Databasedesign og databaseteori

Traditional data architecture patterns are severely limited. To use these patterns, you have to ETL data into each tool--a cost-prohibitive process for making warehouse features available to all of your data. The lack of flexibility with these patterns requires you to lock into a set of priority tools and formats, which creates data silos and data drift. This practical book shows you a better way. Apache Iceberg provides the capabilities, performance, scalability, and savings that fulfill the promise of an open data lakehouse. By following the lessons in this book, you'll be able to achieve interactive, batch, machine learning, and streaming analytics with this high-performance open source format. Authors Tomer Shiran, Jason Hughes, and Alex Merced from Dremio show you how to get started with Iceberg. With this book, you'll learn: The architecture of Apache Iceberg tables What happens under the hood when you perform operations on Iceberg tables How to further optimize Iceberg tables for maximum performance How to use Iceberg with popular data engines such as Apache Spark, Apache Flink, and Dremio Discover why Apache Iceberg is a foundational technology for implementing an open data lakehouse.

The Align > Refine > Design series covers conceptual, logical, and physical data modeling (schema design and patterns) for leading technologies, combining proven data modeling practices with database-specific features to produce better applications. Read Elasticsearch Data Modeling and Schema Design if you are a data professional who needs to expand your modeling skills to include Elasticsearch or a technologist who knows Elasticsearch but needs to grow your schema design skills.The book's introduction and three chapters cover the Align, Refine, and Design approach. We include what the level does in the name by rebranding Conceptual, Logical, and Physical into Align, Refine, and Design. The introduction covers the three modeling characteristics of precise, minimal, and visual; the three model components of entities, relationships, and attributes (including keys); the three model levels of conceptual (align), logical (refine), and physical (design); and the three modeling perspectives of relational, dimensional, and query. Chapter 1, Align, is about agreeing on the common business vocabulary so everyone is aligned on terminology and general initiative scope. Chapter 2, Refine, is about capturing the business requirements. That is, refining our knowledge of the initiative to focus on what is essential. Chapter 3, Design, is about the technical requirements. That is, designing to accommodate our model's unique software and hardware needs.Align, Refine, and Design-that's the approach followed in this book and reinforced through an animal shelter case study. If you are interested in learning how to build multiple database solutions, read all the books in the Align > Refine > Design series. Since each book uses the same template, you can quickly skill up on additional database technologies.

Build and deploy your AI models successfully by exploring model governance, fairness, bias, and potential pitfallsPurchase of the print or Kindle book includes a free PDF eBookKey FeaturesLearn ethical AI principles, frameworks, and governanceUnderstand the concepts of fairness assessment and bias mitigationIntroduce explainable AI and transparency in your machine learning modelsBook DescriptionResponsible AI in the Enterprise is a comprehensive guide to implementing ethical, transparent, and compliant AI systems in an organization. With a focus on understanding key concepts of machine learning models, this book equips you with techniques and algorithms to tackle complex issues such as bias, fairness, and model governance.Throughout the book, you'll gain an understanding of FairLearn and InterpretML, along with Google What-If Tool, ML Fairness Gym, IBM AI 360 Fairness tool, and Aequitas. You'll uncover various aspects of responsible AI, including model interpretability, monitoring and management of model drift, and compliance recommendations. You'll gain practical insights into using AI governance tools to ensure fairness, bias mitigation, explainability, privacy compliance, and privacy in an enterprise setting. Additionally, you'll explore interpretability toolkits and fairness measures offered by major cloud AI providers like IBM, Amazon, Google, and Microsoft, while discovering how to use FairLearn for fairness assessment and bias mitigation. You'll also learn to build explainable models using global and local feature summary, local surrogate model, Shapley values, anchors, and counterfactual explanations.By the end of this book, you'll be well-equipped with tools and techniques to create transparent and accountable machine learning models.What you will learnUnderstand explainable AI fundamentals, underlying methods, and techniquesExplore model governance, including building explainable, auditable, and interpretable machine learning modelsUse partial dependence plot, global feature summary, individual condition expectation, and feature interactionBuild explainable models with global and local feature summary, and influence functions in practiceDesign and build explainable machine learning pipelines with transparencyDiscover Microsoft FairLearn and marketplace for different open-source explainable AI tools and cloud platformsWho this book is forThis book is for data scientists, machine learning engineers, AI practitioners, IT professionals, business stakeholders, and AI ethicists who are responsible for implementing AI models in their organizations.Table of ContentsA Primer on Explainable and Ethical AIAlgorithms Gone Wild - Bias's Greatest HitsOpening the Algorithmic BlackboxOperationalizing Model MonitoringModel Governance - Audit, and Compliance Standards & RecommendationsEnterprise Starter Kit for Fairness, Accountability and TransparencyInterpretability Toolkits and Fairness MeasuresFairness in AI System with Microsoft FairLearnFairness Assessment and Bias Mitigation with FairLearn and Responsible AI ToolboxFoundational Models and Azure OpenAI

Become a data wrangling expert and make well-informed decisions by effectively utilizing and analyzing raw unstructured data in a systematic mannerPurchase of the print or Kindle book includes a free PDF eBookKey Features:Implement query optimization during data wrangling using the SQL language with practical use casesMaster data cleaning, handle the date function and null value, and write subqueries and window functionsPractice self-assessment questions for SQL-based interviews and real-world case study roundsBook Description:The amount of data generated continues to grow rapidly, making it increasingly important for businesses to be able to wrangle this data and understand it quickly and efficiently. Although data wrangling can be challenging, with the right tools and techniques you can efficiently handle enormous amounts of unstructured data.The book starts by introducing you to the basics of SQL, focusing on the core principles and techniques of data wrangling. You'll then explore advanced SQL concepts like aggregate functions, window functions, CTEs, and subqueries that are very popular in the business world. The next set of chapters will walk you through different functions within SQL query that cause delays in data transformation and help you figure out the difference between a good query and bad one. You'll also learn how data wrangling and data science go hand in hand. The book is filled with datasets and practical examples to help you understand the concepts thoroughly, along with best practices to guide you at every stage of data wrangling.By the end of this book, you'll be equipped with essential techniques and best practices for data wrangling, and will predominantly learn how to use clean and standardized data models to make informed decisions, helping businesses avoid costly mistakes.What You Will Learn:Build time series models using data wranglingDiscover data wrangling best practices as well as tips and tricksFind out how to use subqueries, window functions, CTEs, and aggregate functionsHandle missing data, data types, date formats, and redundant dataBuild clean and efficient data models using data wrangling techniquesRemove outliers and calculate standard deviation to gauge the skewness of dataWho this book is for:This book is for data analysts looking for effective hands-on methods to manage and analyze large volumes of data using SQL. The book will also benefit data scientists, product managers, and basically any role wherein you are expected to gather data insights and develop business strategies using SQL as a language. If you are new to or have basic knowledge of SQL and databases and an understanding of data cleaning practices, this book will give you further insights into how you can apply SQL concepts to build clean, standardized data models for accurate analysis.

Revamp your data landscape and implement highly effective data pipelines in AWS with this hands-on guidePurchase of the print or Kindle book includes a free PDF eBookKey Features:Execute extract, transform, and load (ETL) tasks on data lakes, data warehouses, and databasesImplement effective Pandas data operation with data wranglerIntegrate pipelines with AWS data servicesBook Description:Data wrangling is the process of cleaning, transforming, and organizing raw, messy, or unstructured data into a structured format. It involves processes such as data cleaning, data integration, data transformation, and data enrichment to ensure that the data is accurate, consistent, and suitable for analysis. Data Wrangling on AWS equips you with the knowledge to reap the full potential of AWS data wrangling tools.First, you'll be introduced to data wrangling on AWS and will be familiarized with data wrangling services available in AWS. You'll understand how to work with AWS Glue DataBrew, AWS data wrangler, and AWS Sagemaker. Next, you'll discover other AWS services like Amazon S3, Redshift, Athena, and Quicksight. Additionally, you'll explore advanced topics such as performing Pandas data operation with AWS data wrangler, optimizing ML data with AWS SageMaker, building the data warehouse with Glue DataBrew, along with security and monitoring aspects.By the end of this book, you'll be well-equipped to perform data wrangling using AWS services.What You Will Learn:Explore how to write simple to complex transformations using AWS data wranglerUse abstracted functions to extract and load data from and into AWS datastoresConfigure AWS Glue DataBrew for data wranglingDevelop data pipelines using AWS data wranglerIntegrate AWS security features into Data Wrangler using identity and access management (IAM)Optimize your data with AWS SageMakerWho this book is for:This book is for data engineers, data scientists, and business data analysts looking to explore the capabilities, tools, and services of data wrangling on AWS for their ETL tasks. Basic knowledge of Python, Pandas, and a familiarity with AWS tools such as AWS Glue, Amazon Athena is required to get the most out of this book.

This book covers various design problems related to the VLSI modulation circuits and their applications in the areas of signal processing, data conversion, and power management. It provides a good reference for learning through problem solving of PWM modulation, sigma-delta modulation, and special VLSI circuit techniques, such as class-D amplifier circuits, lock-in amplifier circuits, switched-capacitor charge pump circuits, DC/DC converter circuits, chopper circuits, mixer circuit, and fractional-N PLL circuits.

Use easy-to-apply patterns in SQL and Python to adopt modern analytics engineering to build agile platforms with dbt that are well-tested and simple to extend and runPurchase of the print or Kindle book includes a free PDF eBookKey Features- Build a solid dbt base and learn data modeling and the modern data stack to become an analytics engineer- Build automated and reliable pipelines to deploy, test, run, and monitor ELTs with dbt Cloud- Guided dbt + Snowflake project to build a pattern-based architecture that delivers reliable datasetsBook Descriptiondbt Cloud helps professional analytics engineers automate the application of powerful and proven patterns to transform data from ingestion to delivery, enabling real DataOps. This book begins by introducing you to dbt and its role in the data stack, along with how it uses simple SQL to build your data platform, helping you and your team work better together. You'll find out how to leverage data modeling, data quality, master data management, and more to build a simple-to-understand and future-proof solution. As you advance, you'll explore the modern data stack, understand how data-related careers are changing, and see how dbt enables this transition into the emerging role of an analytics engineer. The chapters help you build a sample project using the free version of dbt Cloud, Snowflake, and GitHub to create a professional DevOps setup with continuous integration, automated deployment, ELT run, scheduling, and monitoring, solving practical cases you encounter in your daily work. By the end of this dbt book, you'll be able to build an end-to-end pragmatic data platform by ingesting data exported from your source systems, coding the needed transformations, including master data and the desired business rules, and building well-formed dimensional models or wide tables that'll enable you to build reports with the BI tool of your choice.What you will learn- Create a dbt Cloud account and understand the ELT workflow- Combine Snowflake and dbt for building modern data engineering pipelines- Use SQL to transform raw data into usable data, and test its accuracy- Write dbt macros and use Jinja to apply software engineering principles- Test data and transformations to ensure reliability and data quality- Build a lightweight pragmatic data platform using proven patterns- Write easy-to-maintain idempotent code using dbt materializationWho this book is forThis book is for data engineers, analytics engineers, BI professionals, and data analysts who want to learn how to build simple, futureproof, and maintainable data platforms in an agile way. Project managers, data team managers, and decision makers looking to understand the importance of building a data platform and foster a culture of high-performing data teams will also find this book useful. Basic knowledge of SQL and data modeling will help you get the most out of the many layers of this book. The book also includes primers on many data-related subjects to help juniors get started.Table of Contents- Basics of SQL to transform data- Setting up your dbt Cloud development environment- Data modelling for data engineering- Analytics Engineering as the New Core of Data Engineering- Transforming data with dbt- Writing Maintainable Code- Working with Dimensional Data- Delivering Consistency In Your Code- Delivering Reliability In Your Data- Agile development- Collaboration- Deployment, Execution and Documentation Automation- Moving beyond basics- Enhancing Software Quality- Patterns for frequent use cases

"This practical book shows you how to ensure that the data your organization relies on is trustworthy ... [and] explain[s] how you can use automated data quality monitoring to cover all your tables efficiently, proactively alert on issues, and resolve problems immediately"--

Unleash the full potential of Power BI with the integration of AI and machine learning techniques using OpenAIPurchase of the print or Kindle book includes a free PDF eBookKey Features:Take flight with Power BI machine learning and OpenAI using hands-on examples from the FAA airline dataUnlock the full potential of Power BI for advanced analytics using OpenAIDesign stunning data presentations, seamless integration of machine learning tools and technologies with OpenAIBook Description:Microsoft Power BI is the ultimate solution for businesses looking to make data-driven decisions and unlock the full potential of their data. Unleashing Your Data with Power BI Machine Learning and OpenAI is designed for data scientists and BI professionals seeking to improve their existing solutions and workloads using AI.The book explains the intricacies of the subject by using a workshop-style data story for data ingestion, data modeling, analytics, and predictive analytics with Power BI machine learning. Along the way,you'll learn about AI features, AI visuals, R/Python integration, and OpenAI integration.The workshop-style content allows you to practice all your learnings in real-life challenges and gain hands-on experience. Additionally, you'll gain an understanding of AI/ML, step by step, with replicable examples and references. From enhancing data visualizations to building SaaS Power BI ML models, and integrating Azure OpenAI, this book will help you unlock new capabilities in Power BI.By the end of this book, you'll be well-equipped to build ML models in Power BI, plan projects for both BI and ML, understand R/Python visuals with Power BI, and introduce OpenAI to enhance your analytics solutions.What You Will Learn:Discover best practices for implementing AI and ML capabilities in Power BI along with integration of OpenAI into the solutionUnderstand how to integrate OpenAI and cognitive services into Power BIExplore how to build a SaaS auto ML model within Power BIGain an understanding of R/Python integration with Power BIEnhance data visualizations for ML feature discoveryDiscover how to improve existing solutions and workloads using AI and ML capabilities in Power BI with OpenAIAcquire tips and tricks for successfully using AI and ML capabilities in Power BI along with integration of OpenAI into the solutionWho this book is for:This book is for data science and BI professionals looking to expand their skill sets into Power BI machine learning and OpenAI. This book is also useful for data scientists, data analysts, and IT professionals who want to learn how to incorporate OpenAI into Power BI for advanced experience.

Discover how Snowflake's unique objects and features can be used to leverage universal modeling techniques through real-world examples and SQL recipesPurchase of the print or Kindle book includes a free PDF eBookKey FeaturesLearn core modeling techniques tied to practical examples using native Snowflake architectureAdopt a universal modeling language to communicate business value to functional teamsGo beyond physical modeling with SQL recipes to transform and shape your Snowflake dataBook DescriptionThe Snowflake Data Cloud is one of the fastest-growing platforms for data warehousing and application workloads. Snowflake's scalable, cloud-native architecture and expansive set of features and objects enables you to deliver data solutions quicker than ever before. Yet, we must ensure that these solutions are developed using recommended design patterns and accompanied by documentation that's easily accessible to everyone in the organization. This book will help you get familiar with simple and practical data modeling frameworks that accelerate agile design and evolve with the project from concept to code. These universal principles have helped guide database design for decades, and this book pairs them with unique Snowflake-native objects and examples like never before - giving you a two-for-one crash course in theory as well as direct application. By the end of this Snowflake book, you'll have learned how to leverage Snowflake's innovative features, such as time travel, zero-copy cloning, and change-data-capture, to create cost-effective, efficient designs through time-tested modeling principles that are easily digestible when coupled with real-world examples.What you will learnDiscover the time-saving features and applications of data modelingExplore Snowflake's cloud-native architecture and featuresUnderstand and apply modeling concepts, techniques, and language using Snowflake objectsMaster modeling concepts such as normalization and slowly changing dimensionsGet comfortable reading and transforming semi-structured dataWork directly with pre-built recipes and examplesApply modeling frameworks from Star to Data VaultWho this book is forThis book is for developers working with SQL who are looking to build a strong foundation in modeling best practices and gain an understanding of where they can be effectively applied to save time and effort. Whether you're an ace in SQL logic or starting out in database design, this book will equip you with the practical foundations of data modeling to guide you on your data journey with Snowflake. Developers who've recently discovered Snowflake will be able to uncover its core features and learn to incorporate them into universal modeling frameworks.Table of ContentsUnlocking the Power of ModelingAn Introduction to the Four Modeling TypesMastering Snowflake's ArchitectureMastering Snowflake ObjectsSpeaking Modeling through Snowflake ObjectsSeeing Snowflake's Architecture through Modeling NotationPutting Conceptual Modeling into PracticePutting Logical Modeling into PracticeDatabase NormalizationDatabase Naming and StructurePutting Physical Modeling into PracticePutting Transformational Modeling into PracticeModeling Slowly Changing DimensionsModeling Facts for Rapid AnalysisModeling Semi-Structured DataModeling HierarchiesScaling Data Models through Modern TechniquesAppendix

The Align > Refine > Design series covers conceptual, logical, and physical data modeling (schema design and patterns) for leading technologies, combining proven data modeling practices with database-specific features to produce better applications. Read MongoDB Data Modeling and Schema Design if you are a data professional who needs to expand your modeling skills to include MongoDB or a technologist who knows MongoDB but needs to grow your schema design skills.The book's introduction and three chapters cover the Align, Refine, and Design approach. We include what the level does in the name by rebranding Conceptual, Logical, and Physical into Align, Refine, and Design. The introduction covers the three modeling characteristics of precise, minimal, and visual; the three model components of entities, relationships, and attributes (including keys); the three model levels of conceptual (align), logical (refine), and physical (design); and the three modeling perspectives of relational, dimensional, and query. Chapter 1, Align, is about agreeing on the common business vocabulary so everyone is aligned on terminology and general initiative scope. Chapter 2, Refine, is about capturing the business requirements. That is, refining our knowledge of the initiative to focus on what is essential. Chapter 3, Design, is about the technical requirements. That is, designing to accommodate our model's unique software and hardware needs.Align, Refine, and Design-that's the approach followed in this book and reinforced through an animal shelter case study. If you are interested in learning how to build multiple database solutions, read all the books in the Align > Refine > Design series. Since each book uses the same template, you can quickly skill up on additional database technologies.

The Align > Refine > Design series covers conceptual, logical, and physical data modeling (schema design and patterns) for leading technologies, combining proven data modeling practices with database-specific features to produce better applications. Read Neo4j Data Modeling if you are a data professional who needs to expand your modeling skills to include Neo4j or a technologist who knows Neo4j but needs to grow your schema design skills.The book's introduction and three chapters cover the Align, Refine, and Design approach. We include what the level does in the name by rebranding Conceptual, Logical, and Physical into Align, Refine, and Design. The introduction covers the three modeling characteristics of precise, minimal, and visual; the three model components of entities, relationships, and attributes (including keys); the three model levels of conceptual (align), logical (refine), and physical (design); and the three modeling perspectives of relational, dimensional, and query. Chapter 1, Align, is about agreeing on the common business vocabulary so everyone is aligned on terminology and general initiative scope. Chapter 2, Refine, is about capturing the business requirements. That is, refining our knowledge of the initiative to focus on what is essential. Chapter 3, Design, is about the technical requirements. That is, designing to accommodate our model's unique software and hardware needs.Align, Refine, and Design-that's the approach followed in this book and reinforced through an animal shelter case study. If you are interested in learning how to build multiple database solutions, read all the books in the Align > Refine > Design series. Since each book uses the same template, you can quickly skill up on additional database technologies.

What does platform thinking mean? It is the ability to put platform-based mechanisms at the core of digital business transformations in business.Digital platforms like Uber, Spotify, Airbnb, Booking.com, and many others completely re-shaped the daily life of millions of users. However, many innovation leaders struggle to conceive the significance of their impact. Platform-based business models are not just the real of geeks' startups, digital services, and Unicorns.This book shows there that their potential in creating value is higher than suspected.Even though platform ecosystems are complex, readers will take on a journey to become platform thinkers. Their eyes will be trained to look beyond what's visible and start reading the platform world around them. Then, they will be guided into a step-by-step process and learn how to write a new platform model from scratch.This book is the result of a decade of research. It offers both a framework and practical instruments to champion digital transformation in any organization. It is specially intended for those who are interested in the glittering platform world and are still trying to figure it out.

Have you ever wondered how the technology that is shaping our world works?From mainstream technologies such as computing and the Internet, to emerging technologies such as artificial intelligence and quantum computing, it is no secret that the world is heavily influenced by technology. Yet, the technology that we depend upon is often not fully understood by those not specialized in the field. You are going to learn:What the internet of things really is (and what it is not)How the internet of things is going to affect our livesHow the iot & smart homes will change our householdsWhat iot means for corporations & their business modelsThe main benefits if iot (this will surprise you)Current shortcomings in iot to watch out forThis book is an essential resource for engineers, developers, and data scientists working in the field of industrial IoT. Whether you're a beginner or an experienced practitioner, this book will provide you with the knowledge and skills you need to build effective predictive maintenance systems for industrial IoT applications

Der Standardkommentar zum Haftungsrecht des Straßenverkehrs erscheint in der Neuauflage in der bewährten systematischen Form, die Handbuch und Kommentar verbindet. Das Werk behandelt sämtliche Tatbestände einer verschuldensunabhängigen Haftung, erläutert die Haftung aus unerlaubter Handlung sowie alle sonstigen Haftungstatbestände und enthält alle Regelungskomplexe, aus denen sich eine Beschränkung oder ein Ausschluss der Haftung herleiten lässt. Jeweils gesonderte Abschnitte sind dem Ersatz des Sachschadens und des Personenschadens in all ihren Ausprägungen gewidmet. Ausführlich behandelt wird der Forderungsübergang auf Privatversicherer, Sozialversicherungs- und Versorgungsträger. Erläuterungen zu den prozessualen, insbesondere beweisrechtlichen Fragen der Verkehrsunfallhaftung schließen das Werk ab. In der Neuauflage wird die Rechtsprechung zum Schadensersatzrecht weiter ausdifferenziert, das neue Versicherungsvertragsgesetz eingearbeitet und der verstärkte Einfluss des internationalen und europäischen Rechts auf das Verkehrshaftungsrecht berücksichtigt.

Take your Power BI reports to the next level by learning various data modeling techniques and leveraging the latest features of Power BI effectivelyPurchase of the print or Kindle book includes a free eBook in PDF format.Key Features:Get an understanding of data modeling techniques using Power BI with this up-to-date guideLearn how to define the relationships between data sets to extract valuable insightsExplore best practices for data preparation and modeling and build optimal data models to solve a wide variety of real-world business challengesBook Description:This book is a comprehensive guide to understanding the ins and outs of data modeling and how to create full-fledged data models using Power BI confidently.In this new, fully updated edition, you'll learn how to connect data from multiple sources, understand data, define and manage relationships between data, and shape data models to gain deep and detailed insights about your organization. As you advance through the chapters, the book will demonstrate how to prepare efficient data models in the Power Query Editor and use simpler DAX code with new data modeling features. You'll explore how to use the various data modeling and navigation techniques and perform custom calculations using the modeling features with the help of real-world examples. Finally, you'll learn how to use some new and advanced modeling features to enhance your data models to carry out a wide variety of complex tasks. Additionally, you'll learn valuable best practices and explore common data modeling complications and the solutions to supercharge the process of creating a data model in Power BI and build better-performing data models.By the end of this Power BI book, you'll have gained the skills you need to structure data coming from multiple sources in different ways to create optimized data models that support high-performing reports and data analytics.What You Will Learn:Implement virtual tables and time intelligence functionalities in DAX to build a powerful modelIdentify Dimension and Fact tables and implement them in Power Query EditorDeal with advanced data preparation scenarios while building Star SchemaDiscover different hierarchies and their common pitfallsUnderstand complex data models and how to decrease the level of model complexity with different approachesLearn advanced data modeling techniques such as calculation groups, aggregations, incremental refresh, RLS/OLS, and moreGet well-versed with datamarts and dataflows in PowerBIWho this book is for:This MS Power BI book is for BI users, data analysts, and analysis developers who want to become well-versed with data modeling techniques to make the most of Power BI. Basic working knowledge of Power BI and the Star Schema functionality are required to help you to understand the concepts covered in this book.

This concise but comprehensive book engagingly summarizes both the broad themes and specific tenets of First Amendment law, and the strongest arguments for and against protecting controversial speech such as hate speech and disinformation. It explains the many speech-protective legal rules that emerged during the Civil Rights era, demonstrating how essential free speech is for other human rights.

"The Art of Randomness teaches readers to harness the power of randomness (and Python code) to solve real-world problems in programming, science, and art through hands-on experiments-from simulating evolution to encrypting messages to making machine-learning algorithms. Each chapter describes how randomness plays into the given topic area, then proceeds to demonstrate its problem-solving role with hands-on experiments to work through using Python code"--

Enhance your SQL query writing skills to provide greater business value using advanced techniques such as common table expressions, window functions, and JSONPurchase of the print or Kindle book includes a free PDF eBookKey Features:Examine query design and performance using query plans and indexesSolve business problems using advanced techniques such as common table expressions and window functionsUse SQL in modern data platform solutions with JSON and Jupyter notebooksBook Description:SQL has been the de facto standard when interacting with databases for decades and shows no signs of going away. Through the years, report developers or data wranglers have had to learn SQL on the fly to meet the business needs, so if you are someone who needs to write queries, SQL Query Design and Pattern Best Practices is for you.This book will guide you through making efficient SQL queries by reducing set sizes for effective results. You'll learn how to format your results to make them easier to consume at their destination. From there, the book will take you through solving complex business problems using more advanced techniques, such as common table expressions and window functions, and advance to uncovering issues resulting from security in the underlying dataset. Armed with this knowledge, you'll have a foundation for building queries and be ready to shift focus to using tools, such as query plans and indexes, to optimize those queries. The book will go over the modern data estate, which includes data lakes and JSON data, and wrap up with a brief on how to use Jupyter notebooks in your SQL journey.By the end of this SQL book, you'll be able to make efficient SQL queries that will improve your report writing and the overall SQL experience.What You Will Learn:Build efficient queries by reducing the data being returnedManipulate your data and format it for easier consumptionForm common table expressions and window functions to solve complex business issuesUnderstand the impact of SQL security on your resultsUnderstand and use query plans to optimize your queriesUnderstand the impact of indexes on your query performance and designWork with data lake data and JSON in SQL queriesOrganize your queries using Jupyter notebooksWho this book is for:This book is for SQL developers, data analysts, report writers, data scientists, and other data gatherers looking to expand their skills for complex querying as well as for building more efficient and performant queries.For those new to SQL, this book can help you accelerate your learning and keep you from making common mistakes.

Hamilton: The Energetic Founder provides a brief introduction to the life, work, and legacies of Alexander Hamilton. R. B. Bernstein explores Hamilton's role in revolution, politics, law, constitutionalism, economics, diplomacy, and war, as well as his views on honor and duelling. This thorough history reveals that Hamilton was one of the key founding fathers of the United States.

Take your data wrangling skills to the next level by gaining a deep understanding of tidyverse libraries and effectively prepare your data for impressive analysisPurchase of the print or Kindle book includes a free PDF eBookKey Features:Explore state-of-the-art libraries for data wrangling in R and learn to prepare your data for analysisFind out how to work with different data types such as strings, numbers, date, and timeBuild your first model and visualize data with ease through advanced plot types and with ggplot2Book Description:In this information era, where large volumes of data are being generated every day, companies want to get a better grip on it to perform more efficiently than before. This is where skillful data analysts and data scientists come into play, wrangling and exploring data to generate valuable business insights. In order to do that, you'll need plenty of tools that enable you to extract the most useful knowledge from data.Data Wrangling with R will help you to gain a deep understanding of ways to wrangle and prepare datasets for exploration, analysis, and modeling. This data book enables you to get your data ready for more optimized analyses, develop your first data model, and perform effective data visualization.The book begins by teaching you how to load and explore datasets. Then, you'll get to grips with the modern concepts and tools of data wrangling. As data wrangling and visualization are intrinsically connected, you'll go over best practices to plot data and extract insights from it. The chapters are designed in a way to help you learn all about modeling, as you will go through the construction of a data science project from end to end, and become familiar with the built-in RStudio, including an application built with Shiny dashboards.By the end of this book, you'll have learned how to create your first data model and build an application with Shiny in R.What You Will Learn:Discover how to load datasets and explore data in RWork with different types of variables in datasetsCreate basic and advanced visualizationsFind out how to build your first data modelCreate graphics using ggplot2 in a step-by-step way in Microsoft Power BIGet familiarized with building an application in R with ShinyWho this book is for:If you are a professional data analyst, data scientist, or beginner who wants to learn more about data wrangling, this book is for you. Familiarity with the basic concepts of R programming or any other object-oriented programming language will help you to grasp the concepts taught in this book. Data analysts looking to improve their data manipulation and visualization skills will also benefit immensely from this book.

A bold, groundbreaking argument by a world-renowned expert that unless we treat free speech as the fundamental human right, there can be no others.What are human rights? Are they laid out definitively in the UN’s Universal Declaration of Human Rights or the US Bill of Rights? Are they items on a checklist—dignity, justice, progress, standard of living, health care, housing? In The Most Human Right, Eric Heinze explains why global human rights systems have failed. International organizations constantly report on how governments manage human goods, such as fair trials, humane conditions of detention, healthcare, or housing. But to appease autocratic regimes, experts have ignored the primacy of free speech. Heinze argues that goods become rights only when citizens can claim them publicly and fearlessly: free speech is the fundamental right, without which the very concept of a “right” makes no sense.  Heinze argues that throughout history countless systems of justice have promised human goods. What, then, makes human rights different? What must human rights have that other systems have lacked? Heinze revisits the origins of the concept, exploring what it means for a nation to protect human rights, and what a citizen needs in order to pursue them. He explains how free speech distinguishes human rights from other ideas about justice, past and present.

Create interpretable AI models for transparent and explainable anomaly detection with this hands-on guidePurchase of the print or Kindle book includes a free PDF eBookKey Features:Build auditable XAI models for replicability and regulatory complianceDerive critical insights from transparent anomaly detection modelsStrike the right balance between model accuracy and interpretabilityBook Description:Despite promising advances, the opaque nature of deep learning models makes it difficult to interpret them, which is a drawback in terms of their practical deployment and regulatory compliance.Deep Learning and XAI Techniques for Anomaly Detection shows you state-of-the-art methods that'll help you to understand and address these challenges. By leveraging the Explainable AI (XAI) and deep learning techniques described in this book, you'll discover how to successfully extract business-critical insights while ensuring fair and ethical analysis.This practical guide will provide you with tools and best practices to achieve transparency and interpretability with deep learning models, ultimately establishing trust in your anomaly detection applications. Throughout the chapters, you'll get equipped with XAI and anomaly detection knowledge that'll enable you to embark on a series of real-world projects. Whether you are building computer vision, natural language processing, or time series models, you'll learn how to quantify and assess their explainability.By the end of this deep learning book, you'll be able to build a variety of deep learning XAI models and perform validation to assess their explainability.What You Will Learn:Explore deep learning frameworks for anomaly detectionMitigate bias to ensure unbiased and ethical analysisIncrease your privacy and regulatory compliance awarenessBuild deep learning anomaly detectors in several domainsCompare intrinsic and post hoc explainability methodsExamine backpropagation and perturbation methodsConduct model-agnostic and model-specific explainability techniquesEvaluate the explainability of your deep learning modelsWho this book is for:This book is for anyone who aspires to explore explainable deep learning anomaly detection, tenured data scientists or ML practitioners looking for Explainable AI (XAI) best practices, or business leaders looking to make decisions on trade-off between performance and interpretability of anomaly detection applications. A basic understanding of deep learning and anomaly detection-related topics using Python is recommended to get the most out of this book.

Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible.Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. Youll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what youve learned along the way.Youll learn how to:Wrangletransform your datasets into a form convenient for analysisProgramlearn powerful R tools for solving data problems with greater clarity and easeExploreexamine your data, generate hypotheses, and quickly test themModelprovide a low-dimensional summary that captures true "e;signals"e; in your datasetCommunicatelearn R Markdown for integrating prose, code, and results

Do you want to know all about data science? Do you really want to understand why it is the future in all the most demanding job?The demand for data science is increasing rapidly. The basic reason behind this is the massive boom in the data generated and retained by the companies. Also known as big data, data scientists make the best use of this available information and figure out their best use.The team of data scientists also helps in generating a good amount of analytics from the information available. This information brings clarity to people on how to interact with the web and are the foundations on which most of the critical business strategies rest. This book will discuss the following topics:What is Data Science?What Exactly Does a Data Scientist Do?A Look at What Data Analytics Is All AboutRegression AnalysisHow to work with Database QueryingA Look at Artificial IntelligenceAnd much more!The book has been structured with easy-to-understand sections to help you learn everything you need to know about data science. In this book you will learn about the prerequisites of data science and the skills you need to become a data scientist. So, what are you waiting for? Grab your copy of this comprehensive guide now!

Fully build and productionize end-to-end machine learning solutions using Azure Machine Learning ServiceKey Features:Automate complete machine learning solutions using Microsoft AzureUnderstand how to productionize machine learning modelsGet to grips with monitoring, MLOps, deep learning, distributed training, and reinforcement learningBook Description:Data scientists working on productionizing machine learning (ML) workloads face a breadth of challenges at every step owing to the countless factors involved in getting ML models deployed and running. This book offers solutions to common issues, detailed explanations of essential concepts, and step-by-step instructions to productionize ML workloads using the Azure Machine Learning service. You'll see how data scientists and ML engineers working with Microsoft Azure can train and deploy ML models at scale by putting their knowledge to work with this practical guide.Throughout the book, you'll learn how to train, register, and productionize ML models by making use of the power of the Azure Machine Learning service. You'll get to grips with scoring models in real time and batch, explaining models to earn business trust, mitigating model bias, and developing solutions using an MLOps framework.By the end of this Azure Machine Learning book, you'll be ready to build and deploy end-to-end ML solutions into a production system using the Azure Machine Learning service for real-time scenarios.What You Will Learn:Train ML models in the Azure Machine Learning serviceBuild end-to-end ML pipelinesHost ML models on real-time scoring endpointsMitigate bias in ML modelsGet the hang of using an MLOps framework to productionize modelsSimplify ML model explainability using the Azure Machine Learning service and Azure InterpretWho this book is for:Machine learning engineers and data scientists who want to move to ML engineering roles will find this AMLS book useful. Familiarity with the Azure ecosystem will assist with understanding the concepts covered.

Use modern Python libraries such as pandas, NumPy, and scikit-learn and popular machine learning and deep learning methods to solve financial modeling problemsPurchase of the print or Kindle book includes a free eBook in the PDF formatKey FeaturesExplore unique recipes for financial data processing and analysis with PythonApply classical and machine learning approaches to financial time series analysisCalculate various technical analysis indicators and backtest trading strategiesBook DescriptionPython is one of the most popular programming languages in the financial industry, with a huge collection of accompanying libraries. In this new edition of the Python for Finance Cookbook, you will explore classical quantitative finance approaches to data modeling, such as GARCH, CAPM, factor models, as well as modern machine learning and deep learning solutions.You will use popular Python libraries that, in a few lines of code, provide the means to quickly process, analyze, and draw conclusions from financial data. In this new edition, more emphasis was put on exploratory data analysis to help you visualize and better understand financial data. While doing so, you will also learn how to use Streamlit to create elegant, interactive web applications to present the results of technical analyses.Using the recipes in this book, you will become proficient in financial data analysis, be it for personal or professional projects. You will also understand which potential issues to expect with such analyses and, more importantly, how to overcome them.What you will learnPreprocess, analyze, and visualize financial dataExplore time series modeling with statistical (exponential smoothing, ARIMA) and machine learning modelsUncover advanced time series forecasting algorithms such as Meta's ProphetUse Monte Carlo simulations for derivatives valuation and risk assessmentExplore volatility modeling using univariate and multivariate GARCH modelsInvestigate various approaches to asset allocationLearn how to approach ML-projects using an example of default predictionExplore modern deep learning models such as Google's TabNet, Amazon's DeepAR and NeuralProphetWho this book is forThis book is intended for financial analysts, data analysts and scientists, and Python developers with a familiarity with financial concepts. You'll learn how to correctly use advanced approaches for analysis, avoid potential pitfalls and common mistakes, and reach correct conclusions for a broad range of finance problems.Working knowledge of the Python programming language (particularly libraries such as pandas and NumPy) is necessary.Table of ContentsAcquiring Financial DataData PreprocessingVisualizing Financial Time SeriesExploring Financial Time Series DataTechnical Analysis and Building Interactive DashboardsTime Series Analysis and ForecastingMachine Learning-Based Approaches to Time Series ForecastingMulti-Factor ModelsModelling Volatility with GARCH Class ModelsMonte Carlo Simulations in FinanceAsset AllocationBacktesting Trading StrategiesApplied Machine Learning: Identifying Credit DefaultAdvanced Concepts for Machine Learning ProjectsDeep Learning in Finance