Blogs On Programming

Cybersecurity has become a huge challenge due to technology playing a major role in our lives. Though technology often provides a lot of benefits, its extensive use has also brought along a huge rise in cyberattacks. This has, in turn, led to both personal and professional data being at risk, hence the need for people to stay informed. What are cyberattacks?  A common question that is often asked is: What constitutes a cyberattack? The answer is anything that disrupts the function of computer systems. The most common cyberattacks include the following: Malware: Malware is short for malicious software and refers to any unwanted software that has the potential to inflict damage to systems, capture data, and perform other nefarious deeds. Social engineering:  This is a collective term that involves the use of deception in which a cybercriminal convinces the target to divulge sensitive personal information. The attacker will then use the acquired information to inflict damage on organizational systems. Social engineering attacks include phishing and masquerading. Denial-of-Service (DoS) Attacks:  This is a group of cyberattacks that prevent authorized users from accessing services by rendering the system unusable. A distributed version of such kind of attackers can involve multiple attackers as well. Rendering systems that are unavailable can put an organization in a very precarious position, sometimes leading to permanent closure.  Cybersecurity Essentials You Always Wanted to Know by Vibrant Publishers Essentials of Cybersecurity In case of the current digital landscape and the growing risk of cyberattacks, people need to be informed. Cybersecurity Essentials You Always Wanted to Know seeks to address this knowledge gap. It aims to make everyone aware of what cybersecurity entails and its importance. The result should be a reduction in cyber incidents and attacks. Cybersecurity Essentials takes a programmatic approach to assist you in understanding the discipline of cybersecurity.  The discipline of cybersecurity comprises the following key elements: Cybersecurity, Governance, Risk, and Compliance (GRC): Cybersecurity GRC is a crucial part of any cybersecurity program in an organization, as the success of any such program relies on the strength of these elements. GRC lays the foundation for other actions meant to enhance cybersecurity. Database security: Organizations often handle a lot of personal and customer information and data. This includes personal details, financial documents, and sensitive business information. Such information, when compromised by an attacker, might lead to cases of impersonation and fraud. As this information is typically kept in databases, database security becomes of utmost importance. Business resilience: Business resilience is the organization’s ability to detect incidents and disasters and recover from such assists in maintaining business continuity. Cyber incidents disrupt business operations. Effective cybersecurity ensures smooth operations and uninterrupted continuity of businesses. Cloud security: Due to the advantages of the cloud, including convenience and cost-effectiveness, we are seeing a lot of both businesses and individuals moving operations to the cloud. Customers and clients have trust in cloud platforms as they know that their data is well-secured. Identification and Access Management (IAM): This is currently a key issue in cybersecurity. This is due to the many incidents of unauthorized access. That is why the adoption of strong authentication, including the implementation of Multi-Factor Authentication (MFA), is necessary. The protection of identity privileges is also given prominence in the book. Cryptography: Every cyber-aware individual or organization should ensure that cryptography is implemented to protect sensitive data from unauthorized access. This should be applied to all the data, whether in motion, at rest, or in process. Cryptography is usually effective when accompanied by other security technologies such as firewalls, intrusion detection systems, and secure Wi-Fi connections to protect the network infrastructure. Emerging Trends in Cybersecurity Cybersecurity Essentials addresses emerging trends to allow you to stay up to date with current developments ate not be caught unaware. Some of the major trends include the following: Artificial Intelligence and Machine Learning (AI/ML): AI and ML can be used for a variety of activities in cybersecurity. This ranges from detection and response to cyber threats in general, thereby improving the process of managing cyberattacks. However, you should also note that the same technologies can also be used by cybercriminals to perform even more sophisticated attacks. Hence, it is imperative to stay ahead of criminals in this discipline in terms of knowledge. Zero Trust Architecture (ZTA): The concept of ZTA has been gaining importance in recent times as one of the key methods of dealing with current cyberattacks that are more sophisticated. The concept assumes that an attack may come either from inside or outside the network, and hence, no one should be trusted. This promotes the enforcement of strict access controls.  Cloud Security: Security becomes critical as more organizations move to the cloud. There is no doubt that cloud security will present potential challenges in cybersecurity in the future, hence the emphasis. Conclusion Cybersecurity is an ever-changing landscape, and that is why changes in cybersecurity require constant alertness and vigilance.  The takeaway in cybersecurity is that it’s everybody's responsibility, hence the need to stay informed. The above-provided information allows organizations and individuals to stay updated with emerging threats and newer threats, so one can protect against such situations. To learn more about Cybersecurity and how you can secure your organization, check out my recent publication, Cybersecurity Essentials You Always Wanted to Know. Elastos Chimwanda, author of Cybersecurity Essentials You Always Wanted to Know.  This blog has been written by Elastos Chimwanda, the author of Cybersecurity Essentials You Always Wanted to Know.Read more about the book here:  If this blog has piqued your interest, here’s some additional reading material based on computer science and technology topics: What role does Data Analytics play in decision-making? Demystifying Machine Learning: A Practical Guide for Beginner        Don’t Believe These 7 Myths About Blockchain  

Today, professionals have to work with an immense volume of data, ranging from financial and operational metrics to customer reviews and social media engagements. The challenge that companies encounter is no longer the absence of information, but rather understanding it all, and preferably getting there before their competitors! This is where Business Intelligence (BI) comes in to become our master key. By implementing BI, professionals can release the power within these vast datasets and outmaneuver their competitors. BI tools enable us to turn raw data into accessible visual formats that are easily digestible and simplify complex information. By doing so they help teams to spot trends, extract meaningful insights, and make smarter decisions with speed and clarity. Why Visualizing Data Is Essential Picture yourself in front of a massive spreadsheet filled with numbers. Even if the data you are looking at is accurate and comprehensive, it is pretty challenging to extract any conclusions at a glance from such a database. Data visualization bridges this gap, turning numbers into charts, graphs, and dashboards that make trends and outliers stand out immediately. Imagine a logistics manager tracking global shipments. Instead of flipping through dozens of reports, they can view a dashboard showing delivery statuses, inventory levels, and potential delays. This instant clarity allows them to act decisively and maintain smooth operations. How BI-driven Visuals Deliver Value Here’s how BI-driven visuals turn complex data into engaging, actionable insights: Simplify complexity: Data visualizations condense vast amounts of information into concise, understandable visuals. Facilitate collaboration: Charts and dashboards communicate insights in a way that teams across departments can easily grasp, fostering better alignment. Speed-up decisions: With key metrics displayed clearly, leaders can respond to challenges and opportunities without hesitation. Increase engagement: When data is presented visually, it becomes more relatable, encouraging more people to use it in decision-making. Applications of Visualization Across Industries In every sector, data visualization is revolutionizing how teams deal with data and use it for their business's benefit. For instance, let’s look at the key applications of visualization in the following sectors: Healthcare Hospitals utilize dashboards to monitor patient data and oversee inventory levels. These visual instruments offer a live summary of essential information, assisting healthcare professionals in managing resources effectively and enhancing patient care. Simplifying intricate metrics allows dashboards to help medical teams make informed decisions that enhance operational efficiency and improve patient outcomes. Retail Visualization tools such as heatmaps and trend analyses provide companies with a distinct perspective on customer behaviors and preferences. Retailers can examine buying patterns to handle inventory better, guaranteeing that in-demand products are consistently available while reducing waste. Such insights enable companies to develop focused marketing strategies that connect with their audience, thereby improving the shopping experience and boosting revenue growth. Marketing Marketing experts depend on data visualization to optimize their campaigns instantly. Dashboards monitoring engagement metrics, like click-through rates and conversions, offer prompt insights on which strategies are effective. This allows marketers to swiftly shift or enhance their strategies, making sure that resources focus on initiatives with the highest potential effect. Utilizing BI-enhanced visuals, professionals in various industries are finding novel methods to analyze data and translate insights into action. Tips for Designing Effective Dashboards An impactful dashboard is about more than just adding visuals. Here are some principles to follow: Focus on what matters: Highlight the metrics most relevant to your goals. Choose the right chart: Select visuals that suit your data. For example, use line graphs for trends or bar charts for comparisons. Keep it clean: Avoid clutter by prioritizing simplicity. Draw attention to key insights: Use colors, annotations, or bold elements to emphasize important findings. Tools to Start Your Visualization Journey Platforms like Tableau, Power BI, and Looker make it easy for anyone—regardless of technical background—to create powerful visualizations. These tools offer templates and guides to help beginners start small while still delivering meaningful insights. Getting Started If you’re new to BI visualization, pick a dataset you’re familiar with and try creating a simple chart or dashboard. Focus on one key question you want to answer and use tools like Tableau or Power BI to design a visual that provides clarity. Feedback from colleagues can help refine your approach and ensure your visuals communicate effectively. Building a Visualization-driven Culture Incorporating visualization tools is only the beginning. For organizations to fully harness their potential, they need to invest in building confidence among their teams. Let’s see how they can do this: Providing comprehensive training: Training sessions should be a priority, ensuring employees feel equipped to navigate and use BI tools effectively. Fostering a culture of curiosity: In addition to technical abilities, creating a culture that prioritizes curiosity is also essential. When teams are motivated to investigate data and pose crucial questions, they are more prone to discover insights that foster innovation. Integrating visualization into daily operations: Incorporating data visualization into routine activities like meetings and decision-making discussions guarantees that these tools become fundamental to organizational operations, improving both collaboration and results. The Future of Visualization in BI Data visualization is advancing in tandem with technology. Interactive dashboards, augmented reality enhancements, and AI-driven insights are simplifying data engagement. For example, augmented reality might enable warehouse supervisors to see inventory amounts instantly while they navigate through a facility. AI-driven visualization is also growing. Modern BI tools can now recommend the best chart type for your data or automatically highlight critical trends. These advancements enable anyone to derive insights, bridging the gap between technical experts and decision-makers. Harnessing the Future with Data Visualization Data visualization turns complex datasets into engaging, usable insights. It's not merely about producing visuals; it's about developing a narrative that connects and inspires wiser choices. When executed effectively, data visualization enables teams to understand trends, recognize opportunities, and make decisions supported by clarity and accuracy. This renders it a crucial ability for experts steering through today's data-oriented environment. The real strength of visualization is its capacity to unite teams around a common comprehension. In meetings or strategic conversations, an effectively crafted chart or dashboard can eliminate confusion, allowing decision-makers to concentrate on what is genuinely important. As an increasing number of organizations embrace visualization tools, individuals who excel in this skill will position themselves at the leading edge of innovation and leadership. Looking forward, the prospects for BI and data visualization offer even more opportunities. As AI, augmented reality and predictive analytics evolve, the capacity to generate impactful visuals will not only stay significant but will also become a fundamental element of successful communication and achievement in various sectors. To learn how to leverage BI in-depth, read Business Intelligence Essentials You Always Wanted to Know. It covers the entire spectrum of BI, enabling you to accelerate growth in today's competitive business landscape. This book is a part of Vibrant Publishers’ Self-Learning Management Series and is suitable for entrepreneurs, leaders, and professionals. Business Intelligence Essentials You Always Wanted to Know - A guide on how to turn raw data into accessible visual formats. Find out more about the book here:  Link to the book: Business Intelligence Essentials You Always Wanted to Know Author: Irene Tobajas  Press Release: Vibrant Publishers Launches "Business Intelligence Essentials" to Empower Professionals and Leaders Also Read: Can AI take over Data Analytics?3 Unexpected Applications of Big Data AnalyticsTop 10 Hadoop Big Data Questions and Answers

Are you one of the countless people who have interacted with ChatGPT since its launch in late 2022? If so, you've likely been impressed with its capabilities and maybe even wondered, "How does it work?" Well, to understand this, you will have to know about the building blocks of such an advanced technological milestone: Machine Learning. If you're eager to delve into the inner workings of this cutting-edge technology, then look no further because this book is for you! Why should one read this book? Who stands to gain from delving into this book, you might wonder? Whether you're a student eager to explore the realm of Machine Learning or a seasoned professional planning for a career transition, this book has something special in store for you. It is a comprehensive guide to Machine Learning essentials designed for everyone, regardless of prior knowledge. Book cover of Machine Learning Essentials You Always Wanted To Know - a beginner’s guide to learning and mastering Python. About the Book The book begins with the very basics of machine learning. So, even people who have never even heard about things like Python can take advantage of it to gain the knowledge behind how the modern machine learning applications work. All you need is some basic calculus to get started on this informative journey. All you need is curiosity and willingness to explore new things with some basic calculus knowledge. Here are some key features that will be covered in the book: Starting from the Ground Up: The first step to learning anything is getting some information about what that thing is. We start things off with a beginner-friendly introduction to Machine Learning, ensuring everyone's on the same page before diving deeper. Unlocking Core Concepts: There are a lot of tools available today which make it so easy to use advanced Machine Learning models. However, if you start using those directly, you won’t understand the crux of the concept. So, the book will break down advanced concepts into bite-sized, easily digestible pieces. Hands-On Learning: Theory is one thing, but true understanding comes from getting your hands dirty. Get ready for a series of engaging exercises that will challenge you to build and create using the theoretical knowledge you learnt. But wait, there's more! Here's a roadmap of what lies ahead: We will start with the basics, laying a strong foundation of understanding before moving on to exploring Linear Models and accompanying exercises. Then, we will cover some advanced models like Support Vector Machines and Decision Trees. Dive into the world of ensemble models, exploring Bagging, Boosting, and even getting hands-on with top-performing models like LightGBM. And just when you thought that it would be the end, we'll move into the realm of Deep Learning and Neural Networks, finishing with a gentle introduction to Large Language models— which I consider one of the most innovative technical advancements of the 21st century. But remember, this is just the tip of the iceberg. This book isn't just a source of knowledge; it's a gateway to a world of possibilities. So, get ready for an adventure that will not only help you learn the theory but will also train you on how you can use Machine Learning in real-world scenarios. Dhairya Parikh, author of Machine Learning Essentials You Always Wanted To Know by Vibrant Publishers  This blog is written by Dhairya Parikh, author of Machine Learning Essentials You Always Wanted To Know. Also read: 5 Reasons Why You (Yes You!) Should Learn PythonCan AI take over Data Analytics?Introduction to Data Structures

When most people hear the term "blockchain," their minds often jump straight to Bitcoin, the groundbreaking cryptocurrency that first popularized the technology. Often, the word “blockchain” is not as widely known as Bitcoin. It’s true that blockchain as a technology revolutionized the way financial transactions are carried out, but blockchain potentially has far more use cases than just sending money. Over the past decade, blockchain has evolved into a versatile tool with applications spanning various industries, from healthcare to supply chain management. What is Blockchain? At its core, blockchain is a distributed ledger technology (DLT) that ensures secure, transparent, and tamper-proof record-keeping. Data is stored in "blocks," which are linked together in a chain, forming an immutable record of transactions. Immutability is achieved by the algorithms that are used to secure the blockchain. Blockchain’s decentralized nature means that it operates without a central authority, making it resistant to tampering, fraud, and censorship. These features of blockchain could be applied to various industries. Of course, this doesn’t mean that there are no risks involved, but as long as we make sure to take the risks into consideration and mitigate them, blockchain can work well in tandem with legacy systems. Vibrant’s newly launched book Blockchain Essentials You Always Wanted To Know is curated for beginners with no knowledge on the subject. Order the book to get started on your blockchain journey! Application of Blockchain in Various Industries Some of the industries where blockchain can be integrated are: 1. Supply Chain Management One of the most promising applications of blockchain is in supply chain management. We would all prefer to know where the product that we purchased is coming from. With the help of blockchain, if all the parties involved in making the product (from raw material sourcing to final delivery of goods) make a record, then the end customer will have a transparent record of the product ordered, thereby eliminating the possibility of fraud. For example, a consumer can verify the origin of food products, ensuring that they are sourced ethically and sustainably. This will help not only the end-customer but also the manufacturers as they can trace parts and materials, reducing the risk of counterfeit goods entering the supply chain. This process can be automated using the idea of smart contracts in blockchain technology which will help increase efficiency. 2. Healthcare The healthcare industry, burdened with issues of data security, interoperability, and patient privacy, has found promising solutions in blockchain. Healthcare data is usually kept in siloed systems that are isolated and on top of that, they are not easily accessible and need multiple authorizations for anyone to access it as patient information is confidential. This makes the process of transferring data from one provider to another provider difficult and time-consuming. Blockchain can address this challenge by offering a secure, decentralized system for storing and sharing medical records without storing any personal information on blockchain. Patients could have complete control over their health data, allowing them to decide who can access it and under what circumstances. With a blockchain-based system, medical records would be stored in an immutable, transparent ledger, significantly reducing the risk of data breaches or fraud. Combining this with the use case in the supply chain industry, a patient can be sure that the pharmaceuticals that they receive are authentic. 3. Finance and Banking (Beyond Bitcoin) Blockchain's impact on the finance and banking sectors goes far beyond its role in cryptocurrencies like Bitcoin. Financial institutions are increasingly adopting blockchain for cross-border payments, reducing the need for intermediaries and improving transaction speed and cost-efficiency. Traditionally, cross-border transactions can take several days to settle and incur hefty fees due to the involvement of multiple banks and currency exchanges. By utilizing blockchain, payments can be processed in real-time and at a fraction of the cost. Furthermore, blockchain-based decentralized finance (DeFi) platforms are emerging, allowing individuals to lend, borrow, and trade assets without the need for traditional financial institutions. Smart contracts, which are self-executing contracts with terms directly written into code, are also gaining traction in finance. These contracts automatically execute when predefined conditions are met, reducing the need for intermediaries and minimizing the potential for human error. You might have noticed by this point that most of the use cases of blockchain are possible because of the idea of smart contracts. 4. Real Estate The real estate industry, which traditionally involves lengthy processes for property transactions, is also benefiting from blockchain technology. Blockchain can streamline property sales, reducing paperwork, fraud risks, and time delays that typically occur in traditional real estate deals. It can also keep a copy of all the transactions that were made pertaining to that estate publicly available. Using blockchain, real estate transactions can be recorded in a transparent and immutable ledger, ensuring that the ownership history of a property is clear and tamper-proof. With the help of smart contracts, one can automate the transfer of ownership when certain conditions are met, eliminating the need for lawyers and notaries. Additionally, blockchain can simplify the process of fractional ownership, enabling more people to invest in real estate by purchasing shares in properties through tokenization. 5. Voting Systems With the help of smart contracts, we can make sure that the results of voting are publicly available and that there is transparency in the elections, which has always been one of the biggest challenges in traditional voting systems. Blockchain can address these issues by creating a secure, transparent, and verifiable voting system. We can record each vote with blockchain as a transaction and have the smart contract automatically declare the results once certain conditions are met. Voters could use cryptographic keys to cast their ballots, and the results would be instantly verifiable. Even better, voters could vote directly from the comfort of their homes, instead of going to polling stations and standing for hours in long queues. Blockchain could also reduce the cost and complexity of organizing elections, making them more accessible and secure. 6. Intellectual Property and Digital Rights Management This use case ties back to the real estate scenario, where blockchain can be used to track/protect intellectual property (IP) for artists, creators, and innovators. Blockchain technology can provide an effective solution by creating a decentralized and tamper-proof record of IP ownership. Works, such as music, art, or even software can be placed on blockchain to ensure that usage rights are protected and tracked. Since we are talking about licenses and agreements, managing things like royalty payments and ensuring that creators are compensated fairly can be done with the help of smart contracts. 7. Energy Sector Using blockchain in the energy sector might sound ironic given that blockchain as a technology is well known to take up huge resources of energy (especially Bitcoin) to keep it up and running. There have been many other consensus algorithms that are much more energy-efficient right now. But, on the contrary, blockchain can be used to create decentralized energy markets, allowing consumers to buy and sell energy directly without relying on traditional utility companies. Powerledger is a blockchain-enabled decentralized market based in Australia. If someone has solar panels on their roof and they generate more electricity than they need, they can sell that excess energy directly to their neighbors or others in the network, bypassing the traditional energy utilities. Moreover, blockchain can facilitate the tracking and trading of renewable energy certificates (RECs) and carbon credits, helping to incentivize sustainability efforts. Energy transactions can be made more efficient, transparent, and secure with the help of blockchain, thereby leading to a more sustainable and decentralized energy future. Conclusion While Bitcoin/Finance remain the most prominent use case for blockchain technology, the potential applications of blockchain extend far beyond the realm of cryptocurrency and most of this can be attributed to the idea of smart contracts. From supply chain management to healthcare, real estate, finance, and even voting systems, blockchain is revolutionizing industries by providing more secure, transparent, and efficient ways to conduct transactions. Blockchain is truly a technology for the future, with the ability to reshape a wide range of industries in the years to come. As a technology, blockchain is a fairly new one and one needs to do thorough investigation into how blockchain can replace the legacy systems if they plan to use it Blockchain Essentials is a lucid guide to understanding the fundamentals of blockchain. Read the newly launched book Blockchain Essentials You Always Wanted To Know to dive into the core concepts of blockchain. The book gives a fundamental understanding of processes like cryptography, mining, consensus algorithms, smart contracts and even equips you with all the knowledge needed to create your own smart contract. About the Author Dr. Abhilash Kancharla is an Assistant Teaching Professor in the Computer Science department at The University of Tampa. He also taught Computer Science courses at Oklahoma State University, where he received his Master’s and Doctorate degrees. He began his career as a software tester at Capgemini while working for clients like HSBC, Capital One, and Bank of America. He has worked for over five years with blockchain, primarily Ethereum and Hyperledger blockchains. Also read: Don’t Believe These 7 Myths About Blockchain What role does data analytics play in decision-making? Can AI take over Data Analytics?

In this rapidly evolving world that we live in, the skill to utilize data smartly and innovatively has become a game changer for businesses. Entering 2025, Business Intelligence (BI) has grown from a specialized skill to an essential capability that all professionals should seriously consider cultivating. Regardless of whether you are a CEO or a brand-new graduate, grasping BI can greatly improve your career opportunities. Turning Data Into Decisions with BI We exist in an era where raw data is produced at an unparalleled speed. From customer engagements to operational processes, every facet of a business constantly produces invaluable data. However, raw data is simply that, unfinished and unexplored. Experts are required to explore this data and its potential, and this is indeed where BI becomes relevant. In 1989, Howard Dresner, a well-known author and researcher, defined Business Intelligence as “concepts and methods to improve business decision-making by using fact-based support systems.” Today, BI tools enable companies to evaluate past and present data to make knowledgeable choices. For example, picture yourself as a marketing manager attempting to determine which campaign produced the most successful outcomes. Using BI, you can evaluate metrics such as conversion rates and customer engagement instantaneously, allowing for rapid strategy adjustments. The Increasing Need for BI Expertise The job market has certainly taken notice of the growing need for experts with BI capabilities. Over the last few years, roles like Business Intelligence Analyst, Data Scientist, and BI Developer have steadily climbed the ranks of in-demand professions. And one thing is for sure: this growth isn’t slowing down. Companies have realized now that decisions grounded in data lead to better outcomes, less guesswork, and more confidence in their strategies. As a result, they’re hungry for talent that can navigate dashboards, interpret graphs, and dig deep into the numbers. But it’s not just dedicated “data people” who need these skills. Companies are looking for professionals at all levels who can interpret basic metrics and leverage dashboards. A marketing director who can analyze campaign performance without relying on another department becomes more agile. A project manager who can identify workflow inefficiencies in a BI tool can streamline processes immediately. Thus, knowing BI fundamentals makes you a more versatile and valuable contributor. Critical Skills for BI Experts If you are thinking about entering the realm of Business Intelligence, here are several essential skills you could concentrate on: Data Analysis: Grasping how to analyze data trends and patterns SQL Expertise: Utilizing SQL for database queries as numerous BI tools rely on SQL  Data Visualization: Effectively displaying data with tools such as Tableau or Power BI to enhance the impact of insights  Statistical Understanding: A strong understanding of statistics to support the validation of your analyses  Business Insight: Grasping business processes, to synchronize your perspectives with company objectives. Job Prospects in Business Intelligence The power of BI lies in its adaptability, providing access to multiple career opportunities: Business Intelligence Analyst: Specializes in data analysis and report generation  Data Scientist: Employs sophisticated analysis methods and machine learning  BI Developer: Focuses on the technical aspects, creating BI solutions  BI Consultant: Guides businesses on optimal practices and execution methods. The great news is that contrary to what some may think, these positions are not confined only to major technology companies. Businesses across industries such as healthcare, finance, retail, and manufacturing are learning that data is their hidden advantage. They are thus keen to hire individuals who understand how to utilize data. This means you can leverage your BI expertise to work for any sector that captivates you. Building a Strong Career with BI Skills You may question why it is important to dedicate time to acquiring BI skills now. The answer is straightforward: companies are changing quickly, and those who adapt will prosper. As companies become more dependent on data for strategic choices, possessing a strong background in BI will improve your job prospects and establish you as a key contributor to any team. BI tools bring everyone onto the same page by offering a single source of truth. Instead of sifting through conflicting spreadsheets or stale reports, teams can collaborate around a shared dashboard. As remote work and global teams become more common, this level of transparency keeps everyone aligned, no matter where they sit. Learning BI doesn’t have to be overwhelming. The book Business Intelligence Essentials You Always Wanted to Know provides simplified guidance to help beginners. To start implementing BI, begin with a small project: maybe take a dataset from your current role and experiment with a visualization tool to highlight a key metric. It won't be long until you gain the confidence and intuition needed to dig deeper and offer valuable insights. Even if this does not sound very technical, while you develop your skills, it is highly recommended to keep an eye on the narrative. Specifically, pay attention to how to tell a story with the data, connect it to business goals, and present findings in a way that resonate with different audiences. With a bit of practice, you’ll be the person in the meeting who can back up suggestions with evidence, guiding conversations toward informed decisions. Building Your Personal Brand and Future-Proofing Your Career Becoming data-savvy strengthens your professional brand. When colleagues see you consistently backing up your ideas with clear data visualizations, credible metrics, and logical explanations, you gain trust. Decision-makers start relying on you for answers, and teammates appreciate having a clear picture of what’s happening and why. This trust and credibility can shape the trajectory of your career. It positions you as someone who doesn’t just talk in vague terms but delivers tangible insights. Over time, these small moments of clarity add up, making you stand out as a go-to expert. Ultimately, embracing BI is about staying relevant. In 2025, think of BI as a powerful tool that can help shape your professional future. It’s not just about following a trend; it’s about stepping confidently into a reality where data is central to success. Enhance your BI skills, and you’ll be better positioned to thrive, whatever direction your career takes! To learn how to leverage BI in-depth, read Business Intelligence Essentials You Always Wanted to Know. It covers the entire spectrum of BI, enabling you to accelerate growth in today's competitive business landscape. This book is a part of Vibrant Publishers’ Self-Learning Management Series and is suitable for entrepreneurs, leaders, and professionals. Find out more about the book here: Link to the book: Business Intelligence Essentials You Always Wanted to KnowAuthor: Irene TobajasPress Release: Master Data-Driven Decision Making with Vibrant’s Upcoming Release, “Business Intelligence Essentials” Also Read: Can AI take over Data Analytics?3 Unexpected Applications of Big Data AnalyticsTop 10 Hadoop Big Data Questions and Answers

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.  

In this blog on Graphs in Data Structures, we will learn about graph representation, operations, some graph-related algorithms, and their uses. The graph data structure is widely used in real-life applications. Its main implementation is seen in networking, whether it is on the internet or in transportation. A network of nodes is easily represented through a graph. Here, nodes can be people on a social media platform like Facebook friends or LinkedIn connections or some electric or electronic components in a circuit. Telecommunication and civil engineering networks also make use of the graph data structure.   Graphs in Data Structures: Graphs in data structures consist of a finite number of nodes connected through a set of edges. Graphs visualize a relationship between objects or entities. Nodes in graphs are entities. The relationship between them is defined through paths called edges. Nodes are also called Vertices of graphs. Edges represent a unidirectional or bi-directional relation between the vertices. An undirected edge between two entities or nodes, A and B, represents a bi-directional relation between them. The root node is the parent node of all other nodes in a graph, and it does not have a parent node itself. Every graph has a single root node. Leaf nodes have no child nodes but only contain parent nodes. There are no outgoing edges from leaf nodes.   Types of Graphs: Undirected Graph: A graph with all bi-directional edges is an undirected graph. In them undirected graph below, we can traverse from node A to node B as well as from node B to node A as the edges are bi-directional.     Directed Graph: This graph consists of all unidirectional edges. All edges are pointing in a single direction. In the directed graph below, we can traverse from node A to node B but not from node B to node A as the edges are unidirectional.       A tree is a special type of undirected graph. Any two nodes in a tree are connected only by one edge. In this way, there are no closed loops or circles in a tree., whereas looped paths are present in graphs.     The maximum number of edges in a tree with n nodes is n-1. In graphs, nodes can have more than one parent node, but in trees, each node can only have one parent node except for the root node that has no parent node.   Graph representation: Usually, graphs in data structures are represented in two ways. Adjacency Matrix and Adjacency List. First, let’s see: what is an adjacency in a graph? We say that a vertex or a node is adjacent to another vertex if they have an edge between them. Otherwise, the nodes are non-adjacent. In the graph below, vertex B is adjacent to C but non-adjacent to node D.     Adjacency Matrix: In the adjacency matrix, a graph is represented as a 2-dimensional array. The two dimensions in the form of rows and columns represent the vertices and their value. The values in the form of 1 and 0 tell whether the edge is present between the two vertices or not. For example, if the value of M[A][D] is 1, it means there is an edge between the vertices A and D of the graph. See the graph below and then its adjacency matrix representation for better understanding.     The adjacency matrix gives the benefit of an efficient edge lookup, that is, whether the two vertices have a connected edge or not. But at the same time, it requires more space to store all the possible paths between the edges. For fast results, we have to compromise on space.   Adjacency Lists: In the adjacency list, an array of lists is used to represent a graph. Each list contains all edges adjacent to a particular vertex. For example, list LA will contain all the edges adjacent to the vertex A of a graph. As the adjacency list stores information only for edges that are present in the graph, it proves to be a space-efficient representation. For cases such as sparse matrix where zero values are more, an adjacency list is a good option, whereas an adjacency matrix will take up a lot of unnecessary space. The adjacency list of the above graph would be as,     Graph Operations: Some common operations that can be performed on a graph are: Element search Graph traversal Insertion in graph Finding a path between two nodes   Graph Traversal Algorithms: Visiting the vertices of a graph in a specific order is called graph traversal. Many graph applications require graph traversal according to its topology. Two common algorithms for graph traversal are Breadth-First Search and Depth First Search.   Breadth-First Search Graph Traversal: The breadth-first search approach visits the vertices breadthwise. It covers all the vertices at one level of the graph before moving on to the next level. The breadth-first search algorithm is implemented using a queue. The BFS algorithm includes three steps: Pick a vertex and insert all its adjacent vertices in a queue. This will be level 0 of the graph. Dequeue a vertex, mark it visited, and insert all its adjacent vertices in the queue. This will be level 1. If the vertex has no adjacent nodes, delete it. Repeat the above two steps until the whole graph is covered or the queue is empty. Consider the graph below where we have to search node E.     First, we will visit node A then move on to the next level and visit nodes B and C. When nodes at this level are finished we move on to the next level and visit nodes D and E. Nodes once visited can be stored in an array, whereas adjacent nodes are stored in a queue. The time complexity of the BFS graph is 0(V+E), where V is the number of vertices and E is the number of edges.   Depth First search Graph Traversal: The depth-first search (DFS) algorithm works on the approach of backtracking. It keeps moving on to the next levels, or else it backtracks. DFS algorithms can be implemented using stacks. It includes the following steps. Pick a node and push all its adjacent vertices onto a stack. Pop a vertex, mark it visited, and push all its adjacent nodes onto a stack. If there are no adjacent vertices, delete the node. Repeat the above two steps until the required result is achieved or the stack gets empty. Consider the graph below: To reach node E we start from node A and move downwards, as deep as we can go. We will reach node C. Now, we backtrack to node B, to go downwards towards another adjacent vertex of B. In this way, we reach node E.     IsReachable is a common problem scenario for graphs in Data Structures. The problem states to find whether there is a path between two vertices or not, that is whether a vertex is reachable from the other vertex or not. If the path is present, the function returns true else false. This problem can be solved with both of the traversal approaches we have discussed above, that is BFS and DFS.     Consider a function with three input parameters. The graph and the two vertices we are interested in. Let’s call them u and v. Using a BFS algorithm, the solution consists of the following steps: Initialize an array to store visited vertices of size equal to the size of the graph. Create a queue and enqueue the first vertex, in this case, u = 1. Mark u as visited and store it in the array. Add all the adjacent vertices of u in the queue. Now, dequeue the front element from the queue. Enqueue all its adjacent vertices in the queue. If any vertex is the required vertex v, return true. Else continue visiting adjacent nodes and keep on adding them to the queue. Repeat the above process in a loop. Since there is a path from vertex 1 to vertex 3 we will get a true value. If there is no path, for example, from node 1 to node 8, our queue will run empty, and false will be returned, indicating that vertex u is not reachable to vertex v. Below is an implementation of the IsReachable function for the above graph in Python programming language.       Weighted Graphs: Weighted graphs or di-graphs have weights or costs assigned to the edges present in the graph. Such weighted graphs are used in problems such as finding the shortest path between two nodes on the graph. These graph implementations help in several real-life scenarios. For example, a weighted graph approach is used in computing the shortest route on the map or to trace out the shortest path for delivery service in the city. Using the cost on each edge we can compute the fastest path.   Weighted graphs lead us to an important application of graphs, namely finding the shortest path on the graph. Let’s see how we can do it.     Finding the Shortest Path on the Graph: The goal is to find the path between two vertices such that the sum of the costs of their edges is minimal. If the cost or weight of each edge is one, then the shortest path can be calculated using a Breadth-first Search approach. But if costs are different, then different algorithms can be used to get the shortest path. Three algorithms are commonly used for this problem. Bellman Ford’s Algorithm Dijkstra’s Algorithm Floyd-Warshall’s Algorithm This is an interesting read on shortest path algorithms.   Minimum Spanning Tree: A spanning tree is a sub-graph of an undirected graph containing all the vertices of the graph with the minimum possible number of edges. If any vertex is missing it is not a spanning tree. The total number of spanning trees that can be formed from n vertices of a graph is n(n-2). A type of spanning tree where the sum of the weights of the edges is minimum is called a Minimum Spanning Tree. Let’s elaborate on this concept through an example. Consider the following graph.     The possible spanning trees for the above graph are:       The Minimum Spanning tree for the above graph is where the sum of edges’ weights = 7.   Incidence Matrix: The Incidence matrix relates the vertices and edges of a graph. It stores vertices as rows and edges as columns. In contrast, to the adjacency matrix where both rows and columns are vertices. An incidence matrix of an undirected graph is nxm matrix A where n is the number of vertices of the graph and m is the number of edges between them. If Ai,j =1, it means the vertex vi and edge ej are incident.     Incidence List: Just like the adjacency list, there is also an incidence list representation of a graph. The incident list is implemented using an array that stores all the edges incident to a vertex. For the following graph, the list Li shows the list of all edges incident to vertex A in the above graph.     Ending Note: In this blog on Graphs in Data Structures, we discussed the implementation of graphs, their operations, and real-life applications. Graphs are widely used, especially in networking, whether it is over the internet or for mapping out physical routes. Hopefully, now you have enough knowledge regarding a data structure with extensive applications.   Get one step closer to your dream job! We have books 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 Interview Questions You’ll Most Likely Be Asked.

Introduction   In this world of constantly changing specifications and requirements, it’s not an easy task to be updated with your web services. Meticulous planning and documentation is a must to win the race. There are a few tools in the market that will help you do the job. We will discuss one such tool Swagger – an API documentation and development toolset.     Swagger Swagger is not just a platform, but a set of tools that will help you in the process of documenting and developing APIs. All these APIs follow the Open API specification standard. Their official website (www.swagger.io) describes three different toolsets:   Swagger Editor Swagger UI Swagger Codegen     Let us have a detailed look into these toolsets: Swagger Editor The swagger editor is responsible for designing and creating APIs based on the Open API specification. This editor can be installed on your computer and you can use it by logging into your swagger account. The editor has an intelligent auto-completion feature and has a visual editor feature where you can interact with the API during its creation time itself. Below is the screenshot from the swagger editor.         Swagger UI The swagger UI is another tool that will allow you to visually see and interact with the APIs. These APIs can be created by Swagger Editor or there is provision for uploading other APIs as well. Once the API is given for the tool, it will automatically generate the visual schema of your API. One of the very useful features of this tool is that it will also create documentation for your API. This is very useful while implementing the API in the back end or at the client-side. You can execute the APIs from there itself and test its functionality as well.     Swagger Codegen The Codegen tool takes care of defining the servers and client-side SDKs for the API that you are building. In this way you don’t need to worry about those aspects while developing and API. It supports over 20 programming languages and the creation of these stubs and SDKs are quite an easy task in Codgen. This will help you to focus only on developing APIs than thinking about the client and server implementations.     Opening an account in Swagger An account in Swagger is quite easy to start. Just visit the website www.swagger.io and sign up for an account by providing some basic details. You can also open an account providing your Github credentials. Once you create an account, you will be taken into the swagger hub dashboard. Below is the screenshot of the same.         From the dashboard, you can create or import your APIs. These APIs can be public or private according to your requirement. If you need other teams from swagger to see and collaborate on the API development, then they have to be public.   Once you are working on your APIs, the view will be changed to something as shown below:         Here, you have access to all the tools, the editor, UI, API docs and Codegen. This will make working with the entire process a seamless experience. Once the API is created, there will be an option to download the corresponding server stub and client SDK.     The default account is free which limits the use only to one user per team. If you need more paid options available. Summary Swagger is a set of tools that is targeted towards API development and documentation. The visual editor and codegen tools are some of the compelling features of this platform. As an API developer, it is worth trying swagger for your use cases.

Introduction   APIs are the heart of every web service. When we deal with enterprise web services, there will be requirements for building thousands of APIs and it is not a simple task for the developer to create all of them manually. To resolve this problem there are many API tools available which will handle the complete lifecycle of an API. In this article, we will discuss such a tool called POSTMAN.   POSTMAN According to the official website (www.postman.com), postman is a collaboration platform for API development. Yes, it allows multiple people to come together and work on creating APIs for their applications. Apart from this, it’s a platform that will take care of all the processes involved in an API creation and management.   Use of POSTMAN in the API world As mentioned above the platform takes care of all requirements in API development. Let us take a look at some of the features the postman offers:   Accessibility: The platform is a hosted service and all you need to start using it is an account in postman. You can either use the browser to login to postman or use their desktop app and start working.   Organized: The APIs that you create with postman can be organized well with the feature called collections. They can be placed in folders and subfolders based on your projects.   Monitoring: Another exciting feature of the postman is the power to monitor the APIs that you have created. This will make your life much easier due to the instant notification of failures in any of your API services.   Testing: Nothing is solid unless you test it thoroughly. Postman offers both manual and automated testing of your APIs. You can define test cases and discuss with your team over the platform itself and put the tests into action. Also, these tests can be integrated into your CI/CD pipeline.   Versioning: You might want to provide APIs for different customers that utilize your services. Or there can be different releases of the same API whenever your application releases new versions. In these cases versioning your API is important. The postman platform offers better tagging and versioning provisions that you and your team can utilize effectively.   Getting started with POSTMAN Inorder to use postman, you need to sign up for an account first. Below is the login/signup screen of the postman.         Once you have an account you can download the postman application and start building your APIs, Test cases, etc.   In postman you have the choice of selecting different plans based on your requirement and pricing. Initially you will be provided with a Free Plan where there are certain limitations on the number of API that you can create and manage. When your requirements are high and there is a team working with you, then it’s good to choose a Team, Business or Enterprise plan as per your convenience. Below is the screenshot of different plans that Postman offers.        Using POSTMAN After successfully creating an account in postman, you can download the client application on your desktop and start working with it.   Once you login to your postman application, the dashboard will look like the one below.         Here you can create your APIs, put them into collections and you can perform monitoring, testing, etc. on your APIs. The UI is very intuitive and can be learned quite quickly.   You can create APIs based on JSON or YAML schema formats. Also, there is support for Open API, GraphQL and RAML schemas.   There is a very detailed tutorial on how to use postman on their official website: https://learning.postman.com. You must pay a visit here if you want to learn about the platform in detail.   Summary Postman is a platform to create and manage the lifecycle of  APIs. It is versatile and has a lot of features including team collaboration and monitoring etc. Using postman for your project will not only save time but will provide a clean and neat way of managing your APIs as well.

Introduction: Most real-world applications work with collections like files, variables, records from files, or database result sets. The Java language provides a set of collection frameworks that you can use to create and manage various types of object collections. This blog describes the most common collection classes and how to start using them.     List: The list is an ordered collection, also known as a sequence. Because the list is organized, you have complete control over where list items are placed in a list. One thing to note here is that the Java list collection can only contain objects.   Declare a List:   A generic way       List listOfStrings = new ArrayList();     Using the Diamond operator       List<String> listOfStrings = new ArrayList<>();     Here the object type is not specified during ArrayList instantiation. This is because the type of the class to the right of the expression must match the type on the left. Note that the ArrayList java object is assigned to a variable of the list type. In Java programming, you can assign one type of variable to another, as long as the assigned variable is a superclass or interface implemented by the assignment variable.     List Methods:   To place a list item on a list using add() method       List<Integer> listOfIntegers = new ArrayList<>(); listOfIntegers.add(Integer.valueOf(100));     Note that the add() method adds elements to the end of the list.     To ask how big the list is, call size(). Here in the above example to get the current list size we will call,       listOfIntegers.size();     To retrieve an item from the list call get() and pass it the index of the item you want. For example if you want to get the first item from the listOfIntergers, then you will specify it like this:       listOfIntegers.get(0);     To go through all the records in a list you will iterate through the collection. You can do that easily because list implements the java.lang.Iterable.   When java.lang.Iterable is implemented by a collection it is called an Iterative variable collection. Here you will begin at one end and work on the collection, item by item until you’ve finished processing all the items.To get each item in a list, you can do the following:       for (Integer i : listOfIntegers) {   System.out.print(“Integer value is : ” + i); }     Set: Java Set is a collection construct that, by definition, contains unique elements — that is, no duplicates. The Java Set collection can only contain objects, and it is not an ordered list, which means it does not care about the order of the elements. Because the set is an interface, you cannot instantiate it directly.   Types of Java Set:   In Java, there are three implementations available for a set. They are HashSet, LinkedHashSet & TreeSet.   Declare a Set:       Set<Integer> setOfNumbers = new HashSet<>();     This Hashset is the most widely used version of a Set which gives a unique ordered list.       Set<String> setOfNames = new LinkedHashSet<>();     The only difference in LinkedHashSet with HasSet is that it orders the elements based on insertion order.       Set<Integer>setOfNumbers = new TreeSet<>();     In a TreeSet the ordering takes place based on the values of the inserted element. It follows a natural ordering by default.       Queue: Java Queue is a collection that works on FIFO (First In First Out) principle. The elements that are added first will be removed first from the queue. LinkedList and Priority Queue are the most common types of Queue implementations in Java.   Basic Queue Operations The common operations that can be performed in a queue are addition, deletion & iteration. Like other collections here also we can find out the queue size & length. The enqueue() method will add an element at the back of the queue and the dequeue() method will remove the item which is at the front of a given queue.         Map: The map is a convenient collection construct that you can use to associate one object (key) with another object (value). As you can imagine, the key of the Map must be unique and can be used later to retrieve values. Different implementations of the Map are HashMap, TreeMap, LinkedHashMap, etc. HashMap Java is the common Map type used by programmers.   Declare a Map: A Map can be declared using the Diamond Operator as given below:       Map<Integer, String> sampleMap = new HashMap<>();     Here, the Integer will be the ‘Key’ and Sting will be the ‘Value’. Basic Map Operations: The basic operations that can be performed in a Map are: Put the content in Map Get content from Map Get the key set for Map – use it to iterate.       Common Operations in Java 8 Collections: The collection framework in Java has many common operations that apply to all types of collections. They are summarized here.     End note: We have seen different types of collections in Java, their usage, and the main operations that each collection can perform. When dealing with large amounts of data, collections are the most commonly used data structure in the Java programming world. So it is important to understand their usage thoroughly to become a good Java developer. A Java developer will also need to understand Wrapper class, Streams, Enumeration, Autoboxing, and Threads.     Get one step closer to your dream job!     Prepare for your Java programming interview with Core Java Questions You’ll Most Likely Be Asked. This book has a whopping 290 technical interview questions on Java Collections: List, queues, stacks, and Declarations and access control in Java, Exceptional Handling, Wrapper class, Java Operators, Java 8 and Java 9, etc. and 77 behavioral interview questions crafted by industry experts and interviewers from various interview panels. We’ve updated our blogs to reflect the latest changes in Java technologies. This blog was previously uploaded on April 3rd, 2020, and has been updated on January 8th, 2022.

Introduction This blog talks about Java streams. The stream is a new addition in Java 8 that gives much power to Java while processing data. One thing to note here is that the stream mentioned here has nothing to do with Java I/O streams. This Java tutorial will focus on the Java 8 Streams in detail.Streams make processing a sequence of elements of the same datatype a cakewalk. It can take input in the form of any collections in Java, Arrays, Java I/O streams, etc. The interesting fact here is that you don’t need to iterate through these inputs to process each element. Automatic iterations are a built-in feature of the stream.In this blog, we will learn to create a stream and look at how stream operations in Java function. We will also take a look at improvements made by Java 9 on the stream interface.     Creating a Stream: Let’s see how a stream can be declared in Java 8. First, you will import java.util.stream class to use the Java 8 Stream feature. Now assuming we have an array ‘arrayOfCars’ as the input source to the stream, private static Car[] arrayOfCars = {   new Car(1, “Mercedes Benz”, 6000000.0),   new Car(2, “Porsche”, 6500000.0),   new Car(3, “Jaguar”, 7000000.0) };     The stream can be created as given below:       Stream.of(arrayOfCars);     If we are having a List, a stream can be created by calling the stream() method of that list itself.       sampleList.stream();     We can also create Stream from different objects of a collection by calling the Stream.of() method.       Stream.of (arrayOfCars[0], arrayOfCars[1], arrayOfCars[2]);     Or just by using the Stream.builder() method.       Stream.Builder carStreamBuilder = Stream.builder(); carStreamBuilder.accept( arrayOfCars[0] ); carStreamBuilder.accept( arrayOfCars[1] ); carStreamBuilder.accept( arrayOfCars[2] ); Stream carStream = carStreamBuilder.build();     Stream Operations: We have seen the different ways of creating a stream. Now let us have a look into various Stream operations.   The main use of stream is to perform a series of computations operations on its elements until it reaches the terminal operation.   Let’s look at an example:       List flowers = Arrays.asList(“Rose”, “Lilly”, “Aster”, “Buttercup”, “Clover”); flowers.stream().sorted().map(String::toUpperCase).forEach(System.out::println);     Here the stream performs sorting and mapping operations before iterating and printing each element in the stream. So we can understand that the stream operations are either intermediate or terminal. In intermediate operations, the output will be stream format itself.       Intermediate stream operations:         Terminal stream operations:         Improvement made by Java 9 on stream interfaces Java 9 has added a stream.ofNullable() method to the stream interface. It helps to create a stream with a value that may be null. So, if a non-null value is used in this method, it creates a stream with that method; otherwise, it creates an empty stream. Both the methods takewhile() and drpwhile() can be operated in Java stream. They are used to obtain a subset of the input stream. Java 9 has added an overloaded version of the Stream.iterate() method that accepts a predicate and terminates the stream when the condition specified is true.     End note: In this blog, we learned about the improvements made by Java 9 on stream interfaces. We also studied Java 8 stream features in detail—how to create a stream, stream operations, the uses of a stream, among other topics. Next, we learned about different stream intermediate operations and stream terminal operations and briefly looked at how Java stream provides a functional style of programming to Java besides its Object-oriented programming style. It is important to learn and understand this newly-added feature of a stream while studying Java programming.     Get one step closer to your dream job!   Prepare for your Java programming interview with Core Java Questions You’ll Most Likely Be Asked. This book has a whopping 290 technical interview questions on Java Collections: List, queues, stacks, and Declarations and access control in Java, Exceptional Handling, Wrapper class, Java Operators, Java 8 and Java 9 etc. and 77 behavioral interview questions crafted by industry experts and interviewers from various interview panels. We’ve updated our blogs to reflect the latest changes in Java technologies. This blog was previously uploaded on April 2nd, 2020, and has been updated on January 7th, 2022.

Introduction: One of the most basic requirements of a programming language is to perform mathematical operations. Java is an Object-Oriented Programming language. It provides a rich set of operators to manipulate variables. In this blog, let’s take a look at the different types of Java operators and a few examples of sample code.   We can classify the Java operators into the following groups: Arithmetic Operators Relational Operators Bit Operators Logical Operators Assignment Operator       Arithmetic Operators: Arithmetic operators are used for mathematical expressions. In the following table, you will find a list of all arithmetic operators, as well as their descriptions and example usage.   The examples in the table given below assume that the value of the integer variable X is 20 and the value of variable Y is 40.         One thing to note here is the number of operands involved in each case. While addition, subtraction, multiplication & Java modulo operators require two operands, the increment & decrement operators operate on a single operand.   The sample code given below will show all the use-cases of the above-mentioned operators in detail.     public class TestArithmaticOperators {   public static void main(String [] args){     //operand variable declaration and initializatoin.     int a = 5; int b = 22; int c = 16; int d = 33;     System.out.printIn(“a + b =” + (a + b));     System.out.printIn(“a – b =” + (a – b));     System.out.printIn(“a * b =” + (a * b));     System.out.printIn(“b / a =” + (b / a));     System.out.printIn(“b % a =” + (b % a));     System.out.printIn(“c % a =” + (c % a));     System.out.printIn(“a++ =” + (a++));     System.out.printIn(“a– =” + (a–));       // Check the difference between d ++ and ++ d     System.out.printIn(“d++ =” + (d++));     System.out.printIn(“++d =” + (++d));   } }   The following output is thus produced:     $ javac TestArithmeticOperators.java $java -Xmx128M -Xms16M TestArithmaticOpertors a + b = 27 a – b = -17 a * b = 110 b / a = 4 b % a = 2 c % a = 1 a++ = 5 a– = 6 d++ = 33 ++d = 35     Relational Operators: When we want to relate or compare two values, we use relational operators in our Java code. Two operands are expected for the operator as inputs.The table given below will provide a detailed description of relational operators in Java. Assuming the values of variables are X = 20  & Y = 40.         Let’s look at a sample code that uses the above operators:     public class code {   public static void main (String [] args){       // operand varibale declaration and initialization.     int a = 5; int b = 22; int c = 16; int d = 33;       System.out.println( “a == b =” + (a == b));     System.out.println( “a != b =” + (a != b));     System.out.println( “a < b =” + (a < b));     System.out.println( “a > a =” + (a > a));     System.out.println( “b <= a =” + (b <= a));     System.out.println( “c >= a =” + (c >= a));   } }     After successful compilation, the above code will produce the following result:     Output:   a == b = false a != b = true a < b = true a > a = false b <= a = false c >= a = true     Bit Operator: Java defines bit operators for integer types (int), long integers (short), short integers (char), and byte types (byte).   Bit operators operate on all bits and perform bit-level operations. There are four bitwise operations and three bitshift operations that bit operators perform.   For example, if we take Bitwise AND operator which is a bitwise operator, the operation will be as follows:   Suppose X = 60 and Y = 13; their binary (bit) representation would be as follows:       X = 0011 1100 Y = 0000 1101     Now when we perform a Bitwise AND operation, ie. if both bits are 1, the result is 1 else it’s 0. So here the result will be:       X & Y = 0000 1100     The following table gives a summary of the available bitwise operators and bit shift operators in Java:       Logical Operators: The following table lists the basic operations of logical operators, assuming Boolean variable X is true and variable Y is false.         Assignment Operator: We have discussed this section in detail here: Java Assignment Operator     Other Operators:   Instanceof Operator: This operator is used to manipulate an object instance and check whether the object is a class type or interface type. The instanceof Java operator uses the following format:       (Object reference-variable) instanceof (class / interface type)     If the object pointed to by the variable on the left side of the operator is an object of the class or interface on the right side of the operator, the result is true. Below is an example:         Conditional Operator (?:): The conditional operator in Java is also called ternary operator. The ternary Java operator has 3 operands. The conditional operator is used to determine the boolean values. The main purpose of this operator is to finalize which value among the two should be assigned to a variable.   The syntax would be:       variable test_variable = (expression)? value if true : value if false;       Java operator precedence: When multiple operators are used in one Java statement, it is important to understand which one will act first. To solve this confusion, the Java language has something known as ‘operator precedence.’ This will allow the highest priority operators to act first and then the other operators act, following the precedence order. It’s important to understand that in a multi-operator expression, different operator precedence can lead to very different results.     End Note: We have learned about the different types of operators in Java language that are used for performing various mathematical operations. We also saw examples of  their syntax.   Besides Java Operators, you must also be well-versed with the concepts of Flow control statements and Assertion, Java collections and Stream, Threading in Java, etc. You can take a look at our blogs on these topics for in-depth understanding.     Get one step closer to your dream job!   Prepare for your Java programming interview with Core Java Questions You’ll Most Likely Be Asked. This book has a whopping 290 technical interview questions on Java Collections: List, Queues, Stacks, and Declarations and access control in Java, Exceptional Handling, Wrapper class, Java Operators, Java 8 and Java 9, etc. and 77 behavioral interview questions crafted by industry experts and interviewers from various interview panels. We’ve updated our blogs to reflect the latest changes in Java technologies. This blog was previously uploaded on March 31th, 2020, and has been updated on January 12th, 2022.

Introduction This Java tutorial deals with Java Inner Classes & String handling. Java Inner Classes are an extraordinary feature in Java. In simple terms, an inner class is a class declared inside a class. In a broad sense, inner classes generally include these four types:   Member inner class Local inner class Anonymous inner class Static inner class     Creation of an Inner class: You will create an inner class just like how you do it for a general class. The only difference is that the inner class is always declared inside a class. An inner class can have private access, protected access, public access, and package access.   The code given below will explain the concept in detail:     Here we declared the inner class as ‘private’ which makes it accessible only inside the outer class. The java main class will access the inner class via an instance of its outer class.     Types of Inner class 1. Member inner class:   The member inner class is the most common inner class, and its definition will be inside another class, as given below:     class Circle {   double radius = 0;   public Circle(double radius) {     this.radius = radius;   }   class Draw {     public void drawSahpe() {       System.out.println(“draw shape”);     }   } }     In this way, the class Draw looks like a member of the class Circle, which is called the outer class. Member inner class has unconditional access to all member properties and member methods of outer classes, including private members and static members.   However, we must note that when a member’s inner class owns a member variable or method with the same name as an outer class, a hidden phenomenon occurs, i.e. a member of the member’s inner class is accessed by default. If you want to access the same member of an outer class, you need to access it in the following form:       OuterClass.this.MemberVariable; OuterClass.this.MemberMethod;     2. Local inner class:   A local inner class is a class that is defined in a method or scope, and the difference between it and the member inner class is that access to the local inner class is limited to the method or within the scope.       class Animals{   public Animals() {   } } class Mammals{   public Mammals(){ }     public Animals getHerbivorous(){       class Herbivorous extends Animals{         int number =0;       }     return new Herbivorous();   } }     The local inner classes, like a local variable within a method, cannot have public, protected, private, and static modifiers.     3. Anonymous inner class:   Anonymous inner classes as it sounds don’t have a name. Using anonymous inner classes when writing code for purposes like event monitoring etc. is not only convenient but also makes code easier to maintain.   For a normal class, any number of constructors are possible. But in the case of anonymous inner classes, we can’t have any constructors just because they don’t have any name which is a must for defining a constructor. Due to this most of the anonymous inner classes are used for interface callbacks.   Let’ see an example:     interfaceWeight {   int x = 81;   void getWeight(); } class AnonymousInnerClassDemo {   public static void main(String[] args) {     // WeightClass is implementation class of Weight interface     WeightClass obj=new WeightClass();     // calling getWeight() method implemented at WeightClass     obj.getWeight();   } } // WeightClass implement the methods of Weight Interface class WeightClass implements Weight {   @Override   public void getWeight()   {     // printing the wight     System.out.print(“Weight is “+x);   } }     Here we had to create a separate class ‘WeightClass’ to override methods of an interface. But if we use the anonymous inner class feature we can easily achieve the same result by including the following code block inside the class ‘AnonymousInnerClassDemo’.       Weight obj = new Weight() {   @Override   public void getWeight() {     System.out.println(“Weight is “+x);   } };     Here an object of ‘Weight’ is not created but an object of ‘WeightClass’ is created and copied in the entire class code as shown above.     4. Static inner class:   Static inner classes are also classes defined within another class, except that there is one more keyword ‘static’ in front of the class. Static inner class does not need to rely on external classes, which is somewhat similar to the static member properties of classes, and understandably, they cannot use non-static member variables or methods of external classes. Because, without objects of external classes, you can create objects for static inner classes. Allowing access to non-static members of an external class creates a contradiction because non-static members of the outer class must be attached to specific objects.       public class Test {   public static void main(String[] args) {     Outter.Inner inner = new Outer.Inner();   } }   class Outer {   public Outer() {   }   static class Inner {     public Inner() {     }   } }     String Handling in Java: All the string handling operations in Java are powered by the Java String class. The String class is also known as an immutable character sequence. It is located in the java.lang package, and the Java program imports all classes under java.lang package by default.   Java strings are Unicode character sequences, such as the string “Java” is composed of four Unicode characters ‘J’, ‘a’, ‘v’, ‘a’. We can use the scanner class in Java to parse an input string.   There are multiple operations on a string that we can do with the Java String class. The following code will provide an idea of string handling operations that we can do in Java:       public class StringHandlingDemo {   public static void main(String[] args) {       String stringX = “core Java”;     String stringY = “Core Java”;       //Extract the character with subscript 3     System.out.println( stringX.charAt(3));       //Find the length of a string     System.out.println( stringY.length());       //Compares two strings for equality     System.out.println( stringX.equals(stringY));       //Compare two strings (ignore case)     System.out.println( stringX.equalsIgnoreCase(stringY));       //Whether the string stringX contains word Java     System.out.println( stringX.indexOf(“Java”));       //Whether the string stringX contains word apple     System.out.println( stringX.indexOf(“apple”));       //Replace spaces in stringX with &     String result = stringX.replace(‘ ‘, ‘&’);     System.out.println(“the result is:” + result);       //Whether the string start with ‘core’     System.out.println( stringX.startsWith(“core”));       //Whether the string end with ‘Java’     System.out.println( stringX.endsWith(“Java”));       //Extract substring: from the beginning of the subscript 4 to the end of the string     result = stringX.substring(4);     System.out.println(result);       //Extract substring: subscript [4, 7) does not include 7     result = stringX.substring(4, 7);     System.out.println(result);       //String to Lowercase     result = stringX.toLowerCase();     System.out.println(result);       //String to uppercase     result = stringX.toUpperCase();     System.out.println(result);     String stringZ = ” How old are you!! “;       //Strip spaces from the beginning and end of a string.     Note: the space in the middle cannot be removed     result = stringZ.trim();     System.out.println(result);       //Because String is an immutable string, stringZ is unchanged     System.out.println(stringZ);       String stringZ1 = “Olympics”;     String stringZ2 = “Olympics”;     String stringZ3 = new String(“Winner”);       //String comparison operations     System.out.println( stringZ1 == stringZ2);     System.out.println( stringZ1 == stringZ3);     System.out.println( stringZ1.equals(stringZ3));       //String concatenation operation     System.out.println(stringZ1+” “+stringZ3);     } }     The output of the above code will be:       Output: e 9 false true 5 -1 the result is:core&Java true true Java Ja core java CORE JAVA How old are you!! How old are you!! true false false Olympics Winner     End note: We learned about inner classes and string handling in Java in this blog. In Java, inner classes are very useful while implementing features such as event listening, code abstraction, etc. It also makes the Java code more concise and reusable. String handling operations like java string split etc. are a must for any Java programmer and are the most commonly used feature in Java language.     Get one step closer to your dream job! Prepare for your Java programming interview with Core Java Questions You’ll Most Likely Be Asked. This book has a whopping 290 technical interview questions on Java Collections: List, queues, stacks, and Declarations and access control in Java, Exceptional Handling, Wrapper class, Java Operators, Java 8 and Java 9, etc. 77 behavioral interview questions crafted by industry experts and interviewers from various interview panels.     We’ve updated our blogs to reflect the latest changes in Java technologies. This blog was previously uploaded on April 1st, 2020, and has been updated on January 6th, 2022.