Core Java and HR Interview Questions - Prepare For The Technical and HR Rounds of Your Dream Java Job

With the rise of generative AI technology, investing time in learning to program might seem strange. The advancements in AI technology are impressive. From autocompleting code to identifying bugs, it may seem like there is no longer a place for a programmer. After all, with a simple prompt, AI can generate complete programs much quicker than a novice programmer could ever hope to do so. Generative AI is a valuable tool that enhances the programming workflow in many ways. Code Autocompletion: Typos and misspellings are the bane of many programmers. AI tools can predict and suggest code snippets reducing the likelihood of syntax errors and inaccurate variable and function names. Code Generation from Descriptions: AI can rapidly prototype code based on a description of what is needed. While this shouldn’t be used as the final code, using it as a boilerplate can speed up development. Bug Detection: While Python gives great feedback on errors, there will be times when a programmer is left hunting for a bug with no cause in sight. AI can quickly analyze code to identify syntax errors, and sometimes even logical errors. Add to this the ability to suggest fixes, and you’ve got an invaluable tool. Documentation Generation: Good documentation is important for understanding and managing code, but it is an extra step that doesn’t always get the full attention it deserves. Thankfully AI tools can automatically generate documentation by analyzing code, although it would still need reviewing. While AI tools enhance productivity, they lack the understanding and creativity of human programmers. Despite its capabilities, it is important to understand that generative AI also has limitations. Limited Creativity While AI excels at routine tasks, it lacks the ability to think outside the box. Models like GPT-4 are trained from data pulled from the internet, so that code can be easily generated for problems that have already been solved. While AI seems to solve complex problems, it is often piecing together several similar problems or following established patterns. AI lacks comprehension and cannot synthesize new solutions. On the other hand, human insight and creativity can truly solve novel problems. Misinterpretation of Context While AI is getting better at understanding the context of a request, there is no replacement for human judgment. Nuanced or ambiguous scenarios can be misunderstood by AI, leading to a code that does not fit the requirements of the project. This can lead to situations where there is more work fixing and refining AI code than just writing it. Inconsistent Quality Programmers tend to have what could be called a coding accent. In the same way that people have consistency in the way they speak and write, programmers tend to have a coding style. AI pulls from various sources, so the exact output of the code can be unpredictable, leading to an inconsistent style. Worse than this, the code produced is of varying quality, sometimes written with best practices in mind, and other times written barely functional. Programmers still need to be involved to review the code and ensure it integrates with complex systems. Ethical and Security Risks An AI tool is only as good as the data it was trained on. Much of the code on the internet is not written with security in mind or may not consider security threats older than the training data. Without critically evaluating AI-generated code, vulnerabilities could be introduced into code bases which could put sensitive data at risk. Conclusion When it comes to programming, a balanced approach to generative AI is important. AI tools can enhance productivity by automating mundane tasks and offering enhanced debugging, but they lack the understanding and creativity that programmers have at their disposal. Programming skills will still be required for now and well into the future. Book cover of Python Essentials You Always Wanted To Know- a quintessential guide to begin your coding journey. Python Essentials You Always Wanted to Know is the perfect resource to begin your programming journey. This book guides you through fundamental coding concepts, supplemented with practical examples and case studies, helping you to apply what you have learned seamlessly. Due to Python’s versatility and active community, its applications in data science, automation, and machine learning have become indispensable. With its growing demand, now is the perfect time to level up your coding skills with Python. This blog is written by Shawn Peters, author of Python Essentials You Always Wanted To Know. Find out more about the book here: Link to the book: Python Essentials You Always Wanted To KnowAuthor: Shawn PetersPress Release: Vibrant Publishers’ New Release is a Game-Changer for Professional Growth Also read:Want to stand out for your Upcoming Python Interview?Data Structures & Algorithms Interview Q/A that can land you into your dream jobDeep Dive into Java Operators

Blockchain technology has risen to prominence, but its rapid growth has also led to a wave of misconceptions. At its core, blockchain is a decentralized, distributed ledger that records transactions across multiple computers, ensuring security, transparency, and immutability. While it was originally designed to support digital currencies like Bitcoin, its applications have since expanded across numerous industries. However, misunderstandings about blockchain—regarding its functionality, uses, and implications—can lead to confusion and misinformed opinions. In this blog, we’ll debunk some of the most common myths surrounding blockchain and highlight its broader potential. Let’s separate fact from fiction and uncover the true value of this groundbreaking technology. Myth 1: Blockchain and Bitcoin Are Synonymous One of the most prevalent myths is that blockchain and Bitcoin are the same. It’s understandable, given that many people first heard of Bitcoin before learning about blockchain. Reality: Bitcoin is the first application of blockchain technology, but the two terms are not interchangeable. Blockchain is the underlying technology that powers Bitcoin and other cryptocurrencies. It enables secure, transparent transactions without the need for third-party intermediaries. Beyond cryptocurrencies, blockchain has vast applications in industries such as supply chain management, healthcare, and voting systems, often through the use of smart contracts. Myth 2: Blockchain Is Completely Anonymous Since blockchain transactions often don’t require personal details, many assume they are entirely anonymous, leading to misconceptions about its security and regulatory implications. Reality: Blockchain transactions offer pseudonymity, meaning they are recorded with alphanumeric addresses rather than personal information, unlike traditional credit cards. However, with the right tools, these transactions can be traced back to individuals. This traceability is crucial for regulatory compliance and combating illicit activities. Myth 3: Blockchain Is Unhackable Blockchain’s decentralized nature and cryptographic security often give the impression that it’s entirely immune to hacking, creating a false sense of invulnerability. Reality: While blockchain is generally secure, it is not immune to attacks. Vulnerabilities can arise from poorly designed applications, inadequately tested smart contracts, or simple human error. There have been notable hacks in the past, proving that while the core technology is robust, the surrounding ecosystem requires vigilant management and security measures. Myth 4: Blockchain Is Too Slow for Real-World Applications Blockchain is often criticized for its slow transaction speeds compared to traditional payment systems like Mastercard or Visa. Reality: Early blockchains, like Bitcoin, indeed had slower transaction times. However, technological advancements and new consensus mechanisms have significantly improved scalability. Platforms like Ethereum 2.0 are now capable of processing thousands of transactions per second, making blockchain viable for industries ranging from finance to logistics. Myth 5: Blockchain Is Only for Tech Experts There’s a common belief that blockchain is too complex for the average person and can only be understood by those with technical expertise. Reality: Blockchain is becoming increasingly accessible, with many user-friendly applications and platforms available today. While a basic understanding of the technology can be beneficial, you don’t need to be a programmer to engage with it. As blockchain matures, more educational resources are emerging, empowering a broader audience to explore its potential. My book, Blockchain Essentials You Always Wanted to Know, is a beginner-friendly guide designed to help newcomers navigate the world of blockchain without a technical background. Blockchain Essentials is a lucid guide to understanding the fundamentals of blockchain. Myth 6: Blockchain Will Replace Traditional Systems Entirely Another widespread belief is that blockchain will eventually replace all traditional systems and industries. Reality: While blockchain offers innovative solutions, it is more likely to complement existing systems than replace them entirely. Many businesses are integrating blockchain to enhance transparency, security, and efficiency without overhauling their operations. Blockchain is considered a Web3 technology, while much of the current internet operates on Web2. As Web3 continues to evolve, it will work alongside, rather than supplant, traditional systems. Myth 7: All Blockchains Are the Same It’s often assumed that all blockchains are alike and only differ based on the cryptocurrency they support. Reality: Not all blockchains are created equal. Just as different smartphones offer unique features, various blockchains serve distinct purposes. For example, public blockchains like Bitcoin and Ethereum are open to everyone, while private blockchains restrict access to authorized users. Moreover, Ethereum supports smart contracts, while Bitcoin does not. Understanding these distinctions is key to appreciating blockchain’s diverse applications. Conclusion As blockchain technology continues to evolve, it’s important to distinguish fact from fiction. By debunking common myths, we can foster a clearer understanding of blockchain’s potential and limitations. This informed perspective will help pave the way for innovative solutions and advancements in various fields. This blog is written by Dr. Abhilash Kancharla, author of the upcoming book Blockchain Essentials You Always Wanted to Know. Dr. Kancharla is an Assistant Teaching Professor at the University of Tampa and has been working with blockchain technologies such as Ethereum and Hyperledger for over five years. Also read: Introduction to Data Structures What role does data analytics play in decision-making? Can AI take over Data Analytics?

Introduction Whether you're just starting your career or nearing its end, one undeniable truth remains: the professional landscape we navigate today vastly differs from what it was just a decade ago. Think about the tools you use daily, many of them didn't exist five or ten years ago. In this rapidly evolving digital world, the ability to adapt and thrive is more critical than ever, especially in the era of AI, blockchain, and augmented reality. Enter Python – a programming language renowned for its simplicity, versatility, and robust ecosystem. Originally favored by developers, Python has now become indispensable across various industries and professions. While it's no surprise that Python dominates fields like data science, analytics, and web development, its influence now extends to finance, healthcare, education, and beyond. Python's popularity stems from its user-friendly syntax and extensive libraries, making it an ideal starting point for beginners venturing into the world of programming. Its readability and simplicity allow newcomers to easily grasp fundamental concepts, laying a solid foundation for further learning and skill development. Consider that both of these programs take in two integer values and print out their sum. The Python program on the left is certainly easier to understand than the Java program on the right. Python's easy-to-read syntax makes it a preferred language for professionals from various fields. With Python as your starting point, you'll embark on a journey of discovery and growth, equipped with a powerful toolset to navigate the ever-evolving landscape of modern technology. Here are five compelling reasons why learning Python can supercharge your professional journey: 1 - Automating Repetitive Tasks In any profession, some tasks are necessary but dreadfully mundane. Whether it's managing emails, organizing files, or generating reports, these repetitive chores can drain your time and energy. Python comes to the rescue with its powerful automation capabilities. You can streamline workflows with Python scripts, saving time and minimizing errors. Python's versatility extends beyond your local environment, enabling you to automate tedious tasks effortlessly. 2 - Analyzing and Visualizing Data In today's data-driven world, Python plays a pivotal role in extracting valuable insights from vast datasets. Its intuitive syntax and rich toolkit enable professionals to efficiently clean, transform, and analyze data of all sizes and complexities. Python offers a variety of visualization options, from simple charts to complex plots, empowering users to represent data in an insightful manner. 3 - Enhancing Teamwork and Efficiency Python serves as a catalyst for enhancing collaboration and productivity within organizations. Through custom solutions and frameworks, Python enables streamlined communication and project management. Whether it's developing web applications or implementing chatbots for automated support, Python fosters teamwork and efficiency. 4 - Embracing Modern Solutions Python stands as a leading force in modern technology, providing adaptable solutions designed for the current digital environment. Its simplicity and flexibility render it a prime candidate for emerging technologies, including the Internet of Things (IoT) - a network of interconnected devices that communicate and share data to perform tasks more efficiently. In practical terms, professionals can utilize Python to create IoT solutions for various industries, such as smart home automation, industrial monitoring, and healthcare device integration. Python's user-friendly nature and vast libraries streamline the development process, allowing for rapid prototyping and deployment of IoT applications. 5 - Engaging in Personalized Professional Development Python opens doors to a world of opportunities for personalized growth and exploration. Whether you're interested in data analysis, web development, or machine learning, Python offers a versatile platform for showcasing your skills and pursuing your passions. Its vibrant open-source community encourages collaboration and innovation, while certifications and specializations provide avenues for advancing your career. Conclusion With Python as your toolkit, you're not merely a passive consumer of technology – you have the power to become a proactive creator, shaping the future of your profession and driving innovation forward. Galley cover of Python Essentials You Always Wanted To Know - a beginner’s guide to learning and mastering Python. For those looking to embark on this journey, Python Essentials You Always Wanted to Know is the perfect resource. This comprehensive guide breaks down complex concepts into manageable, easy-to-understand chapters, helping you to master Python one step at a time. With Python as your starting point, you will embark on a journey of discovery and growth, equipped with a powerful toolset to navigate the ever-evolving landscape of modern technology. Also read:Want to stand out for your Upcoming Python Interview?Data Structures & Algorithms Interview Q/A that can land you into your dream jobDeep Dive into Java Operators

In this blog, we will discuss the Tree as a part of Graphs, as well as Tree traversal algorithms and Special types of trees. The tree data structure is used to present a hierarchical relationship between the data. The elements of the tree are called nodes. Starting from the root (initial) node, each node has a certain number of children. The nodes higher in the hierarchy are called the parent nodes and the children are called the child nodes. The nodes having the same parent are called siblings. The node with no child node is called a leaf node. The level of a node is the depth of the tree from that node to the root node. Since the tree is a hierarchical structure, the child node at one level can act as the parent node for the nodes at the next level. The tree in which each node has a maximum of 2 child nodes is called a Binary tree. Nodes of a binary tree can be represented using a structure or a class consisting of node information and a pointer each for the left and the right child node. The process of accessing the elements of a tree is called tree traversal. There are 4 types of tree traversal algorithms: 1. Inorder traversal: For Inorder traversal, the left subtree of a node is accessed first followed by the value of the node and then the right subtree of the node. For example, consider the following tree.     The Inorder traversal of the tree in Fig 1 will be 8,4,9,2,10,5,11,1,12,6,13,3,14,7,15. 2. Preorder traversal: For Preorder traversal, the value of the node is accessed first followed by the left subtree of a node and then the right subtree of the node. The Preorder traversal of the tree in Fig 1 will be 1,2,4,8,8,5,10,11,3,6,12,13,7,14,15. 3. Postorder traversal: For Postorder traversal, the left subtree of the node is accessed first followed by the right subtree of a node, and then the value of the node. The Postorder traversal of the tree in Fig 1 will be 8,9,4,10,11,5,12,13,6,14,15,7,3,1.  4. Level order traversal: For Level order traversal, starting from the root node, the nodes at a single level are accessed first before moving to the next level. Level order traversal is also Breadth-first traversal (BSF). The Level order traversal of the tree in Fig 1 will be 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15. Binary Search Tree can also be converted to a doubly linked list such that the nodes of the doubly linked list are placed according to the inorder traversal of the tree. Special types of trees: 1. Binary Search Trees: A Binary search tree (BST) is a special type of tree in which the nodes are sorted according to the Inorder traversal. The search time complexity in a binary tree is O(log n). Insertion in a BST is achieved by moving to the left subtree if the value of the node to be inserted is lower than the current node or by moving to the right subtree if the value of the node to be inserted is greater than the current node. This process is repeated until a leaf node is found. 2. AVL Trees: AVL trees are BSTs in which for each node, the difference between the max level of the left subtree and the max level of the right subtree is not more than 1. AVL trees are also called the self-balancing BSTs. 3. Red Black Trees: Red Black trees are BSTs in which the nodes are colored either black or red with the root node always being black. No adjacent nodes in a Red Black tree can be red and for each node, any path to a leaf node has the same number of black nodes. Like AVL trees, Red Black trees are also self-balancing BSTs. 4. Trie: Trie is a special type of independent data structure which is in the form of a tree. It is generally used for string processing. Each node in a Trie has 26 child nodes indicating one of 26 English characters. Trie also has a Boolean data element marking the end of a string. The structure of a Trie can differ to incorporate various use cases. 5. Threaded Binary Trees: Threaded binary trees are used to make an iterative algorithm for the inorder traversal of a tree. The idea is to point the null right child of any node to its inorder successor. There are two types of threaded binary trees. a. Single-threaded: Only the right null child of each node points towards the inorder successor of the tree. b. Double-threaded: Both the left and the right null child of each node point towards the inorder predecessor and inorder successor of the node respectively. 6. Expression Trees: An Expression tree is a special type of tree used to solve mathematical expressions. Each non-leaf node in an expression tree represents an operator and each leaf node is an operand.   Ending note: In this blog, we discussed the Tree in data structures and Tree Transversal Algorithms used in Machine learning. Special trees help to solve different problems in optimized time and space. Overall, trees are advantageous as they depict a structural correlation between the data. Moreover, trees also provide flexible insertion and sorting advantages. Get one step closer to your dream job! Check out the books we have, which are designed to help you clear your interview with flying colors, no matter which field you are in. These include HR Interview Questions You’ll Most Likely Be Asked (Third Edition) and Innovative Interview Questions You’ll Most Likely Be Asked.

Algorithms are sequenced steps of instructions proposing a generalized solution for a problem. Algorithms determine the efficiency of a coding solution. They are divided into different categories depending on their nature of implementation. In this blog, we will discuss Sorting Algorithms focusing on their description, the way they work, and some common implementations.     Sorting Algorithm: As the name describes, the sorting algorithm is generally used to sort an array in a particular order. An array sorted in an ascending order means that every successor element in an array is greater than the previous one. A sorting algorithm takes an array as an input, performs sorting operations on it, and outputs a permutation of the array that is now sorted. Array {a, b, c, d} is alphabetically sorted. Array {1, 2, 3, 4} is sorted in ascending order. Generally, sorting algorithms are divided into two types: Comparison Sort and Integer Sort.   Comparison Sort: Comparison sort algorithms compare elements at each step to determine their position in the array. Such algorithms are easy to implement but are slower. They are bounded by O(nlogn), which means on average, comparison sorts cannot be faster than O(nlogn).     Integer Sort: Integer sort algorithms are also known as counting algorithms. The integer sort algorithm checks for each element, say x, how many elements are smaller than x and places x at that location in an array. For element x, if 10 elements are less than x then the position of element x is at index 11. Such algorithms do not perform comparisons and are thus not bound by Ω (nlogn). The efficiency of the selected sorting algorithm is determined by its run time complexity and space complexity.   Stability of a Sorting Algorithm: A sorting algorithm is said to be stable if it preserves the order of the same or equal keys in the output array as it is in the input array. Keys are the values based on which algorithm is sorting an array. Below is an example of stable sorting, Following is an unstable sorting as the order of equal keys is not preserved in the output. Next, let’s discuss some commonly used sorting algorithms.   Insertion Sort: This is a comparison-based algorithm. It takes one element, finds its place in the array, places it there, and in doing so sorts the whole array. For an array of size n, insertion sort considers the first element on the left as a sorted array and all the remaining n-1 elements on the right as an unsorted array. It then picks the first unsorted element (element number 2 of the array) and places it with a sorted element on the left moving elements if necessary. Now there are two arrays, a sorted array of size 2 and an unsorted of size n-2. The process continues until we get the whole array sorted, starting from the left. The best case of insertion sort is O(N) and the worst-case O(N^2).     Selection Sort: Selection sort is quite an easy algorithm in terms of implementation. It selects the smallest element present in an array and replaces it with the first element. It again scans for the smallest element in the remaining n-1 array and replaces it with the second element or the first element of the unsorted (n-1) array. The process of selecting the smallest element and replacing it continues until the whole array is sorted. The selection sort algorithm has the best and worst-case of O(N^2).     Merge Sort: Merge is a comparison-based algorithm that works on merging two sorted arrays. The technique used by the merge sort is divide and conquer. It divides the array into two subarrays, performs sorting on them separately, either recursively or iteratively, and then merges these two sorted subarrays. The result is a sorted array. Merge sort works in O(nlogn) run time.     Heap Sort: The comparison-based heap sort algorithm uses a binary heap data structure for sorting an array. A max-heap is formed from an unsorted array. The largest element from the binary heap is selected. As it is max-heap, the root is the largest value. This maximum value is placed at the end of an array. The heap shrinks by 1 element and the array increases by 1 element. Again, the above process is applied to the remaining heap. That is, convert it into max-heap and then replace the root (maximum) element with the last element. The process is repeated till we get a sorted array and the heap is shrunk to 0 elements. The run time of heap sort is O(nlogn).     Quick Sort: Quicksort works on the divide and conquer strategy. It selects a pivot element and forms two subarrays around this pivot. Suppose the pivot element is A[y]. Two subarrays are sorted as A[x,… y-1] and A[y+1,… z] such that all elements less than the pivot are in one subarray, and all elements greater than the pivot are in the second subarray. The subarrays can be sorted recursively or iteratively. The outcome is a sorted array. The average run time complexity of a quick sort is O(nlogn).     Bubble Sort: This comparison-based sorting algorithm compares elements of an array in pairs. The algorithm ‘bubbles’ through the entire array from left to right, considering two elements at a time and swapping the greater element with the smaller element of the pair. For an array A, element A[0] is compared with element A[1]. If element A[0] > A[1], they are swapped. Next, elements A[1] and A[2] are compared and swapped if required. These two steps are repeated for an entire array. The average run time complexity of Bubble sort is O(n2) and is considered an inefficient algorithm.     Shell Sort: Shell sort algorithm, in a way, works on insertion sort. It is considered faster than the insertion sort itself. It starts by sorting subsets of the entire array. Gradually the size of subarrays is increased till we get a complete sorted array as a result. In other words, shell sort partially sorts the array elements and then applies insertion sort on the entire array.   Shell sort is generally optimized using different methods to increase the size of subsets. The most commonly used method is Knuth’s method. The worst case of shell run time is O(n^(3/2) using Knuth’s method.     Distribution Sort Algorithms: Sorting algorithms where input is distributed into substructure, sorted, and then combined at the output are distribution sort algorithms. Many merge sort algorithms are distribution sort algorithms. Radix sort is an example of distribution sorting. Counting Sort: Counting sort is an integer-based sorting algorithm instead of a comparison-based sorting algorithm. The algorithm works on the assumption that every element in the input list has a key value ranging from 0 to k, where k is an integer. For each element, the algorithm determines all the elements that are smaller than it, and in this way places that element at an appropriate location in the output list. For this, the algorithm maintains three lists – one as an input list, the second as a temporary list for key values, and the third as an output list. Counting sort is considered as an efficient sorting algorithm with a run time of Θ(n) where the size of the input list, n, is not much smaller than the largest key value of the input list, k.   Radix Sort: Radix sort works on the subarrays and uses the counting sort algorithm to sort them. It groups the individual keys that take the same place and value. It sorts from the least significant digit to the most significant digit. In base ten, radix sort will first sort digits in 1’s place, then at 10’s place, and so on. The sorting is done using the counting sort algorithm. Counting sort can sort elements in one place value. As an example, base 10, can sort from 0 to 9. For 2-digit numbers, it will have to deal with base 100. Radix sort, on the other hand, can handle multi-digit numbers without dealing with a large range of keys. The list [46, 23, 51, 59] will be sorted as [51, 23, 46, 59] as for 1’s place value 1<3<6<9. Sorting of second place value will give [23, 46, 51, 58]. Another important property of the radix sort is its stability. It is a stable sorting algorithm. The runtime of radix sort is O(n) which means it takes a linear time for sorting.   Ending Note: Sorting algorithms are quite important in computer science as they help in reducing the complexity of the problem. The sorting algorithms that we discussed above have useful applications in databases, search algorithms, data structure, and many other fields of computer science.     Get one step closer to your dream job! Prepare for your interview by supplementing your technical knowledge. Our Job Interview Questions Book Series is designed for this very purpose, aiming to prepare you for HR questions you’ll most likely be asked. Check out the book here! panels.

When contemplating opportunities for Java professionals, many dismiss it as an outdated technology option, thinking, will Java remain in demand? Let us dispel this illusion for you with some illuminating facts. According to CODEGYM, the Java job market had more opportunities in the year 2020 than ever before, regardless of the global pandemic crisis. The market witnessed several Java job vacancies. Even the job switch rate for Java professionals is less than 8%, compared to 27% for the software development profession as a whole and 35% for database administrators. Also, given a higher-level administrative role, a significant number of Java developers simply prefer to retain their position. If this isn’t the best proof that Java programming is the ideal career choice for the vast majority of professionals, we don’t know what is. We can state that the future for Java professionals is as bright as the rising sun. Now that it’s pretty clear the Java job market is not going anywhere, Java job vacancies will witness an increasing trend in the coming decade. So, let us begin with what this beloved programming language has in store for all Java Job seekers out there. What can Java professionals expect from the Java job market in the near future? According to reports, the IT industry will witness significant job shifts, resulting in numerous Java job vacancies. With the advancement of the tech industry, the Java job market will experience a favorable turn as many corporations rely on this programming language to create their core offerings. Java job vacancies reach a rather high ranking with every season. The above facts back up the Java obsession. Fresh graduates and experienced professionals remain inclined towards Java. Presently, there are over 7 million Java developers, with around 0.5 million new coders joining the Java community each year. This showcases the level of aggressive competition in the field for Java developers. How can Java professionals achieve lucrative jobs in the cut-throat Java job market? Java is unquestionably an excellent way to begin for people who are new to coding. Even the Java professionals who have sustained in the market till now need to stay updated with the market needs to scale their careers to new heights. However, getting past the screenings and interviews for a Java job is no cakewalk. Coders have to go through various technical rounds of tests and interviews. Adequate preparation of commonly asked questions in a Java job interview is crucial for selection. One can unearth smart tactics from specially compiled books that put the candidate in charge and push them to do their best. Acquiring additional skills in Java-related technologies can also provide you an edge in the Java job market. What is the scope for other Java-related technologies/platforms? Java professionals can multiply their prospects for a high-paying job by upgrading their skills and mastering related tools and frameworks. This language has several frameworks (VUE.js, jQuery, Angualr.js, and React.js) with a strong presence in the market and is continually expanding. As per the Devskillers report, Javascript stands as the most sought-after IT skill among developers worldwide. At least 80 percent of the websites rely on a third-party JavaScript web framework or library to perform client-side scripting tasks. To understand the potential of Java in the job market, it is also necessary to know how it fairs in contrast to other popular languages. So, before we move further, let’s take a peek at Java’s performance in contrast to other competing languages such as Python and C++. Where does Java stand in comparison to other eminent languages like Python and C++? The most common programming languages used besides Java are C++ and Python. These have been the foundation for almost all prominent development projects worldwide. C++ has grown in popularity as a fast and compiled programming language, and it is among the first languages that a newbie programmer learns. However, in contrast to Java, the language has lower portability. C++ programmers are anticipated to have a satisfactory amount of opportunities at least until 2022. Candidates with substantial expertise will have bright prospects and multiple avenues in C++ programming. On the other hand, Python is an interpreted language that is both current and quick to type. The code is 3-4 times shorter than that of Java. There has always been a tussle between Python and Java in terms of their demand. Python programmers have a very strong job market for web development projects. As a result, there is always business for a Python coder. Java remains popular because of its platform independence. Programmers have used this language to give life to many popular apps and software systems. Java is also being employed to create solutions for machine learning, genetic programming, and multi-robotic systems, all of which have a promising future—no wonder the Java job market continues to thrive. Conquer greater heights in the Java job market with robust preparedness! Some of the world’s biggest companies, such as Twitter, Google, LinkedIn, eBay, and Amazon, use Java to establish a coherent architecture between their web application and backend systems. This fact emphasizes the scope and appeal of Java Programmers. As a result, the salaries of Java Programmers in many countries are among the highest in the computer and internet networking industries. Artificial Intelligence, Big Data, and Blockchain are some of the new technologies where the scope of Java is likely to expand. Therefore, being on your feet and educating yourself to meet industry standards improves your chances of landing a successful job in Java programming. A great deal of preparation with assistance from Vibrant Publishers will help you accomplish this goal. Remember friends, the best way to predict your future is to create it! We wish you success! The new edition of the book, Core Java Interview Questions You’ll Most Likely Be Asked, released in Sep 2021, has all the goods of the old edition plus additions like the latest Java interview questions, scenario-based questions, and a tutorial on building an ATS-compliant resume. The book is ideal for job seekers with zero to five years of experience.

Searching algorithms belong to a category of widely used algorithms in Computer Science. Searching algorithms are mainly used to lookup an entry from data structures, such as linked lists, trees, graphs, etc. In this blog, we will discuss the working, types, and a few implementations of searching algorithms. Searching Algorithm: A searching algorithm is used to locate and retrieve an element – say x – from a data structure where it is stored – say a list. The algorithm returns the location of the searched element or indicates if it is not present in the data structure. Searching algorithms are categorized into two types depending upon their search strategy. Sequential Search: These algorithms linearly search the whole data structure by visiting every element. These algorithms are, therefore, comparatively slower. An example is a linear search algorithm. Interval Search: these algorithms perform searching in a sorted data structure. The idea is to target the center of the data structure and then perform a search operation on the two halves. These algorithms are efficient. An example is a binary search algorithm. Next, let’s discuss some commonly used searching algorithms. Linear Search: The very basic searching algorithm traverses the data structure sequentially. The algorithm visits every element one by one until the match is found. If the element is not present in the data structure, -1 is returned. Linear search applies to both sorted and unsorted data structures. Although the implementation is quite easy, linear search has a time complexity of O(n), where n is the size of the data structure. It means it is linearly dependent on the size and quite slow in terms of efficiency. Binary Search: Binary search works on a sorted data structure. It finds the middle of the list. If the middle element is the required element, it is returned as a result. Otherwise, it is compared with the required element. If the middle element is smaller than the required element, the right half is considered for further search, else the left half is considered. Again, the middle of the selected half is selected and compared with the required value. The process is repeated until the required value is found. Binary search with time complexity of O(nlogn) is an efficient search algorithm, especially for large and sorted data records. Interpolation Search: Interpolation search is a combination of binary and linear search algorithms. Also called an extrapolation search, this algorithm first divides the data structure into two halves (like binary search) and then sequentially searches in the required half (linear search). In this way, it saves from dividing the data structure into two halves every time as in a binary search. For an array, arr[], x is the element to be searched. The ‘start’ is the starting index of the array and ‘end’ is the last index. The position of the element x is calculated by the formula: position = start + [ (x-arr[start])*(end-start) / (arr[end]-list[start]) ] The position is calculated and returned if it is a match. If the result is less than the required element, the position in the left sub-array is calculated. Else, the position is calculated in the right sub-array. The process is repeated until the element is found or the array reduces to zero. Interpolation search works when the data structure is sorted. When the elements are evenly distributed, the average run time of interpolation search is O(log(log(n))). Breadth-First Search Algorithm: BFS is used to search for a value in a graph or tree data structure. It is a recursive algorithm and traverses the vertices to reach the required value. The algorithm maintains two lists of visited vertices and non-visited vertices. It is implemented using a queue data structure. The idea is to visit every node at one level (breadth-wise) before moving to the next level of the graph. The algorithm selects the first element from the queue and adds it to the visited list. All the adjacent vertices (neighbors) of this element are traversed. If a neighbor is not present in the visited list, it means it is not traversed yet. It is placed at the back of the queue and in the visited list. If a neighbor is present in the visited list already, it means it has been traversed and is ignored. The process is repeated until the required element is found or the queue is empty. The following example will make the concept clear of breadth-first traversal.       Depth First Search Algorithm: This is another recursive search algorithm used for graphs and trees. In contrast to the BFS algorithm, DFS works on the strategy of backtracking. The algorithm keeps on moving to the next levels (depth-wise) until there are no more nodes. The algorithm then backtracks to the upper level and repeats for the other node. The DFS algorithm is implemented using a stack data structure. The algorithm pops an element from the top of the stack and places it in the visited list. All the nodes adjacent to this element are also selected. If any adjacent node value is not present in the visited list, it means it is not traversed yet, and it is placed at the stack top. Steps are repeated until the element is found or the stack is empty. See the following example for a better understanding of depth-first traversal.       Next, let’s discuss some common algorithm solutions for searching problems. Problem 1: Find the largest element in an array of unique elements. Algorithm: We can use Linear Search to find the largest element in an array of size n. The time complexity would be O(n). Initialize an integer value as max = 0. In a loop, visit each element in an array and compare it with the max value. If the element is greater than the max, swap it with the max value. When the loop ends, the max will contain the largest element of the array. Problem 2: Find the position of an element in an infinite sorted array Algorithm: A sorted array indicates that a binary search algorithm can work here. Another point to note is that the array is infinite. It means we don’t know the maximum index or upper bound of the array. To solve such a problem, we will first find the upper index and then apply the binary algorithm on the array. To find the upper bound/index of the infinite array: Initialize integer min, max, and value for minimum, maximum, and first array element. Set min, max, and value as 0, 1, and arr[0]. Run a loop until the value is less than the element we are looking for. If the value is less than the element swap min with the max, double the max, and store arr[max] in value. At the end of the loop, we get lower and upper bounds of the array in min and max. We can call binary search on these values. For binary search: If the max value is greater than 1, find mid of the array by min + (max -1)/2. If mid is the value we are looking for, return mid. If mid is less than the search value, call binary search on the left half of the array. Take mid-1 as the maximum bound. Else, call binary search on the right half of the array, and take mid+1 as the minimum bound.   Problem 3: Find the number of occurrence for an element in a sorted array Algorithm: To find how many times the element appears in a sorted array, we can use both linear and binary search. In linear search, maintain two integer variables result and key. Loop over the entire array, on every occurrence of the key, increment the result by 1. At the end of the loop, the result contains the number of occurrences of the key. Problem 4: Find if the given array is a subset of another array: Algorithm: For this problem, we don’t know if the array is sorted or not. The algorithm for linear search includes running two loops for array 1 and array 2. The outer loop selects elements of array 2 one by one. The inner loop performs a linear search in array 1 for array 2’s element. The complexity of this algorithm is O(mxn), where m and n are the sizes of array 1 and array 2, respectively. The problem can also be solved using binary search after sorting the array. First sort array 1. The time complexity of sorting is O(m log m). For each element of array 2, call binary search on array 1. Time complexity is O(n log m). The total time complexity for the algorithm is O(m logm + n logm), where m and n are the sizes of arrays 1 and 2, respectively. Ending Note: In this blog on searching algorithms, we discussed a very basic algorithm type. Search algorithms are the most commonly used algorithms, as the concept of searching data and records is quite common in software development. The records from databases, servers, files, and other storage locations are searched and retrieved frequently. Efficient searching algorithms with efficient time and storage complexity play a key role here. Get one step closer to your dream job!   Prepare for your programming interview with Data Structures & Algorithms Interview Questions You’ll Most Likely Be Asked. This book has a whopping 200 technical interview questions on lists, queues, stacks, and algorithms and 77 behavioral interview questions crafted by industry experts and interviewers from various interview panels.

The concept of data compression is very common in computer applications. Information is transmitted as a bit-stream of 0’s and 1’s over the network. The goal is to transmit information over the network with a minimum number of bits. Such transmission is faster in terms of speed and bandwidth. Huffman Coding is one such technique of data compression. This blog discusses Huffman coding, how it works, its implementation, and some applications of the method.   What is Huffman Coding? This data compression method assigns codes to unique pieces of data for transmission. The data byte occurring most often is assigned a shorter code whereas the next most occurring byte is assigned a longer code. In this way, the whole data is encoded into bytes. The two types of encoding methods used by Huffman coding are Static Huffman encoding and Dynamic Huffman encoding. Static Huffman encoding encodes text using fixed-sized codes. Dynamic Huffman coding, on the other hand, encodes according to the frequency of data characters. This approach is often known as variable encoding.   Huffman Coding Implementation: Huffman coding is done in two steps. The first step is to build a binary tree of data characters depending on the character frequency. The second step is to encode these binary codes in bits of 0’s and 1’s.   Building a Huffman Tree: The binary tree consists of two types of nodes; leaf nodes and parent nodes. Each node contains the number of occurrences of a character. The parent node or internal node has two children. The left child is indicated by bit 0 and right the child by bit 1. The algorithm for building a Huffman binary tree using a priority queue includes the following steps: 1- Each character is added to the queue as a leaf node. 2- While the queue is not empty, pick two elements from the queue front, and create a parent node with these two as child nodes. The frequency of the parent node is the sum of these two nodes. Add this node to the priority queue. Consider the following example for a better understanding. Suppose we have characters p, q, r, s, and t with frequencies 4, 6, 7, 7, and 16. Now, build the binary tree:       Encoding the Huffman binary tree: As discussed above, the left nodes are assigned 0 bit and the right nodes 1 bit. In this way, codes are generated for all paths from the root node to any child node.   Huffman Compression technique: While building the Huffman tree, we have applied the technique of Huffman compression. It is also called Huffman encoding. We have encoded the data characters into the bits of 0s and 1s. This method reduces the overall bit size of the data. Hence, it is called the compression technique. Let’s see how our above data characters p, q, r, s, and t are compressed.     Considering each character is represented by 8 bits or 1 byte in computer language, the total bit size of data characters (4 p, 6 q, 7 r, and 16 t) is 40 * 8 = 320. After the Huffman compression algorithm, the bit size of the data reduces to 40 + 40 + 88 = 168 bits.   Huffman Decompression Technique: For the decompression technique, we need codes. Using these codes, we traverse the Huffman tree and decode the data. We start from the root node, assign 0 to the left node, and 1 to the right node. When a leaf node is encountered, we stop.     To decode character t, we will start from the root node, and traverse the path of 111 till we reach a leaf node that is, t.   Time Complexity of Huffman Coding Algorithm: As Huffman coding is implemented using a binary tree, it takes O(nlogn) time, where n is the number of data characters compressed.   Ending Note: In this blog on Huffman coding, we have discussed an algorithm that forms the basis of many compression techniques used in software development. Various compression formats like GZIP and WinZip use Huffman coding. Image compression techniques like JPEG and PNG also work on the Huffman algorithm. Although it is sometimes deemed as a slow technique, especially for digital media compression, the algorithm is still widely used due to its storage efficiency and straightforward implementation.   Get one step closer to your dream job!   Check out the books we have, which are designed to help you clear your interview with flying colors, no matter which field you are in. These include HR Interview Questions You’ll Most Likely Be Asked (Third Edition) and Innovative Interview Questions You’ll Most Likely Be Asked.