Humble Tech Book Bundle: Data Science by O'Reilly

Data Engineering for Multimodal AI

Vasundra Srinivasan, O'Reilly

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AI is only as capable as the context it’s given, and context is a data engineering problem before it’s a model problem. As multimodal AI systems and… AI is only as capable as the context it’s given, and context is a data engineering problem before it’s a model problem. As multimodal AI systems and applications become increasingly sophisticated and data-hungry, the infrastructure that produces and governs that context has to evolve to keep pace. Data Engineering for Multimodal AI is one of the first practical guides for data engineers, machine learning engineers, and MLOps specialists looking to rapidly master the skills needed to build robust, scalable data infrastructures that multimodal AI systems and applications depend on for effective context engineering. You’ll follow the entire lifecycle of AI-driven data engineering, from conceptualizing data architectures to implementing data pipelines optimized for multimodal learning in both cloud-native and on-premises environments. And each chapter includes step-by-step guides and best practices for implementing key concepts. Design and implement cloud-native data architectures optimized for multimodal AI workloads Build efficient and scalable ETL processes for preparing diverse AI training data Implement real-time data processing pipelines for multimodal AI inference Develop and manage feature stores that support multiple data modalities Apply data governance and security practices specific to multimodal AI projects Optimize data storage and retrieval for various types of multimodal ML models Integrate data versioning and lineage tracking in multimodal AI workflows Implement data-quality frameworks to ensure reliable outcomes across data types Design data pipelines that support responsible AI practices in a multimodal context See more

AI-Assisted Statistics for Data Scientists

Peter Bruce, Andrew Bruce, Peter Gedeck, O'Reilly

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3rd Edition

AI-Ready Data Blueprints

Navnit Shukla, Kien Pham, Srikanth Sopirala, Harsha Tadiparthi, O'Reilly

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Companies innovating with generative AI understand that having the right data foundation is critical for success and profitability. To best position… Companies innovating with generative AI understand that having the right data foundation is critical for success and profitability. To best position themselves for long-term success, organizations must prioritize investments in data and AI governance. AI-Ready Data Blueprints is your map to connecting data strategy, GenAI, and ethical practices to build and scale truly effective solutions. Taking a comprehensive, cloud-agnostic approach focused on real-world business challenges, seasoned data and AI experts Navnit Shukla, Kien Pham, Srikanth Sopirala, and Harsha Tadiparthi share actionable insights to guide you in designing and implementing effective data-centric GenAI systems. Whether you're new to GenAI or are already focusing on optimizing it for accuracy, speed, or both, the principles shared in this book will empower you to excel in all your AI endeavors. Identify the key elements of a solid data foundation for generative AI Apply data governance and orchestration techniques to ensure high data quality, access control, and proper data lineage for reliable AI systems Optimize GenAI applications through prompt engineering, fine-tuning, and retrieval-augmented generation Implement security, compliance, and governance measures, including responsible AI practices, transparency, and more See more

Microsoft Power BI Data Analyst Associate Study Guide

Paul Turley, O'Reilly

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Passing the PL-300 exam with 2025 revisions isn't just about memorization—you need to thoroughly know the basic features of Power BI. However, data… Passing the PL-300 exam with 2025 revisions isn't just about memorization—you need to thoroughly know the basic features of Power BI. However, data professionals must also apply best practices that make Power BI solutions scalable and future-proof. The first half of this go-to companion by Paul Turley provides complete coverage of the PL-300 exam objectives for desktop and self-service users, while the second half equips you with necessary best practices and practical skills for real-world success after the exam. Immerse yourself in exam prep, practice questions, and hands-on references for applying time-tested design patterns in Power BI. You'll learn how to transform raw data into actionable insights using Power Query, DAX, and dimensional modeling. Perfect for data analysts and business intelligence developers, this guide shows how Power BI fits into modern data platforms like Azure and Microsoft Fabric, preparing you for the exam and for the evolving world of data engineering. Understand PL-300 exam topics and key prep strategies Use best practices to discover scalable, enterprise-grade Power BI solutions Learn how to correctly apply Power Query, DAX, and visualizations in real-world scenarios with real business data Uncover how to build for scale See how Power BI fits into modern architectures like Azure and Microsoft Fabric See more

Data Contracts

Chad Sanderson, Mark Freeman, B. E. Schmidt, O'Reilly

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Poor data quality can cause major problems for data teams, from breaking revenue-generating data pipelines to losing the trust of data consumers.… Poor data quality can cause major problems for data teams, from breaking revenue-generating data pipelines to losing the trust of data consumers. Despite the importance of data quality, many data teams still struggle to avoid these issues—especially when their data is sourced from upstream workflows outside of their control. The solution: data contracts. Data contracts enable high-quality, well-governed data assets by documenting expectations of the data, establishing ownership of data assets, and then automatically enforcing these constraints within the CI/CD workflow. This practical book introduces data contract architecture with a clear definition of data contracts, explains why the data industry needs them, and shares real-world use cases of data contracts in production. In addition, you'll learn how to implement components of the data contract architecture and understand how they're used in the data lifecycle. Finally, you'll build a case for implementing data contracts in your organization. Authors Chad Sanderson, Mark Freeman, and B.E. Schmidt will help you: Explore real-world applications of data contracts within the industry Understand how to apply each component of this architecture, such as CI/CD, monitoring, version control data, and more Learn how to implement data contracts using open source tools Examine ways to resolve data quality issues using data contract architecture Measure the impact of implementing a data contract in your organization Develop a strategy to determine how data contracts will be used in your organization See more

3D Data Science with Python

Florent Poux, O'Reilly

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Our physical world is grounded in three dimensions. To create technology that can reason about and interact with it, our data must be 3D too. This… Our physical world is grounded in three dimensions. To create technology that can reason about and interact with it, our data must be 3D too. This practical guide offers data scientists, engineers, and researchers a hands-on approach to working with 3D data using Python. From 3D reconstruction to 3D deep learning techniques, you'll learn how to extract valuable insights from massive datasets, including point clouds, voxels, 3D CAD models, meshes, images, and more. Dr. Florent Poux helps you leverage the potential of cutting-edge algorithms and spatial AI models to develop production-ready systems with a focus on automation. You'll get the 3D data science knowledge and code to: Understand core concepts and representations of 3D data Load, manipulate, analyze, and visualize 3D data using powerful Python libraries Apply advanced AI algorithms for 3D pattern recognition (supervised and unsupervised) Use 3D reconstruction techniques to generate 3D datasets Implement automated 3D modeling and generative AI workflows Explore practical applications in areas like computer vision/graphics, geospatial intelligence, scientific computing, robotics, and autonomous driving Build accurate digital environments that spatial AI solutions can leverage Florent Poux is an esteemed authority in the field of 3D data science who teaches and conducts research for top European universities. He's also head professor at the 3D Geodata Academy and innovation director for French Tech 120 companies. See more

Data Engineering Design Patterns

Bartosz Konieczny, O'Reilly

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Data projects are an intrinsic part of an organization's technical ecosystem, but data engineers in many companies continue to work on problems that… 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 systems How these challenges relate to data system components Useful applications of data engineering patterns How to identify and fix issues with your current data components Technology-agnostic solutions to new and existing data projects, with open source implementation examples Bartosz 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. See more

Hands-On APIs for AI and Data Science

Ryan Day, O'Reilly

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Are you ready to grow your skills in AI and data science? A great place to start is learning to build and use APIs in real-world data and AI projects.… Are you ready to grow your skills in AI and data science? A great place to start is learning to build and use APIs in real-world data and AI projects. API skills have become essential for AI and data science success, because they are used in a variety of ways in these fields. With this practical book, data scientists and software developers will gain hands-on experience developing and using APIs with the Python programming language and popular frameworks like FastAPI and StreamLit. As you complete the chapters in the book, you'll be creating portfolio projects that teach you how to: Design APIs that data scientists and AIs love Develop APIs using Python and FastAPI Deploy APIs using multiple cloud providers Create data science projects such as visualizations and models using APIs as a data source Access APIs using generative AI and LLMs See more

Software Engineering for Data Scientists

Catherine Nelson, O'Reilly

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Data science happens in code. The ability to write reproducible, robust, scaleable code is key to a data science project's success—and is absolutely… Data science happens in code. The ability to write reproducible, robust, scaleable code is key to a data science project's success—and is absolutely essential for those working with production code. This practical book bridges the gap between data science and software engineering,and clearly explains how to apply the best practices from software engineering to data science. Examples are provided in Python, drawn from popular packages such as NumPy and pandas. If you want to write better data science code, this guide covers the essential topics that are often missing from introductory data science or coding classes, including how to: Understand data structures and object-oriented programming Clearly and skillfully document your code Package and share your code Integrate data science code with a larger code base Learn how to write APIs Create secure code Apply best practices to common tasks such as testing, error handling, and logging Work more effectively with software engineers Write more efficient, maintainable, and robust code in Python Put your data science projects into production And more See more

Deciphering Data Architectures

James Serra, O'Reilly

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Data fabric, data lakehouse, and data mesh have recently appeared as viable alternatives to the modern data warehouse. These new architectures have… Data fabric, data lakehouse, and data mesh have recently appeared as viable alternatives to the modern data warehouse. These new architectures have solid benefits, but they're also surrounded by a lot of hyperbole and confusion. This practical book provides a guided tour of these architectures to help data professionals understand the pros and cons of each. James Serra, big data and data warehousing solution architect at Microsoft, examines common data architecture concepts, including how data warehouses have had to evolve to work with data lake features. You'll learn what data lakehouses can help you achieve, as well as how to distinguish data mesh hype from reality. Best of all, you'll be able to determine the most appropriate data architecture for your needs. With this book, you'll: Gain a working understanding of several data architectures Learn the strengths and weaknesses of each approach Distinguish data architecture theory from reality Pick the best architecture for your use case Understand the differences between data warehouses and data lakes Learn common data architecture concepts to help you build better solutions Explore the historical evolution and characteristics of data architectures Learn essentials of running an architecture design session, team organization, and project success factors Free from product discussions, this book will serve as a timeless resource for years to come. See more

Data Science: The Hard Parts

Daniel Vaughan, O'Reilly

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This practical guide provides a collection of techniques and best practices that are generally overlooked in most data engineering and data science… This practical guide provides a collection of techniques and best practices that are generally overlooked in most data engineering and data science pedagogy. A common misconception is that great data scientists are experts in the "big themes" of the discipline—machine learning and programming. But most of the time, these tools can only take us so far. In practice, the smaller tools and skills really separate a great data scientist from a not-so-great one. Taken as a whole, the lessons in this book make the difference between an average data scientist candidate and a qualified data scientist working in the field. Author Daniel Vaughan has collected, extended, and used these skills to create value and train data scientists from different companies and industries. With this book, you will: Understand how data science creates value Deliver compelling narratives to sell your data science project Build a business case using unit economics principles Create new features for a ML model using storytelling Learn how to decompose KPIs Perform growth decompositions to find root causes for changes in a metric Daniel Vaughan is head of data at Clip, the leading paytech company in Mexico. He's the author of Analytical Skills for AI and Data Science (O'Reilly). See more

Data Management at Scale

Piethein Strengholt, O'Reilly

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2nd Edition

Python Data Science Handbook

Jake VanderPlas, O'Reilly

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2nd Edition

Practical Linear Algebra for Data Science

Mike X Cohen, O'Reilly

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If you want to work in any computational or technical field, you need to understand linear algebra. As the study of matrices and operations acting… If you want to work in any computational or technical field, you need to understand linear algebra. As the study of matrices and operations acting upon them, linear algebra is the mathematical basis of nearly all algorithms and analyses implemented in computers. But the way it's presented in decades-old textbooks is much different from how professionals use linear algebra today to solve real-world modern applications. This practical guide from Mike X Cohen teaches the core concepts of linear algebra as implemented in Python, including how they're used in data science, machine learning, deep learning, computational simulations, and biomedical data processing applications. Armed with knowledge from this book, you'll be able to understand, implement, and adapt myriad modern analysis methods and algorithms. Ideal for practitioners and students using computer technology and algorithms, this book introduces you to: The interpretations and applications of vectors and matrices Matrix arithmetic (various multiplications and transformations) Independence, rank, and inverses Important decompositions used in applied linear algebra (including LU and QR) Eigendecomposition and singular value decomposition Applications including least-squares model fitting and principal components analysis See more

Data Quality Fundamentals

Barr Moses, Lior Gavish, Molly Vorwerck, O'Reilly

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Do your product dashboards look funky? Are your quarterly reports stale? Is the data set you're using broken or just plain wrong? These problems… Do your product dashboards look funky? Are your quarterly reports stale? Is the data set you're using broken or just plain wrong? These problems affect almost every team, yet they're usually addressed on an ad hoc basis and in a reactive manner. If you answered yes to these questions, this book is for you. Many data engineering teams today face the "good pipelines, bad data" problem. It doesn't matter how advanced your data infrastructure is if the data you're piping is bad. In this book, Barr Moses, Lior Gavish, and Molly Vorwerck, from the data observability company Monte Carlo, explain how to tackle data quality and trust at scale by leveraging best practices and technologies used by some of the world's most innovative companies. Build more trustworthy and reliable data pipelines Write scripts to make data checks and identify broken pipelines with data observability Learn how to set and maintain data SLAs, SLIs, and SLOs Develop and lead data quality initiatives at your company Learn how to treat data services and systems with the diligence of production software Automate data lineage graphs across your data ecosystem Build anomaly detectors for your critical data assets See more

Python for Data Analysis

Wes McKinney, O'Reilly

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3rd Edition

Fundamentals of Data Engineering

Joe Reis, Matt Housley, O'Reilly

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Data engineering has grown rapidly in the past decade, leaving many software engineers, data scientists, and analysts looking for a comprehensive view… 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 landscape Assess data engineering problems using an end-to-end framework of best practices Cut through marketing hype when choosing data technologies, architecture, and processes Use the data engineering lifecycle to design and build a robust architecture Incorporate data governance and security across the data engineering lifecycle See more

Essential Math for Data Science

Thomas Nield, O'Reilly

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Master the math needed to excel in data science, machine learning, and statistics. In this book author Thomas Nield guides you through areas like… Master the math needed to excel in data science, machine learning, and statistics. In this book author Thomas Nield guides you through areas like calculus, probability, linear algebra, and statistics and how they apply to techniques like linear regression, logistic regression, and neural networks. Along the way you'll also gain practical insights into the state of data science and how to use those insights to maximize your career. Learn how to: Use Python code and libraries like SymPy, NumPy, and scikit-learn to explore essential mathematical concepts like calculus, linear algebra, statistics, and machine learning Understand techniques like linear regression, logistic regression, and neural networks in plain English, with minimal mathematical notation and jargon Perform descriptive statistics and hypothesis testing on a dataset to interpret p-values and statistical significance Manipulate vectors and matrices and perform matrix decomposition Integrate and build upon incremental knowledge of calculus, probability, statistics, and linear algebra, and apply it to regression models including neural networks Navigate practically through a data science career and avoid common pitfalls, assumptions, and biases while tuning your skill set to stand out in the job market See more

Data Science on the Google Cloud Platform

Valliappa Lakshmanan, O'Reilly

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2nd Edition

Data Science at the Command Line

Jeroen Janssens, O'Reilly

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2nd Edition

Data Science on AWS

Chris Fregly, Antje Barth, O'Reilly

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With this practical book, AI and machine learning practitioners will learn how to successfully build and deploy data science projects on Amazon Web… With this practical book, AI and machine learning practitioners will learn how to successfully build and deploy data science projects on Amazon Web Services. The Amazon AI and machine learning stack unifies data science, data engineering, and application development to help level up your skills. This guide shows you how to build and run pipelines in the cloud, then integrate the results into applications in minutes instead of days. Throughout the book, authors Chris Fregly and Antje Barth demonstrate how to reduce cost and improve performance. Apply the Amazon AI and ML stack to real-world use cases for natural language processing, computer vision, fraud detection, conversational devices, and more Use automated machine learning to implement a specific subset of use cases with SageMaker Autopilot Dive deep into the complete model development lifecycle for a BERT-based NLP use case including data ingestion, analysis, model training, and deployment Tie everything together into a repeatable machine learning operations pipeline Explore real-time ML, anomaly detection, and streaming analytics on data streams with Amazon Kinesis and Managed Streaming for Apache Kafka Learn security best practices for data science projects and workflows including identity and access management, authentication, authorization, and more See more

Data Governance: The Definitive Guide

Evren Eryurek, Uri Gilad, Valliappa Lakshmanan, Anita Kibunguchy-Grant, Jessi Ashdown, O'Reilly

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As you move data to the cloud, you need to consider a comprehensive approach to data governance, along with well-defined and agreed-upon policies to… As you move data to the cloud, you need to consider a comprehensive approach to data governance, along with well-defined and agreed-upon policies to ensure your organization meets compliance requirements. Data governance incorporates the ways people, processes, and technology work together to ensure data is trustworthy and can be used effectively. This practical guide shows you how to effectively implement and scale data governance throughout your organization. Chief information, data, and security officers and their teams will learn strategy and tooling to support democratizing data and unlocking its value while enforcing security, privacy, and other governance standards. Through good data governance, you can inspire customer trust, enable your organization to identify business efficiencies, generate more competitive offerings, and improve customer experience. This book shows you how. You'll learn: Data governance strategies addressing people, processes, and tools Benefits and challenges of a cloud-based data governance approach How data governance is conducted from ingest to preparation and use How to handle the ongoing improvement of data quality Challenges and techniques in governing streaming data Data protection for authentication, security, backup, and monitoring How to build a data culture in your organization See more

Semantic Modeling for Data

Panos Alexopoulos, O'Reilly

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What value does semantic data modeling offer? As an information architect or data science professional, let’s say you have an abundance of the… What value does semantic data modeling offer? As an information architect or data science professional, let’s say you have an abundance of the right data and the technology to extract business gold—but you still fail. The reason? Bad data semantics. In this practical and comprehensive field guide, author Panos Alexopoulos takes you on an eye-opening journey through semantic data modeling as applied in the real world. You’ll learn how to master this craft to increase the usability and value of your data and applications. You’ll also explore the pitfalls to avoid and dilemmas to overcome for building high-quality and valuable semantic representations of data. Understand the fundamental concepts, phenomena, and processes related to semantic data modeling Examine the quirks and challenges of semantic data modeling and learn how to effectively leverage the available frameworks and tools Avoid mistakes and bad practices that can undermine your efforts to create good data models Learn about model development dilemmas, including representation, expressiveness and content, development, and governance Organize and execute semantic data initiatives in your organization, tackling technical, strategic, and organizational challenges See more

Data Science from Scratch

Joel Grus, O'Reilly

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2nd Edition

Fundamentals of Data Visualization

Claus O. Wilke, O'Reilly

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Effective visualization is the best way to communicate information from the increasingly large and complex datasets in the natural and social… Effective visualization is the best way to communicate information from the increasingly large and complex datasets in the natural and social sciences. But with the increasing power of visualization software today, scientists, engineers, and business analysts often have to navigate a bewildering array of visualization choices and options. This practical book takes you through many commonly encountered visualization problems, and it provides guidelines on how to turn large datasets into clear and compelling figures. What visualization type is best for the story you want to tell? How do you make informative figures that are visually pleasing? Author Claus O. Wilke teaches you the elements most critical to successful data visualization. Explore the basic concepts of color as a tool to highlight, distinguish, or represent a value Understand the importance of redundant coding to ensure you provide key information in multiple ways Use the book’s visualizations directory, a graphical guide to commonly used types of data visualizations Get extensive examples of good and bad figures Learn how to use figures in a document or report and how employ them effectively to tell a compelling story See more

Data Science on the Google Cloud Platform

Valliappa Lakshmanan, O'Reilly

download, pdf, epub

2nd Edition

Data Science at the Command Line

Jeroen Janssens, O'Reilly

download, pdf, epub

2nd Edition

Data Science on AWS

Chris Fregly, Antje Barth, O'Reilly

download, pdf, epub

With this practical book, AI and machine learning practitioners will learn how to successfully build and deploy data science projects on Amazon Web… With this practical book, AI and machine learning practitioners will learn how to successfully build and deploy data science projects on Amazon Web Services. The Amazon AI and machine learning stack unifies data science, data engineering, and application development to help level up your skills. This guide shows you how to build and run pipelines in the cloud, then integrate the results into applications in minutes instead of days. Throughout the book, authors Chris Fregly and Antje Barth demonstrate how to reduce cost and improve performance. Apply the Amazon AI and ML stack to real-world use cases for natural language processing, computer vision, fraud detection, conversational devices, and more Use automated machine learning to implement a specific subset of use cases with SageMaker Autopilot Dive deep into the complete model development lifecycle for a BERT-based NLP use case including data ingestion, analysis, model training, and deployment Tie everything together into a repeatable machine learning operations pipeline Explore real-time ML, anomaly detection, and streaming analytics on data streams with Amazon Kinesis and Managed Streaming for Apache Kafka Learn security best practices for data science projects and workflows including identity and access management, authentication, authorization, and more See more

Data Governance: The Definitive Guide

Evren Eryurek, Uri Gilad, Valliappa Lakshmanan, Anita Kibunguchy-Grant, Jessi Ashdown, O'Reilly

download, pdf, epub

As you move data to the cloud, you need to consider a comprehensive approach to data governance, along with well-defined and agreed-upon policies to… As you move data to the cloud, you need to consider a comprehensive approach to data governance, along with well-defined and agreed-upon policies to ensure your organization meets compliance requirements. Data governance incorporates the ways people, processes, and technology work together to ensure data is trustworthy and can be used effectively. This practical guide shows you how to effectively implement and scale data governance throughout your organization. Chief information, data, and security officers and their teams will learn strategy and tooling to support democratizing data and unlocking its value while enforcing security, privacy, and other governance standards. Through good data governance, you can inspire customer trust, enable your organization to identify business efficiencies, generate more competitive offerings, and improve customer experience. This book shows you how. You'll learn: Data governance strategies addressing people, processes, and tools Benefits and challenges of a cloud-based data governance approach How data governance is conducted from ingest to preparation and use How to handle the ongoing improvement of data quality Challenges and techniques in governing streaming data Data protection for authentication, security, backup, and monitoring How to build a data culture in your organization See more

Semantic Modeling for Data

Panos Alexopoulos, O'Reilly

download, pdf, epub

What value does semantic data modeling offer? As an information architect or data science professional, let’s say you have an abundance of the… What value does semantic data modeling offer? As an information architect or data science professional, let’s say you have an abundance of the right data and the technology to extract business gold—but you still fail. The reason? Bad data semantics. In this practical and comprehensive field guide, author Panos Alexopoulos takes you on an eye-opening journey through semantic data modeling as applied in the real world. You’ll learn how to master this craft to increase the usability and value of your data and applications. You’ll also explore the pitfalls to avoid and dilemmas to overcome for building high-quality and valuable semantic representations of data. Understand the fundamental concepts, phenomena, and processes related to semantic data modeling Examine the quirks and challenges of semantic data modeling and learn how to effectively leverage the available frameworks and tools Avoid mistakes and bad practices that can undermine your efforts to create good data models Learn about model development dilemmas, including representation, expressiveness and content, development, and governance Organize and execute semantic data initiatives in your organization, tackling technical, strategic, and organizational challenges See more

Data Science from Scratch

Joel Grus, O'Reilly

download, epub, pdf

2nd Edition

Fundamentals of Data Visualization

Claus O. Wilke, O'Reilly

download, pdf, epub

Effective visualization is the best way to communicate information from the increasingly large and complex datasets in the natural and social… Effective visualization is the best way to communicate information from the increasingly large and complex datasets in the natural and social sciences. But with the increasing power of visualization software today, scientists, engineers, and business analysts often have to navigate a bewildering array of visualization choices and options. This practical book takes you through many commonly encountered visualization problems, and it provides guidelines on how to turn large datasets into clear and compelling figures. What visualization type is best for the story you want to tell? How do you make informative figures that are visually pleasing? Author Claus O. Wilke teaches you the elements most critical to successful data visualization. Explore the basic concepts of color as a tool to highlight, distinguish, or represent a value Understand the importance of redundant coding to ensure you provide key information in multiple ways Use the book’s visualizations directory, a graphical guide to commonly used types of data visualizations Get extensive examples of good and bad figures Learn how to use figures in a document or report and how employ them effectively to tell a compelling story See more

Semantic Modeling for Data

Panos Alexopoulos, O'Reilly

download, pdf, epub

What value does semantic data modeling offer? As an information architect or data science professional, let’s say you have an abundance of the… What value does semantic data modeling offer? As an information architect or data science professional, let’s say you have an abundance of the right data and the technology to extract business gold—but you still fail. The reason? Bad data semantics. In this practical and comprehensive field guide, author Panos Alexopoulos takes you on an eye-opening journey through semantic data modeling as applied in the real world. You’ll learn how to master this craft to increase the usability and value of your data and applications. You’ll also explore the pitfalls to avoid and dilemmas to overcome for building high-quality and valuable semantic representations of data. Understand the fundamental concepts, phenomena, and processes related to semantic data modeling Examine the quirks and challenges of semantic data modeling and learn how to effectively leverage the available frameworks and tools Avoid mistakes and bad practices that can undermine your efforts to create good data models Learn about model development dilemmas, including representation, expressiveness and content, development, and governance Organize and execute semantic data initiatives in your organization, tackling technical, strategic, and organizational challenges See more

Data Science from Scratch

Joel Grus, O'Reilly

download, epub, pdf

2nd Edition

Fundamentals of Data Visualization

Claus O. Wilke, O'Reilly

download, pdf, epub

Effective visualization is the best way to communicate information from the increasingly large and complex datasets in the natural and social… Effective visualization is the best way to communicate information from the increasingly large and complex datasets in the natural and social sciences. But with the increasing power of visualization software today, scientists, engineers, and business analysts often have to navigate a bewildering array of visualization choices and options. This practical book takes you through many commonly encountered visualization problems, and it provides guidelines on how to turn large datasets into clear and compelling figures. What visualization type is best for the story you want to tell? How do you make informative figures that are visually pleasing? Author Claus O. Wilke teaches you the elements most critical to successful data visualization. Explore the basic concepts of color as a tool to highlight, distinguish, or represent a value Understand the importance of redundant coding to ensure you provide key information in multiple ways Use the book’s visualizations directory, a graphical guide to commonly used types of data visualizations Get extensive examples of good and bad figures Learn how to use figures in a document or report and how employ them effectively to tell a compelling story See more