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Data structures (Computer science)

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Explore Golang's data structures and algorithms to design, implement, and analyze code in the professional setting Key Features Learn the basics of data structures and algorithms and implement them efficiently Use data structures such as arrays, stacks, trees, lists and graphs in real-world scenarios Compare the complexity of different algorithms and data structures for improved code performance Book Description Golang is one of the fastest growing programming languages in the software industry. Its speed, simplicity, and reliability make it the perfect choice for building robust applications. This brings the need to have a solid foundation in data structures and algorithms with Go so as to build scalable applications. Complete with hands-on tutorials, this book will guide you in using the best data structures and algorithms for problem solving. The book begins with an introduction to Go data structures and algorithms. You'll learn how to store data using linked lists, arrays, stacks, and queues. Moving ahead, you'll discover how to implement sorting and searching algorithms, followed by binary search trees. This book will also help you improve the performance of your applications by stringing data types and implementing hash structures in algorithm design. Finally, you'll be able to apply traditional data structures to solve real-world problems. By the end of the book, you'll have become adept at implementing classic data structures and algorithms in Go, propelling you to become a confident Go programmer. What you will learn Improve application performance using the most suitable data structure and algorithm Explore the wide range of classic algorithms such as recursion and hashing algorithms Work with algorithms such as garbage collection for efficient memory management Analyze the cost and benefit trade-off to identify algorithms and data structures for problem solving Explore techniques for writing pseudocode algorithm and ace whiteboard coding in interviews Discover the pitfalls in selecting data structures and algorithms by predicting their speed and efficiency Who this book is for This book is for developers who want to understand how to select the best data structures and algorithms that will help solve coding problems. Basic Go programming experience will be an added advantage.

Data structures (Computer science)

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A comprehensive guide to becoming a skilled Azure DevOps engineer Key Features Explore a step-by-step approach to designing and creating a successful DevOps environment Understand how to implement continuous integration and continuous deployment pipelines on Azure Integrate and implement security, compliance, containers, and databases in your DevOps strategies Book Description Implementing Azure DevOps Solutions helps DevOps engineers and administrators to leverage Azure DevOps Services to master practices such as continuous integration and continuous delivery (CI/CD), containerization, and zero downtime deployments. This book starts with the basics of continuous integration, continuous delivery, and automated deployments. You will then learn how to apply configuration management and Infrastructure as Code (IaC) along with managing databases in DevOps scenarios. Next, you will delve into fitting security and compliance with DevOps. As you advance, you will explore how to instrument applications, and gather metrics to understand application usage and user behavior. The latter part of this book will help you implement a container build strategy and manage Azure Kubernetes Services. Lastly, you will understand how to create your own Azure DevOps organization, along with covering quick tips and tricks to confidently apply effective DevOps practices. By the end of this book, you'll have gained the knowledge you need to ensure seamless application deployments and business continuity. What you will learn Get acquainted with Azure DevOps Services and DevOps practices Implement CI/CD processes Build and deploy a CI/CD pipeline with automated testing on Azure Integrate security and compliance in pipelines Understand and implement Azure Container Services Become well versed in closing the loop from production back to development Who this book is for This DevOps book is for software developers and operations specialists interested in implementing DevOps practices for the Azure cloud. Application developers and IT professionals with some experience in software development and development practices will also find this book useful. Some familiarity with Azure DevOps basics is an added advantage.

Data structures (Compter science)

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Take the strategic and systematic approach to analyze data to solve business problems Key Features Gain detailed information about the theory of data science Augment your coding knowledge with practical data science techniques for efficient data analysis Learn practical ways to strategically and systematically use data Book Description Principles of Strategic Data Science is created to help you join the dots between mathematics, programming, and business analysis. With a unique approach that bridges the gap between mathematics and computer science, this book takes you through the entire data science pipeline. The book begins by explaining what data science is and how organizations can use it to revolutionize the way they use their data. It then discusses the criteria for the soundness of data products and how to best visualize information. As you progress, you'll discover the strategic aspects of data science by learning the five-phase framework that enables you to enhance the value you extract from data. The final chapter of the book discusses the role of a data science manager in helping an organization take the data-driven approach. By the end of this book, you'll have a good understanding of data science and how it can enable you to extract value from your data. What you will learn Get familiar with the five most important steps of data science Use the Conway diagram to visualize the technical skills of the data science team Understand the limitations of data science from a mathematical and ethical perspective Get a quick overview of machine learning Gain insight into the purpose of using data science in your work Understand the role of data science managers and their expectations Who this book is for This book is ideal for data scientists and data analysts who are looking for a practical guide to strategically and systematically use data. This book is also useful for those who want to understand in detail what is data science and how can an organization take the data-driven approach. Prior programming knowledge of Python and R is assumed.

Data structures (Compter science)

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Understand data science concepts and methodologies to manage and deliver top-notch solutions for your organization Key Features Learn the basics of data science and explore its possibilities and limitations Manage data science projects and assemble teams effectively even in the most challenging situations Understand management principles and approaches for data science projects to streamline the innovation process Book Description Data science and machine learning can transform any organization and unlock new opportunities. However, employing the right management strategies is crucial to guide the solution from prototype to production. Traditional approaches often fail as they don't entirely meet the conditions and requirements necessary for current data science projects. In this book, you'll explore the right approach to data science project management, along with useful tips and best practices to guide you along the way. After understanding the practical applications of data science and artificial intelligence, you'll see how to incorporate them into your solutions. Next, you will go through the data science project life cycle, explore the common pitfalls encountered at each step, and learn how to avoid them. Any data science project requires a skilled team, and this book will offer the right advice for hiring and growing a data science team for your organization. Later, you'll be shown how to efficiently manage and improve your data science projects through the use of DevOps and ModelOps. By the end of this book, you will be well versed with various data science solutions and have gained practical insights into tackling the different challenges that you'll encounter on a daily basis. What you will learn Understand the underlying problems of building a strong data science pipeline Explore the different tools for building and deploying data science solutions Hire, grow, and sustain a data science team Manage data science projects through all stages, from prototype to production Learn how to use ModelOps to improve your data science pipelines Get up to speed with the model testing techniques used in both development and production stages Who this book is for This book is for data scientists, analysts, and program managers who want to use data science for business productivity by incorporating data science workflows efficiently. Some understanding of basic data science concepts will be useful to get the most out of this book.

Data structures (Compter science)