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AI has acquired startling new language capabilities in just the past few years. Driven by the rapid advances in deep learning, language AI systems are able to write and understand text better than ever before. This trend enables the rise of new features, products, and entire industries. With this book, Python developers will learn the practical tools and concepts they need to use these capabilities today.You'll learn how to use the power of pre-trained large language models for use cases like copywriting and summarization; create semantic search systems that go beyond keyword matching; build systems that classify and cluster text to enable scalable understanding of large amounts of text documents; and use existing libraries and pre-trained models for text classification, search, and clusterings.This book also shows you how to:Build advanced LLM pipelines to cluster text documents and explore the topics they belong toBuild semantic search engines that go beyond keyword search with methods like dense retrieval and rerankersLearn various use cases where these models can provide valueUnderstand the architecture of underlying Transformer models like BERT and GPTGet a deeper understanding of how LLMs are trainedUnderstanding how different methods of fine-tuning optimize LLMs for specific applications (generative model fine-tuning, contrastive fine-tuning, in-context learning, etc.) Read more

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Make better decisions with this deconstructed guide to data analyticsWant to add data analytics to your skill stack? Having trouble finding where to start?Cell by cell, bit by bit, this book teaches you the vocabulary, tools, and basic algorithms to think like a data scientist. Using the "Lego set" approach, each chapter adds and connects individual blocks of knowledge to build your data literacy. This linear structure to unpacking data analytics takes you from zero to confidently analyzing and discussing data problems.Who is This Book For?This book is ideal for anyone interested in making sense of data analytics without the assumption that you understand data science terminology or advanced math. If you've tried to learn data analytics before and failed, this book is for you.Practical ApproachThis book takes a hands-on approach to learning. This includes practical examples, visual examples, as well as two bonus coding exercises in Python, including free video content to walk you through both exercises. By the end of the book, you will have the practical knowledge to tackle real data problems in your organization or daily life.What You Will Learn• How to recognize the common data types every data scientist needs to master• Where to store your data, including Big Data• New trends in data analytics, including what is Alternative Data and why few people know about it!• How to explain the distinction between Data Mining, Machine Learning, and Analytics to your colleagues• When and how to use Regression Analysis, Classification, Clustering, Association Analysis, and Natural Language Processing• How to make better business decisions using Data Visualization and Business IntelligenceJoin this introductory course by buying a copy now or sending a free sample to your chosen device. Read more

Data engineering has grown rapidly in the past decade, leaving many software engineers, data scientists, and analysts looking for a comprehensive view of this practice. With this practical book, you'll learn how to plan and build systems to serve the needs of your organization and customers by evaluating the best technologies available through the framework of the data engineering lifecycle.Authors Joe Reis and Matt Housley walk you through the data engineering lifecycle and show you how to stitch together a variety of cloud technologies to serve the needs of downstream data consumers. You'll understand how to apply the concepts of data generation, ingestion, orchestration, transformation, storage, and governance that are critical in any data environment regardless of the underlying technology.This book will help you:Get a concise overview of the entire data engineering landscapeAssess data engineering problems using an end-to-end framework of best practicesCut through marketing hype when choosing data technologies, architecture, and processesUse the data engineering lifecycle to design and build a robust architectureIncorporate data governance and security across the data engineering lifecycle Read more

YAMLYAML stands for YAML Ain't Markup Language. It is a very popular data serialization format for storing configuration data or exchanging data between people and computers. It is more human-readable and concise than XML or JSON. It has become a key technology for creating complex configurations in various modern technologies like automation, testing, containerization, cloud (AWS, Azure, or GCP) deployment, DevOps, etc.Why Learn YAML?Learning about YAML would be very beneficial for today's software engineers. Below is a list of some popular software tools and frameworks that extensively use the YAML data format.DevOps - CircleCI, Travis CI, Azure DevOps, Jenkins XFrameworks: Spring Boot,Automation: Chef Automate, AnsibleContainer: Docker, KubernetesDocumentation: Swagger,and many more ...Who is this book for?If you looking to build expertise in using YAML for your projects? Then this book is right for you.This book is for developers of all skill levels who wish to stay up to date with YAML. This book is intended for Software Engineers, Full Stack Web Developers, DevOps Engineers, Software Architects, Managers, and anyone who wants to learn about YAML.What topics are covered in this book?The main objective of this book is to give you a foundation for using YAML. To do that, it takes you on a journey where we start with basic concepts of data serialization and then gradually explore more advanced topics like YAML syntax, anchors, etc.You will also get to know about various parsers and emitters available in the modern programming languages and a list of various tools which are leveraging YAML technology.What will you learn?This book will cover everything you need to know about YAML. We start by going through the syntax per the latest YAML 1.3 specification. Next, we will also cover the fundamental principles behind it and how it works, and several YAML features that include:scalar data types (i.e., strings and numbers)containers (a list of elements)rich data types (i.e., dates and times)anchors (a way to create links in YAML documents)advanced features of YAML processors.comparison of YAML with other data formats like JSON and XML.tools and technologies that leverage the YAML format.After reading this book, you will be able to:Understand what YAML is and how it works.Know the differences between XML, JSON, and YAML.Learn the basics of the YAML syntax.Use YAML in your projects to serialize and deserialize data.Know what tools are available to work with YAML.Learn how to use anchors in a YAML document.By the end of this book, you will be well-versed with the YAML language and will have all that you need to get started with using it for your projects.TestimonialsCheck out what existing readers have to say about this book:- This book is very helpful for beginners to learn easily from the basics and gain expertise. The core topics are covered in-depth step by step with live examples and all fundamentals concepts; I truly enjoyed learning this course very informative and highly recommended to everyone. I am waiting eagerly to learn more topics.- Hi Tarun, I think you have covered everything required. I was not aware of YAML before, and now when I am studying AWS cloud formation, then I realize the content you have covered is very much useful. Thanks for the course.- The simplicity with which the author has explained this topic is appreciable. Even a beginner can understand it very well with clarity. I very much liked the comparison used among JSON, XML, and YAML to explain the concepts, which helped me to understand the context of YAML usage. Read more

Modeling and Simulation in Python teaches readers how to analyze real-world scenarios using the Python programming language, requiring no more than a background in high school math.Modeling and Simulation in Python is a thorough but easy-to-follow introduction to physical modeling—that is, the art of describing and simulating real-world systems. Readers are guided through modeling things like world population growth, infectious disease, bungee jumping, baseball flight trajectories, celestial mechanics, and more while simultaneously developing a strong understanding of fundamental programming concepts like loops, vectors, and functions.Clear and concise, with a focus on learning by doing, the author spares the reader abstract, theoretical complexities and gets right to hands-on examples that show how to produce useful models and simulations. Read more

Learn everything you need to know to manage data as a product and shift toward a more modular and decentralized socio-technical data architecture to deliver business value in an incremental, measurable, and sustainable wayKey FeaturesLeverage data-as-product to unlock the modular platform potential and fix flaws in traditional monolithic architecturesLearn how to identify, implement, and operate data products throughout their life cycleDesign and execute a forward-thinking strategy to turn your data products into organizational assetsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionTraditional monolithic data platforms struggle with scalability and burden central data teams with excessive cognitive load, leading to challenges in managing technological debt. As maintenance costs escalate, these platforms lose their ability to provide sustained value over time. With two decades of hands-on experience implementing data solutions and his pioneering work in the Open Data Mesh Initiative, Andrea Gioia brings practical insights and proven strategies for transforming how organizations manage their data assets.Managing Data as a Product introduces a modular and distributed approach to data platform development, centered on the concept of data products. In this book, you’ll explore the rationale behind this shift, understand the core features and structure of data products, and learn how to identify, develop, and operate them in a production environment. The book guides you through designing and implementing an incremental, value-driven strategy for adopting data product-centered architectures, including strategies for securing buy-in from stakeholders. Additionally, it explores data modeling in distributed environments, emphasizing its crucial role in fully leveraging modern generative AI solutions.By the end of this book, you’ll have gained a comprehensive understanding of product-centric data architecture and the essential steps needed to adopt this modern approach to data management.What you will learnOvercome the challenges in scaling monolithic data platforms, including cognitive load, tech debt, and maintenance costsDiscover the benefits of adopting a data-as-a-product approach for scalability and sustainabilityNavigate the complete data product lifecycle, from inception to decommissioningAutomate data product lifecycle management using a self-serve platformImplement an incremental, value-driven strategy for transitioning to data-product-centric architecturesOptimize data modeling in distributed environments to enhance GenAI-based use casesWho this book is forIf you’re an experienced data engineer, data leader, architect, or practitioner committed to reimagining your data architecture and designing one that enables your organization to get the most value from your data in a sustainable and scalable way, this book is for you. Whether you’re a staff engineer, product manager, or a software engineering leader or executive, you’ll find this book useful. Familiarity with basic data engineering principles and practices is assumed.Table of ContentsFrom Data as a Byproduct to Data as a ProductData ProductsData Product-Centered ArchitecturesIdentifying Data Products and Prioritizing DevelopmentsDesigning and Implementing Data ProductsOperating Data Products in ProductionAutomating Data Product Lifecycle ManagementMoving through the Adoption JourneyTeam Topologies and Data Ownership at ScaleDistributed Data ModelingBuilding an AI-Ready Information ArchitectureBringing It All Together Read more

In this "important and comprehensive" guide to statistical thinking (New Yorker), discover how data literacy is changing the world and gives you a better understanding of life’s biggest problems.   Statistics are everywhere, as integral to science as they are to business, and in the popular media hundreds of times a day. In this age of big data, a basic grasp of statistical literacy is more important than ever if we want to separate the fact from the fiction, the ostentatious embellishments from the raw evidence -- and even more so if we hope to participate in the future, rather than being simple bystanders. In The Art of Statistics, world-renowned statistician David Spiegelhalter shows readers how to derive knowledge from raw data by focusing on the concepts and connections behind the math. Drawing on real world examples to introduce complex issues, he shows us how statistics can help us determine the luckiest passenger on the Titanic, whether a notorious serial killer could have been caught earlier, and if screening for ovarian cancer is beneficial. The Art of Statistics not only shows us how mathematicians have used statistical science to solve these problems -- it teaches us how we too can think like statisticians. We learn how to clarify our questions, assumptions, and expectations when approaching a problem, and -- perhaps even more importantly -- we learn how to responsibly interpret the answers we receive. Combining the incomparable insight of an expert with the playful enthusiasm of an aficionado, The Art of Statistics is the definitive guide to stats that every modern person needs. Read more

Analytics and Data Reporting for Beginners is your starting point for understanding and utilizing data to make informed business decisions. This guide covers the basics of data collection, analysis, and visualization, breaking down complex concepts into easy-to-understand steps. Learn how to create simple yet effective reports and gain insights that can drive meaningful outcomes. Perfect for anyone new to data analytics looking to build a strong foundational skill set. Read more

Everything you need to know to get into Python coding, with 7 books in onePython All-in-One For Dummies is your one-stop source for answers to all your Python questions. From creating apps to building complex web sites to sorting big data, Python provides a way to get the work done. This book is great as a starting point for those new to coding, and it also makes a perfect reference for experienced coders looking for more than the basics. Apply your Python skills to data analysis, learn to write AI-assisted code using GitHub CoPilot, and discover many more exciting uses for this top programming language. Get started coding in Python—even if you’re new to computer programmingReference all the essentials and the latest updates, so your code is air-tightLearn how Python can be a solution for large-scale projects and big datasetsAccelerate your career path with this comprehensive guide to learning PythonExperienced and would-be coders alike will love this easy-to-follow guide to learning and applying Python. Read more

Data projects are an intrinsic part of an organization’s technical ecosystem, but data engineers in many companies continue to work on problems that others have already solved. This hands-on guide shows you how to provide valuable data by focusing on various aspects of data engineering, including data ingestion, data quality, idempotency, and more.Author Bartosz Konieczny guides you through the process of building reliable end-to-end data engineering projects, from data ingestion to data observability, focusing on data engineering design patterns that solve common business problems in a secure and storage-optimized manner. Each pattern includes a user-facing description of the problem, solutions, and consequences that place the pattern into the context of real-life scenarios.Throughout this journey, you’ll use open source data tools and public cloud services to apply each pattern. You'll learn:Challenges data engineers face and their impact on data systemsHow these challenges relate to data system componentsUseful applications of data engineering patternsHow to identify and fix issues with your current data componentsTTechnology-agnostic solutions to new and existing data projects, with open source implementation examplesBartosz Konieczny is a freelance data engineer who's been coding since 2010. He's held various senior hands-on positions that allowed him to work on many data engineering problems in batch and stream processing. Read more

The definitive guide to dimensional design for your data warehouseLearn the best practices of dimensional design. Star Schema: The Complete Reference offers in-depth coverage of design principles and their underlying rationales. Organized around design concepts and illustrated with detailed examples, this is a step-by-step guidebook for beginners and a comprehensive resource for experts.This all-inclusive volume begins with dimensional design fundamentals and shows how they fit into diverse data warehouse architectures, including those of W.H. Inmon and Ralph Kimball. The book progresses through a series of advanced techniques that help you address real-world complexity, maximize performance, and adapt to the requirements of BI and ETL software products. You are furnished with design tasks and deliverables that can be incorporated into any project, regardless of architecture or methodology.Master the fundamentals of star schema design and slow change processingIdentify situations that call for multiple stars or cubesEnsure compatibility across subject areas as your data warehouse growsAccommodate repeating attributes, recursive hierarchies, and poor data qualitySupport conflicting requirements for historic dataHandle variation within a business process and correlation of disparate activitiesBoost performance using derived schemas and aggregatesLearn when it's appropriate to adjust designs for BI and ETL tools Read more

Leverage this example-packed, comprehensive guide for all your Python computational needsKey FeaturesLearn the first steps within Python to highly specialized conceptsExplore examples and code snippets taken from typical programming situations within scientific computing.Delve into essential computer science concepts like iterating, object-oriented programming, testing, and MPI presented in strong connection to applications within scientific computing.Book DescriptionPython has tremendous potential within the scientific computing domain. This updated edition of Scientific Computing with Python features new chapters on graphical user interfaces, efficient data processing, and parallel computing to help you perform mathematical and scientific computing efficiently using Python.This book will help you to explore new Python syntax features and create different models using scientific computing principles. The book presents Python alongside mathematical applications and demonstrates how to apply Python concepts in computing with the help of examples involving Python 3.8. You'll use pandas for basic data analysis to understand the modern needs of scientific computing, and cover data module improvements and built-in features. You'll also explore numerical computation modules such as NumPy and SciPy, which enable fast access to highly efficient numerical algorithms. By learning to use the plotting module Matplotlib, you will be able to represent your computational results in talks and publications. A special chapter is devoted to SymPy, a tool for bridging symbolic and numerical computations.By the end of this Python book, you'll have gained a solid understanding of task automation and how to implement and test mathematical algorithms within the realm of scientific computing.What you will learnUnderstand the building blocks of computational mathematics, linear algebra, and related Python objectsUse Matplotlib to create high-quality figures and graphics to draw and visualize resultsApply object-oriented programming (OOP) to scientific computing in PythonDiscover how to use pandas to enter the world of data processingHandle exceptions for writing reliable and usable codeCover manual and automatic aspects of testing for scientific programmingGet to grips with parallel computing to increase computation speedWho this book is forThis book is for students with a mathematical background, university teachers designing modern courses in programming, data scientists, researchers, developers, and anyone who wants to perform scientific computation in Python.Table of ContentsGetting StartedVariables and Basic TypesContainer TypesLinear Algebra – ArraysAdvanced Array ConceptsPlottingFunctionsClassesIteratingSeries and Dataframes - Working With PandasCommunication by a Graphical User InterfaceError and Exception HandlingNamespaces, Scopes, and ModulesInput and OutputTestingSymbolic Computations - SymPyInteracting with the Operating SystemPython for Parallel ComputingComprehensive Examples Read more

Unlock the power of data and artificial intelligence with the Fundamentals Series—a comprehensive two-book collection designed to equip you with the essential skills to succeed in today’s technology-driven world.Whether you’re looking to build a career in data analytics, harness the potential of AI, or monetize cutting-edge tools like large language models, this series provides the ultimate roadmap.Inside, you’ll discover:✅ Fundamentals of Data Analytics – Learn how to analyze, interpret, and visualize data to drive business success and innovation.✅ Fundamentals of Artificial Intelligence & Large Language Models – Master prompt engineering, understand AI’s real-world applications, and explore lucrative opportunities in the AI economy.With beginner-friendly insights, real-world applications, and strategies for success, this series is your key to staying competitive in the digital age. Whether you're a professional, entrepreneur, or curious learner, the Fundamentals Series will help you turn knowledge into opportunity.Don’t wait to take advantage of these insights—because plenty of others are already using AI to their financial benefit. Get started now to stay ahead of the game and find financial success! Read more

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Gain expertise in advanced deep learning domains such as neural networks, meta-learning, graph neural networks, and memory augmented neural networks using the Python ecosystemKey FeaturesGet to grips with building faster and more robust deep learning architecturesInvestigate and train convolutional neural network (CNN) models with GPU-accelerated libraries such as TensorFlow and PyTorchApply deep neural networks (DNNs) to computer vision problems, NLP, and GANsBook DescriptionIn order to build robust deep learning systems, you’ll need to understand everything from how neural networks work to training CNN models. In this book, you’ll discover newly developed deep learning models, methodologies used in the domain, and their implementation based on areas of application.You’ll start by understanding the building blocks and the math behind neural networks, and then move on to CNNs and their advanced applications in computer vision. You'll also learn to apply the most popular CNN architectures in object detection and image segmentation. Further on, you’ll focus on variational autoencoders and GANs. You’ll then use neural networks to extract sophisticated vector representations of words, before going on to cover various types of recurrent networks, such as LSTM and GRU. You’ll even explore the attention mechanism to process sequential data without the help of recurrent neural networks (RNNs). Later, you’ll use graph neural networks for processing structured data, along with covering meta-learning, which allows you to train neural networks with fewer training samples. Finally, you’ll understand how to apply deep learning to autonomous vehicles.By the end of this book, you’ll have mastered key deep learning concepts and the different applications of deep learning models in the real world.What you will learnCover advanced and state-of-the-art neural network architecturesUnderstand the theory and math behind neural networksTrain DNNs and apply them to modern deep learning problemsUse CNNs for object detection and image segmentationImplement generative adversarial networks (GANs) and variational autoencoders to generate new imagesSolve natural language processing (NLP) tasks, such as machine translation, using sequence-to-sequence modelsUnderstand DL techniques, such as meta-learning and graph neural networksWho this book is forThis book is for data scientists, deep learning engineers and researchers, and AI developers who want to further their knowledge of deep learning and build innovative and unique deep learning projects. Anyone looking to get to grips with advanced use cases and methodologies adopted in the deep learning domain using real-world examples will also find this book useful. Basic understanding of deep learning concepts and working knowledge of the Python programming language is assumed.Table of ContentsThe Nuts and Bolts of Neural NetworksUnderstanding Convolutional NetworksAdvanced Convolutional NetworksObject Detection and Image SegmentationGenerative ModelsLanguage ModellingUnderstanding Recurrent NetworksSequence-to-Sequence Models and AttentionEmerging Neural Network DesignsMeta LearningDeep Learning for Autonomous Vehicles Read more

Six-Step Relational Database DesignTM bridges the gaps between database theory, database modeling, and database implementation by outlining a simple but reliable six-step process for accurately modeling user data on a Crow's Foot Relational Model Diagram, and then demonstrating how to implement this model on any relational database management system.The second edition contains a new chapter on implementation that goes through the steps necessary to implement each of the case studies on a relational database management system, clearly relating the design to implementation and database theory. In addition, questions are also included at the end of each of the six steps and one of the previous case studies has been replaced, making the case study selection more diverse.Six-Step Relational Database DesignTM uses three case studies and starts with a statement of the problem by the client and then goes through the six steps necessary to create a reliable and accurate data model of the client's business requirements. This model can then be used to implement the database on any relational database management system.Six-Step Relational Database DesignTM should be used as a handbook for students and professionals in the software-development field. The technique described in this book can be used by students for quickly developing relational databases for their applications, and by professionals for developing sturdy, reliable, and accurate relational database models for their software applications. Read more

Take your first steps to becoming a fully qualified data analyst by learning how to explore complex datasetsKey FeaturesMaster each concept through practical exercises and activitiesDiscover various statistical techniques to analyze your dataImplement everything you've learned on a real-world case study to uncover valuable insightsBook DescriptionEvery day, businesses operate around the clock, and a huge amount of data is generated at a rapid pace. This book helps you analyze this data and identify key patterns and behaviors that can help you and your business understand your customers at a deep, fundamental level.SQL for Data Analytics, Third Edition is a great way to get started with data analysis, showing how to effectively sort and process information from raw data, even without any prior experience.You will begin by learning how to form hypotheses and generate descriptive statistics that can provide key insights into your existing data. As you progress, you will learn how to write SQL queries to aggregate, calculate, and combine SQL data from sources outside of your current dataset. You will also discover how to work with advanced data types, like JSON. By exploring advanced techniques, such as geospatial analysis and text analysis, you will be able to understand your business at a deeper level. Finally, the book lets you in on the secret to getting information faster and more effectively by using advanced techniques like profiling and automation.By the end of this book, you will be proficient in the efficient application of SQL techniques in everyday business scenarios and looking at data with the critical eye of analytics professional.What you will learnUse SQL to clean, prepare, and combine different datasetsAggregate basic statistics using GROUP BY clausesPerform advanced statistical calculations using a WINDOW functionImport data into a database to combine with other tablesExport SQL query results into various sourcesAnalyze special data types in SQL, including geospatial, date/time, and JSON dataOptimize queries and automate tasksThink about data problems and find answers using SQLWho this book is forIf you're a database engineer looking to transition into analytics or a backend engineer who wants to develop a deeper understanding of production data and gain practical SQL knowledge, you will find this book useful. This book is also ideal for data scientists or business analysts who want to improve their data analytics skills using SQL.Basic familiarity with SQL (such as basic SELECT, WHERE, and GROUP BY clauses) as well as a good understanding of linear algebra, statistics, and PostgreSQL 14 are necessary to make the most of this SQL data analytics book.Table of ContentsUnderstanding and Describing DataThe Basics of SQL for AnalyticsSQL for Data PreparationAggregate Functions for Data AnalysisWindow Functions for Data AnalysisImporting and Exporting DataAnalytics Using Complex Data TypesPerformant SQLUsing SQL to Uncover the Truth – a Case Study Read more

Geospatial Data Science Essentials is your hands-on guide to mastering the science of geospatial analytics using Python. Designed for practitioners and enthusiasts alike, this book distills years of experience by wrapping up 101 key concepts from theory to implementation, ensuring you gain a practical understanding of the tools and methods that define the geospatial data science landscape today.Whether you are a seasoned data scientist, a GIS professional, a newcomer to spatial data, or simply a map lover, this book provides you solid foundation to level up your skills. The book is centered around practicalities, as you will explore real-world examples with compact code throughout ten topics and 101 sections. From understanding spatial data structures to leveraging advanced analytical techniques, from spatial networks to machine learning, this book equips you with a wide range of knowledge to navigate and succeed in the rapidly evolving field of geospatial data science.Embrace the journey into geospatial data science with this essential guide and discover the power of Python in unlocking the potential of spatial analytics. Read more