Publisher: Packt Publishing
Daniel Afonso, Ricardo Mestre
State management, a crucial aspect of the React ecosystem, has gained significant attention in recent times. While React offers various libraries and tools to handle state, each with different approaches and perspectives, one thing is clear: state management solutions for handling client state are not optimized for dealing with server state. React Query was created to address this issue of managing your server state, and this guide will equip you with the knowledge and skills needed to effectively use React Query for state management.Starting with a brief history of state management in the React ecosystem, you’ll find out what prompted the split from a global state to client and server state and thus understand the need for React Query. As you progress through the chapters, you'll see how React Query enables you to perform server state tasks such as fetching, caching, updating, and synchronizing your data with the server. But that’s not all; once you’ve mastered React Query, you’ll be able to apply this knowledge to handle server state with server-side rendering frameworks as well. You’ll also work with patterns to test your code by leveraging the testing library and Mock Service Worker.By the end of this book, you'll have gained a new perspective of state and be able to leverage React Query to overcome the obstacles associated with server state.
Prabhanjan Narayanachar Tattar
Statistical Analysis involves collecting and examining data to describe the nature of data that needs to be analyzed. It helps you explore the relation of data and build models to make better decisions.This book explores statistical concepts along with R and Python, which are well integrated from the word go. Almost every concept has an R code going with it which exemplifies the strength of R and applications. The R code and programs have been further strengthened with equivalent Python programs. Thus, you will first understand the data characteristics, descriptive statistics and the exploratory attitude, which will give you firm footing of data analysis. Statistical inference will complete the technical footing of statistical methods. Regression, linear, logistic modeling, and CART, builds the essential toolkit. This will help you complete complex problems in the real world.You will begin with a brief understanding of the nature of data and end with modern and advanced statistical models like CART. Every step is taken with DATA and R code, and further enhanced by Python.The data analysis journey begins with exploratory analysis, which is more than simple, descriptive, data summaries. You will then apply linear regression modeling, and end with logistic regression, CART, and spatial statistics.By the end of this book you will be able to apply your statistical learning in major domains at work or in your projects.
James D. Miller
Data science is an ever-evolving field, which is growing in popularity at an exponential rate. Data science includes techniques and theories extracted from the fields of statistics; computer science, and, most importantly, machine learning, databases, data visualization, and so on.This book takes you through an entire journey of statistics, from knowing very little to becoming comfortable in using various statistical methods for data science tasks. It starts off with simple statistics and then move on to statistical methods that are used in data science algorithms. The R programs for statistical computation are clearly explained along with logic. You will come across various mathematical concepts, such as variance, standard deviation, probability, matrix calculations, and more. You will learn only what is required to implement statistics in data science tasks such as data cleaning, mining, and analysis. You will learn the statistical techniques required to perform tasks such as linear regression, regularization, model assessment, boosting, SVMs, and working with neural networks.By the end of the book, you will be comfortable with performing various statistical computations for data science programmatically.
Pratap Dangeti
Complex statistics in machine learning worry a lot of developers. Knowing statistics helps you build strong machine learning models that are optimized for a given problem statement.This book will teach you all it takes to perform the complex statistical computations that are required for machine learning. You will gain information on the statistics behind supervised learning, unsupervised learning, reinforcement learning, and more. You will see real-world examples that discuss the statistical side of machine learning and familiarize yourself with it. You will come across programs for performing tasks such as modeling, parameter fitting, regression, classification, density collection, working with vectors, matrices, and more.By the end of the book, you will have mastered the statistics required for machine learning and will be able to apply your new skills to any sort of industry problem.
P. Taylor Goetz, Peter T Goetz, Brian...
Harley Ferguson
Strategic Leadership in Responsive Web Design is an essential guide that will equip you with the knowledge and foresight needed to navigate the ever-evolving digital landscape.You’ll start by learning about the strategic importance of responsive web design (RWD) before delving into its intricacies. As you progress, you’ll not only discover the technical aspects of adaptive design, but also learn how to seamlessly integrate these principles within your organization’s business objectives. You’ll develop the skills required to integrate RWD principles into broader business goals. Next, you’ll learn how to navigate SEO in modern times while ensuring platforms are consistent in design across devices. Later, this book shows you how to measure the ROI when making RWD decisions, and how to ensure you’re making the right one.By the end of this book, you’ll have understood the role that RWD plays in achieving broad business objectives.
Garima Bajpai, Thomas Schuetz
Many organizations are embracing cloud technology to remain competitive, but implementing and adopting development processes while modernizing a cloud-based ecosystem can be challenging.Strategizing Continuous Delivery in Cloud helps you modernize continuous delivery and achieve infrastructure-application convergence in the cloud. You’ll learn the differences between cloud-based and traditional delivery approaches and develop a tailored strategy. You’ll discover how to secure your cloud delivery environment, ensure software security, run different test types, and test in the pre-production and production stages.You’ll also get to grips with the prerequisites for onboarding cloud-based continuous delivery for organizational and technical aspects. Then, you’ll explore key aspects of readiness to overcome core challenges in your cloud journey, including GitOps, progressive delivery controllers, feature flagging, differences between cloud-based and traditional tools, and implementing cloud chaos engineering.By the end of this book, you’ll be well-equipped to select the right cloud environment and technologies for CD and be able to explore techniques for implementing CD in the cloud.