Organizations usually have large data sets with thousands or millions of elements. It is practically impossible for them to find out a specific solution or result within a dataset without the intervention of AI. Therefore, searching algorithms form an essential component in AI. They make it easier for organizations to go through a vast amount of information and find out whether a particular element is present in a data set and its exact data position.
Searching algorithms are classified into two main categories, namely, sequential search and interval search. Sequential search is referred to as linear search, whereas interval search is also called binary search. In linear search, the algorithm goes series-wise through each item mentioned in the dataset till it finds the required element. The linear search algorithm is beneficial in searching through a data set that is not arranged.
Since the search algorithm goes through every item, it takes time to produce the desired results. Therefore, it is rarely used. Organizations mostly use the binary search algorithm. Let us know more about the same.
What is Binary Search Algorithm?
A binary search algorithm is an interval search algorithm widely used in a sorted array to determine the position of a particular value or element. A sorted array is a dataset in which elements are arranged in a specific periodic, alphabetic, or numeric order.
Here is an example to help you better understand the concept of the binary search algorithm. Suppose you need to find a word in a dictionary. In that case, you can use the binary search algorithm to find out the exact position of the word because the words in a dictionary are arranged in alphabetical order.
On the contrary, if you want to find a particular word in a book in which the words are arranged in an order, you will have to use the linear search algorithm.
A numerical dataset should be arranged in an ascending or descending manner to use the binary search algorithm. If the dataset consists of words, they should be present in the alphabetical arrangement.
Applications of Binary Search Algorithm
The binary search algorithm is considered one of the best searching algorithms because of its efficiency. Following are some of the practical applications of the binary search algorithm.
1. Search Tree
The binary search algorithm is used to find specific information from large data sets like dictionaries and telephone directories.
2. Debugging a Program
While testing a program, when you come across a bug at a particular event, you can use the binary search algorithm and enter a range to find the exact position of the bug instead of rerunning the entire code.
3. Saves Memory
XA practical application of the binary search tree is that it saves storage space. Since the algorithm finds a reasonable range within a dataset within which an element will be present, it keeps only the required values while discarding the other elements.
How To Implement Binary Search Algorithms?
Binary search algorithms are easy to implement. Instead of going through the entire search result, the algorithm first checks the middle element and then proceeds to find out the exact position of the element. Here is how you can understand it.
The binary search algorithm compares the middle element in the sorted array to find an element in a numerical dataset. There are usually three possibilities that arise from this. In the first case, the middle element matches the key that is being searched. The second probability is that the position of the key element is located after the middle one. In the last case, the key element is placed before the middle element in the sorted array.
If the algorithm was searching for the same element positioned in the middle, the search would end. However, in the second and the third cases, the algorithm decides if the key element is greater or smaller than the middle element. It then searches through the first half or last half accordingly. If the element is not present in the dataset, the binary search algorithm will show the ‘dataset not found’ result.
By first checking the middle element, the binary search algorithm helps to reduce time. It cuts down the search area by deciding whether the element will be present in the first or the second half.
Binary Search Tree and Search Operation
Now that you have learned about the binary search algorithm let us understand the concept of a binary search tree. The binary search algorithm divides a sorted array into parts that make searching easier and quicker.
Before that, you must first know about the concept of data trees in programming. Trees are hierarchical structures that store data in the form of nodes connected through edges. You can consider it as the branches of the tree. The first node of the tree is called the parent node, and the other nodes connected to it are referred to as the child nodes.
In a binary search tree, each parent has a maximum of two child nodes. The trees are divided into the middle data item, the left child node, and the right child node. It is a sorted numerical dataset in which the value of the left node is less than the value of the middle element. Similarly, the value of the right node is greater than the middle element.
The binary search tree helps in finding the exact position of the required element. The middle element is observed first. If the value does not match the required element, the algorithm will check in the left or right node. Only the left node will be considered if the item’s value is less than the middle element. However, if the element’s value is greater than the middle element, we need to only go through the right node. The left one will be discarded.
Limitations of Binary Search Algorithm
Even though the binary search algorithm has several advantages, there are certain limitations too.
- To implement the binary search algorithm, you must have a sorted array. If the dataset is not arranged alphabetically or numerically, it becomes impossible to implement the binary search algorithm.
- Binary search algorithms are not beneficial for small unsorted arrays as they will require a lot of time to sort the dataset. In such cases, the linear search algorithm is a more practical choice.
- Binary search algorithms may not tell us the exact position of an element as a Linear search algorithm because it goes only through one part of the dataset.
Career Opportunities After Learning Binary Search Algorithm
The binary search algorithm in computer science is related to the data structure. Therefore, if you pursue a master’s of computer science in data science, you can take up the following career roles:
- Data engineer or developer
- Data modelling jobs like experimental design and structured modelling
- Data analytics like machine learning and recommender systems
How Can You Learn the Practical Application Of Binary Search Algorithms?
Knowledge of Binary Search Algorithms is a must if you want to pursue career opportunities in computer science. For this, you must have a bachelor’s degree in computer science with excellent credentials. A master’s degree in computer science gives you an edge as you get a chance to gain more knowledge on the subject.
Anyone looking for a master’s course to learn the fundamentals of binary searching algorithms and their practical application can go for the Master of Science in Machine Learning & AI course offered by upGrad.
It is offered in association with the Liverpool John Moores University, ranked amongst one of the top 50 universities in the UK. If you are new to programming, upGrad also offers pre-program preparatory content that introduces Python, data visualization, data analysis, and more crucial concepts.
In addition to this, you will also get a chance to work on more than 12 case studies and projects. Students also get to enjoy live sessions with experts and mentors, peer-to-peer learning opportunities, and personalized mentorship for their career growth.
Conclusion
Binary search algorithms are a crucial concept in programming. If you are interested in data science and machine learning, it is best to learn in-depth about binary and other search algorithms that will help you in your career ahead. Along with theoretical knowledge, you will also need practical knowledge of this topic.
