedly.
This kind of algorithm focuses on covering the more unpredictable issue of a problem. To put it in more simple terms, any dynamic programming algorithm reserves the previous outcomes in order to uncover new results.
Divide and Conquer Algorithm
As the name of the algorithm suggests, the Divide and Conquer algorithm approaches the issue in two different areas. In the first section, the issue is divided into subproblems and in the second part, the ordinary issue is tackled. Later on, both sections are combined in order to create the final outcome.
In the greedy algorithm, the outcome is derived fragment by fragment. The algorithm does not consider any option that has been taken lately and it is used to search for instant solutions.
The backtracking algorithm tries to find out the solution to an issue in a more steady or stabilized manner. Using the same algorithm, you can steadily approach the solution while diminishing the solutions that do not satisfy the condition of the issue at any given time.
A randomized Algorithm takes in any random number at the time of calculation in order to find out the solution to a problem.
Different characteristics of Algorithms
Every algorithm follows certain characteristics and some of them are discussed below:
- Input specified: Input is the particulars provided to get the output and the information can be converted during the evaluation. The exactness of the input determines your understanding of the information and what kind of formation the information should possess.
- Output specified: Output is the end result of the calculation. You can do one or more than one output as a result of the calculation.
- Unambiguous: Any perfect algorithm can be regarded as unambiguous because the instructions are straightforward and clear.
- Effectiveness: Each and every instruction in an algorithm affects the entire process of calculation, therefore it needs to be adequate.
Practical application of Algorithms
Here is a list of practical applications for algorithms:
- The Internet which has become an important part of our everyday life is the result of creative and precocious algorithms. With the assistance of algorithms, numerous websites can operate on the internet.
- Everyday financial activities in the eCommerce platforms and our financial data which includes passwords, OTPs, debit or credit card numbers, etc. All these facilities we enjoy are due to complex mathematical algorithms.
- Multiple software and applications also utilize algorithms. For example, popular apps like Netflix, Amazon, etc. use algorithms for providing us with movie or series recommendations.
- Social media platforms such as Meta, Instagram, Twitter, etc. utilize algorithms to determine which type of content people will like or dislike.
Understanding Data Structure
Before delving into data structures, let’s understand what is “data.” Data is optimized information in the configuration of facts, figures, and numbers that are stored in computers. Just like the human brain stores valuable information throughout our lives, the computer has its own memory wherein it holds data for further processing.
Originating from data, the data structure is a certain manner of organizing data into specialized format computers so that the raw information can be organized, categorized, stored, processed, and retrieved as per the needs of the user. Data structures are a means of effective information handling, making the raw data accessible and easy for usage.
In computer science, every application, software, and program comprises two founding elements: (i) Algorithms, and (ii) Data. While data is information, algorithms are sets of instructions and rules that transform the raw data into useful components for further programming. To piece it all together, you can simply remember these two simple equations:
- Set of related data + Set of permissible operations on the data = Data Structures
- Data structures + Algorithms = Programs
Understanding The Relationships Between Data Types and Data Structures
Data structures are categorized under three basic data types for a comprehensive understanding of information:
- Abstract – Data that is defined by its behavior is called abstract data. Examples are queues, graphs, sets, and stacks.
- Composite – Also known as compound data, this data combines primitive data types like records, arrays, classes, structs, and strings.
- Primitive – Primitive data consists of basic data such as Boolean, integers, characters, pointers, fixed and floating-point numbers.
All the three above data types are considered the building blocks of data structures. The relation between data types and data structures is that data types tell the interpreter or programmers how they can plan on using the data. Data types also assist data analysts in mapping and choosing from among different data structure classifications to select the best structure suited to their needs.
Types of Data Structures
Data structures are categorized into three broad structural classifications, each of which has their own characteristics. Let’s take a look at the types of data structures:
- Linear and Nonlinear – Linear data structures arrange data into a linear sequence as can be seen in an array, queue, or list. Nonlinear data structures are not sequential but tend to connect to several informational objects in a tree or a graph.
- Static and Dynamic – As the name suggests, static data structures are of fixed and permanent structures as well as sizes at compile time. In this, the array set has a significant portion of collected memory which is constructed by the programmer in advance.
Dynamic data structures are those that have loosened memory size that keep on shrinking or expanding to fit the program’s execution requirements. Also, in dynamic data, the location of the associated memory might change.
- Homogeneous and Non-Homogeneous – Homogeneous data structures contain the matching data element type as element collections that you can find in an array. In non-homogeneous structures, data doesn’t have to be of the matching type, like the structures.
Components of Data Structures
Till now we were dealing with data structure and data types categories, this section is about the several components of data structures. Let’s walk through the same below:
Array – An array is an assemblage of multiple data items of the matching type that are preserved all together in adjoining memory locations. Here, each data item is considered as an “element.” Arrays form the most fundamental part of the data structure.
Graphs – Graphs are nonlinear and pictorial representations of data sets. Graphs have finite node sets and also known as vertices that are adjoined by links (alternately called edges). Trees are a variety of graphs except that they don’t have rules to govern how the nodes connect.
Hash Tables – Hash tables are alternately known as hash maps. Hash tables can be used both as nonlinear or linear data structures, but they usually favor the former. The hash table’s structure is mostly built using arrays and helps map keys to values.
Linked List – Linked lists store data item collections in a straight order. Every component in a linked list contains data items and links to the succeeding item on the list.
Stack – Stack stores troupes of data items in a straight manner to be used when applying operations.
Queue – Queue stores item packs in a linear, sequential manner just like stacks, but the functional order should be “first in, first out” only.
Tree – Tree stores item collections in abstract hierarchies with multilevel data structures using nodes. The base nodes are known as “leaf nodes,” while the uppermost node is called the “root node.” Every single node through its pointers points to adjacent nodes.
Trie – Trie, different from ‘Tree’, is a data structure that stores strings placed in a pictorial graph. Tries are also known as keyword trees or prefix trees.
Applications of Data Structures
Data structures have multiple applications in IT processes including the following:
- Data Storage – Data structures fuel efficient data persistence, such as identifying indicator collections and listing as per corresponding structures thus proving to be extremely useful in database management systems for storing records.
- Resource Management – Data structures like linked lists reinforce the performance of functions like processing scheduling queues, file directory management, and memory allocation – all of which form the core of resources and services management in bigger corporations.
- Scalability – Big data applications are hugely dependent on data structures for data storage across distributed storage locations, thus enhancing their scalability and high performance.
- Data Exchange – Organized information as in data structures make it convenient to be distributed between several applications like TCP/IP packets.
Why should you learn data structures and algorithms?
As digitization drives programming in every field from marketing to aviation to travel, the need for IT-related skills is more urgent than ever. Wherever you see, programming has been implemented to make lives easier. And data structures and algorithms are the fundamentals aspects of any piece of program or computer code.
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