Master the art and science of analytics engineering with 'Fundamentals of Analytics Engineering.' This book takes you on a comprehensive journey from understanding foundational concepts to implementing end-to-end analytics solutions. You'll gain not just theoretical knowledge but practical expertise in building scalable, robust data platforms to meet organizational needs. What this Book will help me do Design and implement effective data pipelines leveraging modern tools like Airbyte, BigQuery, and dbt. Adopt best practices for data modeling and schema design to enhance system performance and develop clearer data structures. Learn advanced techniques for ensuring data quality, governance, and observability in your data solutions. Master collaborative coding practices, including version control with Git and strategies for maintaining well-documented codebases. Automate and manage data workflows efficiently using CI/CD pipelines and workflow orchestrators. Author(s) Dumky De Wilde, alongside six co-authors-experienced professionals from various facets of the analytics field-delivers a cohesive exploration of analytics engineering. The authors blend their expertise in software development, data analysis, and engineering to offer actionable advice and insights. Their approachable ethos makes complex concepts understandable, promoting educational learning. Who is it for? This book is a perfect fit for data analysts and engineers curious about transitioning into analytics engineering. Aspiring professionals as well as seasoned analytics engineers looking to deepen their understanding of modern practices will find guidance. It's tailored for individuals aiming to boost their career trajectory in data engineering roles, addressing fundamental to advanced topics.

COMPUTATIONAL INTELLIGENCE and HEALTHCARE INFORMATICS The book provides the state-of-the-art innovation, research, design, and implements methodological and algorithmic solutions to data processing problems, designing and analysing evolving trends in health informatics, intelligent disease prediction, and computer-aided diagnosis. Computational intelligence (CI) refers to the ability of computers to accomplish tasks that are normally completed by intelligent beings such as humans and animals. With the rapid advance of technology, artificial intelligence (AI) techniques are being effectively used in the fields of health to improve the efficiency of treatments, avoid the risk of false diagnoses, make therapeutic decisions, and predict the outcome in many clinical scenarios. Modern health treatments are faced with the challenge of acquiring, analyzing and applying the large amount of knowledge necessary to solve complex problems. Computational intelligence in healthcare mainly uses computer techniques to perform clinical diagnoses and suggest treatments. In the present scenario of computing, CI tools present adaptive mechanisms that permit the understanding of data in difficult and changing environments. The desired results of CI technologies profit medical fields by assembling patients with the same types of diseases or fitness problems so that healthcare facilities can provide effectual treatments. This book starts with the fundamentals of computer intelligence and the techniques and procedures associated with it. Contained in this book are state-of-the-art methods of computational intelligence and other allied techniques used in the healthcare system, as well as advances in different CI methods that will confront the problem of effective data analysis and storage faced by healthcare institutions. The objective of this book is to provide researchers with a platform encompassing state-of-the-art innovations; research and design; implementation of methodological and algorithmic solutions to data processing problems; and the design and analysis of evolving trends in health informatics, intelligent disease prediction and computer-aided diagnosis. Audience The book is of interest to artificial intelligence and biomedical scientists, researchers, engineers and students in various settings such as pharmaceutical & biotechnology companies, virtual assistants developing companies, medical imaging & diagnostics centers, wearable device designers, healthcare assistance robot manufacturers, precision medicine testers, hospital management, and researchers working in healthcare system.

Go beyond the basics of D3.js to create maintainable, modular, and testable charts and to package them into a library that can be distributed as open source software or kept for private use. This book will show you how to transform regular D3.js chart code into reusable and extendable modules.You know the basics of working with D3.js, but it's time to become a professional D3.js practitioner. This book is your launching pad to refactoring code, composing complex visualizations from small components, working as a team with other developers, and integrating charts with a Continuous Integration system. You'll begin by creating a production-ready chart using D3.js v5, ES2015, and a test-driven approach and then move on to using and extending Britecharts, the reusable charting library based on Reusable API patterns. Finally, you'll see how to use D3.js along with React to document and build your charts to compose a charting library you can release into the NPM repository. With Pro D3.js, you'll become an accomplished D3.js developer in no time. What You Will Learn Create v5 D3.js charts with ES2016 and unit tests Develop modular, testable and extensible code with the Reusable API pattern Work with and extend Britecharts, a reusable charting library created at Eventbrite Use Webpack and npm to create and publish a charting library from your own chart collections Write reference documentation and build a documentation homepage for your library. Who This Book Is For Data scientists, data visualization engineers, and frontend developers with a fundamental knowledge of D3.js and some experience with JavaScript, as well as data journalists and consultants.

Learn Python by Building Data Science Applications takes a hands-on approach to teaching Python programming by guiding you through building engaging real-world data science projects. This book introduces Python's rich ecosystem and equips you with the skills to analyze data, train models, and deploy them as efficient applications. What this Book will help me do Get proficient in Python programming by learning core topics like data structures, loops, and functions. Explore data science libraries such as NumPy, Pandas, and scikit-learn to analyze and process data. Learn to create visualizations with Matplotlib and Altair, simplifying data communication. Build and deploy machine learning models using Python and share them as web services. Understand development practices such as testing, packaging, and continuous integration for professional workflows. Author(s) None Kats and None Katz are seasoned Python developers with years of experience in teaching programming and deploying data science applications. Their expertise spans providing learners with practical knowledge and versatile skills. They combine clear explanations with engaging projects to ensure a rewarding learning experience. Who is it for? This book is ideal for individuals new to programming or data science who want to learn Python through practical projects. Researchers, analysts, and ambitious students with minimal coding background but a keen interest in data analysis and application development will find this book beneficial. It's a perfect choice for anyone eager to explore and leverage Python for real-world solutions.

Thanks to approaches such as continuous integration and continuous delivery, companies that once introduced new products every six months are now shipping software several times a day. Reaching the market quickly is vital today, but rapid updates are impractical unless they provide genuine customer value. With this ebook, you’ll learn how online controlled experiments can help you gain customer feedback quickly so you can maintain a speedy release cycle. Using examples from Google, LinkedIn, and other organizations, Adil Aijaz, Trevor Stuart, and Henry Jewkes from Split Software explain basic concepts and show you how to build a scalable experimentation platform for conducting full-stack, comprehensive, and continuous tests. You’ll learn practical tips on best practices and common pitfalls you’re likely to face along the way. This ebook is ideal for engineers, data scientists, and product managers. Build an experimentation platform that includes a robust targeting engine, a telemetry system, a statistics engine, and a management console Dive deep into types of metrics, as well as metric frameworks, including Google’s HEART framework and LinkedIn’s 3-tiered framework Learn best practices for an building experimentation platform, such as A/A testing, power measuring, and an optimal ramp strategy Understand common pitfalls: how users are assigned across variants and control, how data is interpreted, and how metrics impact is understood

An invaluable tool in Bioinformatics, this unique volume provides both theoretical and experimental results, and describes basic principles of computational intelligence and pattern analysis while deepening the reader's understanding of the ways in which these principles can be used for analyzing biological data in an efficient manner. This book synthesizes current research in the integration of computational intelligence and pattern analysis techniques, either individually or in a hybridized manner. The purpose is to analyze biological data and enable extraction of more meaningful information and insight from it. Biological data for analysis include sequence data, secondary and tertiary structure data, and microarray data. These data types are complex and advanced methods are required, including the use of domain-specific knowledge for reducing search space, dealing with uncertainty, partial truth and imprecision, efficient linear and/or sub-linear scalability, incremental approaches to knowledge discovery, and increased level and intelligence of interactivity with human experts and decision makers Chapters authored by leading researchers in CI in biology informatics. Covers highly relevant topics: rational drug design; analysis of microRNAs and their involvement in human diseases. Supplementary material included: program code and relevant data sets correspond to chapters. Note: The ebook version does not provide access to the companion files.