Blockchain Essentials You Always Wanted To Know

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.