Learn the fundamentals of data science with Python by analyzing real datasets and solving problems using pandas Key Features * Learn how to apply data retrieval, transformation, visualization, and modeling techniques using pandas * Become highly efficient in unlocking deeper insights from your data, including databases, web data, and more * Build your experience and confidence with hands-on exercises and activities Book Description The Pandas Workshop will teach you how to be more productive with data and generate real business insights to inform your decision-making. You will be guided through real-world data science problems and shown how to apply key techniques in the context of realistic examples and exercises. Engaging activities will then challenge you to apply your new skills in a way that prepares you for real data science projects. You'll see how experienced data scientists tackle a wide range of problems using data analysis with pandas. Unlike other Python books, which focus on theory and spend too long on dry, technical explanations, this workshop is designed to quickly get you to write clean code and build your understanding through hands-on practice. As you work through this Python pandas book, you'll tackle various real-world scenarios, such as using an air quality dataset to understand the pattern of nitrogen dioxide emissions in a city, as well as analyzing transportation data to improve bus transportation services. By the end of this data analytics book, you'll have the knowledge, skills, and confidence you need to solve your own challenging data science problems with pandas. What you will learn * Access and load data from different sources using pandas * Work with a range of data types and structures to understand your data * Perform data transformation to prepare it for analysis * Use Matplotlib for data visualization to create a variety of plots * Create data models to find relationships and test hypotheses * Manipulate time-series data to perform date-time calculations * Optimize your code to ensure more efficient business data analysis Who This Book Is For This data analysis book is for anyone with prior experience working with the Python programming language who wants to learn the fundamentals of data analysis with pandas. Previous knowledge of pandas is not necessary.

In dem Standardwerk der Informationsverarbeitung werden nicht nur die elektroakustischen und nachrichtentechnischen Grundlagen dargestellt, auch die Sprache als menschliche Kommunikationsform wird aus linguistischer und physiologischer Perspektive beschrieben. Berucksichtigt wird dabei neben der Theorie stets die Anwendung auf dem neuesten Stand der Technik. Die 2.Auflage bietet neue Abschnitte zu den Grundzugen der Signalanalyse und zu Sprachdialogsystemen. Audiobeispiele und multimediale Vortragselemente zum Download auf extras.springer.com.

Quickly build and deploy massive data pipelines and improve productivity using Azure Databricks Key Features Get to grips with the distributed training and deployment of machine learning and deep learning models Learn how ETLs are integrated with Azure Data Factory and Delta Lake Explore deep learning and machine learning models in a distributed computing infrastructure Book DescriptionMicrosoft Azure Databricks helps you to harness the power of distributed computing and apply it to create robust data pipelines, along with training and deploying machine learning and deep learning models. Databricks' advanced features enable developers to process, transform, and explore data. Distributed Data Systems with Azure Databricks will help you to put your knowledge of Databricks to work to create big data pipelines. The book provides a hands-on approach to implementing Azure Databricks and its associated methodologies that will make you productive in no time. Complete with detailed explanations of essential concepts, practical examples, and self-assessment questions, you'll begin with a quick introduction to Databricks core functionalities, before performing distributed model training and inference using TensorFlow and Spark MLlib. As you advance, you'll explore MLflow Model Serving on Azure Databricks and implement distributed training pipelines using HorovodRunner in Databricks. Finally, you'll discover how to transform, use, and obtain insights from massive amounts of data to train predictive models and create entire fully working data pipelines. By the end of this MS Azure book, you'll have gained a solid understanding of how to work with Databricks to create and manage an entire big data pipeline. What you will learn Create ETLs for big data in Azure Databricks Train, manage, and deploy machine learning and deep learning models Integrate Databricks with Azure Data Factory for extract, transform, load (ETL) pipeline creation Discover how to use Horovod for distributed deep learning Find out how to use Delta Engine to query and process data from Delta Lake Understand how to use Data Factory in combination with Databricks Use Structured Streaming in a production-like environment Who this book is forThis book is for software engineers, machine learning engineers, data scientists, and data engineers who are new to Azure Databricks and want to build high-quality data pipelines without worrying about infrastructure. Knowledge of Azure Databricks basics is required to learn the concepts covered in this book more effectively. A basic understanding of machine learning concepts and beginner-level Python programming knowledge is also recommended.

Oberlegungen iiber die Automatisierung der Verwaltungstatigkeit in indu striellen Unternehmungen lie en vermuten, daB die verschiedenartigen ein zelnen Arbeiten auf eine gleichartige Grundfunktion zuriickgefiihrt werden konnen. Zu dieser Fragestellung gab Herr Professor Dr. Dr. Beste dankens werterweise die Anregung, die Untersuchung in der vorliegenden allgemei nen Fassung durchzufiihren. Der Verfasser hat sich bemiiht, in mehrjahriger praktischer Tatigkeit die dargestellten theoretischen Erkenntnisse aus den in der industriellen Praxis vorgefundenen Gegebenheiten heraus zu entwickeln. Bei der Durchsprache einzelner Probleme erhielt der Verfasser dariiber hin aus von Herrn Professor Dr. Dr. Beste und Herrn Professor Dr. von Kortz fleisch wertvolle Anregungen, fUr die er auch an dieser Stelle seinen beson deren Dank aussprechen mochte. Die Arbeit wurde im Rahmen des Industrieseminars der Universitat Koln angefertigt. Essen, den 1. November 1962 INHALT Seite 5 Vorwort I. Begriffe und Bereich einer betriebswirtschaftlichen Untersuchung uber die Information in der industriellen Unternehmung . . . . . . . 9 A. Unterschiedliche Produktivit1it bei der Materialverarbeitung und der Informationsverarbeitung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 B. Zum Wesen der Information als Tatigkeitsgegenstand in der industriellen Unternehmung und zur Kommunikation . . . . . . . 12 C. Bereich und Ziele der Untersuchung . . . . . . . . . . . . . . . . . . . . . . . . 14 II. Die Grundbausteine der Information und ihrer Verarbeitung . . . 17 A. Die elementare Struktur der Information . . . . . . . . . . . . . . . . . . . . 17 1. Der formale Gehalt der Information . . . . . . . . . . . . . . . . . . . . . . . 18 2. Der informative Gehalt cler Information . . . . . . . . . . . . . . . . . . . . 23 3. Die betriebswirtschaftliche MaBeinheit der Information . . . . . . . 26 B. Die elementaren Kommunikationswege . . . . . . . . . . . . . . . . . . . . . . 28 1. Die vertikale und die horizontale Anordnung der Kommunikationswege . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 2. Das geschlossene Kommunikationssystem als grundsatzliche or- nisatorische Struktur . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 C. Die elementaren Verarbeitungsvorgange . . . . . . . . . . . . . . . . . . . . ."

Die Absicht, ein Buch iiber Programmieren von Ziffernrechenanlagen zu schreiben, entstand auf Grund einer Vorlesung gleichen Titels, die ich seit nunmehr sieben Jahren an der Technischen Hochschule Wien halte. Ich hatte dabei bemerkt, daB das Interesse fiir die Programmierung von Ziffernrechnern immer weitere Kreise zieht und daB es moglich ist, dieses Interesse aus einem einheitlichen Gesichtswinkel zu befriedigen. Der Zugang zur Kenntnis des Programmierens erfolgt heute iiblicher- weise mit Hille der Mathematischen Verfahrenstechnik oder von seiten der Administrativen Automation, oder schlieBlich iiber die mit tech- nischen Einzelheiten vermengte Beschreibung spezieller Maschinen. Ich bin nun der Meinung, daB man ein Buch iiber Programmieren schreiben kann, ohne auf Einzelheiten der Mathematischen Verfahrenstechnik und der Biiroautomation oder auf technische Eigenschaften spezieller Ma- schinen eingehen zu miissen, und ohne damit jewells einem Tell der Leser das Verstandnis zu erschweren. Was nach Fortlassung der ge- nannten Gebiete bleibt, ist nicht ein trockener, unverstandlicher Rest, sondern der Inbegriff aller fiir das Programmieren wesentlichen Prin- zipien. Sowohl der Naturwissenschaftler als auch der Verwaltungsfach- mann, der diese Prinzipien erfaBt hat, wird jederzeit in der Lage sein, sie seinen besonderen Aufgaben dienstbar zu machen. Kapitel A solI zeigen, welchen Platz der Rechenautomat unter den technischen Errungenschaften einnimmt und wie er dorthin gelangt ist. Besonderes Anliegen ist mir hier der geschichtliche Uberblick, well einer- seits die deutschsprachigen Biicher auf diesem Gebiet kaum historische Angaben enthalten und andererseits die anglo-amerikanische Literatur die kontinentaleuropaische Entwicklung iibergeht. - Kapitel B enthalt die Beschreibung einer gedachten Maschine TElCO in allen Einzelheiten.

Take your first steps to becoming a fully qualified data analyst by learning how to explore complex datasets Key Features Master each concept through practical exercises and activities Discover various statistical techniques to analyze your data Implement everything you've learned on a real-world case study to uncover valuable insights Book 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 learn Use SQL to clean, prepare, and combine different datasets Aggregate basic statistics using GROUP BY clauses Perform advanced statistical calculations using a WINDOW function Import data into a database to combine with other tables Export SQL query results into various sources Analyze special data types in SQL, including geospatial, date/time, and JSON data Optimize queries and automate tasks Think about data problems and find answers using SQL Who 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.

A comprehensive introduction to help you get up and running with creating interactive dashboards to visualize and monitor time-series data in no time Key Features Install, set up, and configure Grafana for real-time data analysis and visualization Visualize and monitor data using data sources such as InfluxDB, Prometheus, and Elasticsearch Explore Grafana's multi-cloud support with Microsoft Azure, Amazon CloudWatch, and Google Stackdriver Book DescriptionGrafana is an open-source analytical platform used to analyze and monitoring time-series data. This beginner's guide will help you get to grips with Grafana's new features for querying, visualizing, and exploring metrics and logs no matter where they are stored. The book begins by showing you how to install and set up the Grafana server. You'll explore the working mechanism of various components of the Grafana interface along with its security features, and learn how to visualize and monitor data using, InfluxDB, Prometheus, Logstash, and Elasticsearch. This Grafana book covers the advanced features of the Graph panel and shows you how Stat, Table, Bar Gauge, and Text are used. You'll build dynamic dashboards to perform end-to-end analytics and label and organize dashboards into folders to make them easier to find. As you progress, the book delves into the administrative aspects of Grafana by creating alerts, setting permissions for teams, and implementing user authentication. Along with exploring Grafana's multi-cloud monitoring support, you'll also learn about Grafana Loki, which is a backend logger for users running Prometheus and Kubernetes. By the end of this book, you'll have gained all the knowledge you need to start building interactive dashboards. What you will learn Find out how to visualize data using Grafana Understand how to work with the major components of the Graph panel Explore mixed data sources, query inspector, and time interval settings Discover advanced dashboard features such as annotations, templating with variables, dashboard linking, and dashboard sharing techniques Connect user authentication to Google, GitHub, and a variety of external services Find out how Grafana can provide monitoring support for cloud service infrastructures Who this book is forThis book is for business intelligence developers, business analysts, data analysts, and anyone interested in performing time-series data analysis and monitoring using Grafana. Those looking to create and share interactive dashboards or looking to get up to speed with the latest features of Grafana will also find this book useful. Although no prior knowledge of Grafana is required, basic knowledge of data visualization and some experience in Python programming will help you understand the concepts covered in the book.

Plan, design, develop, and manage robust Power BI solutions to generate meaningful insights and make data-driven decisions. Purchase of the print or Kindle book includes a free eBook in the PDF format. Key Features Master the latest dashboarding and reporting features of Microsoft Power BI Combine data from multiple sources, create stunning visualizations and publish Power BI apps to thousands of users Get the most out of Microsoft Power BI with real-world use cases and examples Book DescriptionMastering Microsoft Power BI, Second Edition, provides an advanced understanding of Power BI to get the most out of your data and maximize business intelligence. This updated edition walks through each essential phase and component of Power BI, and explores the latest, most impactful Power BI features. Using best practices and working code examples, you will connect to data sources, shape and enhance source data, and develop analytical data models. You will also learn how to apply custom visuals, implement new DAX commands and paginated SSRS-style reports, manage application workspaces and metadata, and understand how content can be staged and securely distributed via Power BI apps. Furthermore, you will explore top report and interactive dashboard design practices using features such as bookmarks and the Power KPI visual, alongside the latest capabilities of Power BI mobile applications and self-service BI techniques. Additionally, important management and administration topics are covered, including application lifecycle management via Power BI pipelines, the on-premises data gateway, and Power BI Premium capacity. By the end of this Power BI book, you will be confident in creating sustainable and impactful charts, tables, reports, and dashboards with any kind of data using Microsoft Power BI. What you will learn Build efficient data retrieval and transformation processes with the Power Query M language and dataflows Design scalable, user-friendly DirectQuery, import, and composite data models Create basic and advanced DAX measures Add ArcGIS Maps to create interesting data stories Build pixel-perfect paginated reports Discover the capabilities of Power BI mobile applications Manage and monitor a Power BI environment as a Power BI administrator Scale up a Power BI solution for an enterprise via Power BI Premium capacity Who this book is forBusiness Intelligence professionals and intermediate Power BI users looking to master Power BI for all their data visualization and dashboarding needs will find this book useful. An understanding of basic BI concepts is required and some familiarity with Microsoft Power BI will be helpful to make the most out of this book.

Build a continuously learning and adapting organization that can extract increasing levels of business, customer and operational value from the amalgamation of data and advanced analytics such as AI and Machine Learning Key Features Master the Big Data Business Model Maturity Index methodology to transition to a value-driven organizational mindset Acquire implementable knowledge on digital transformation through 8 practical laws Explore the economics behind digital assets (data and analytics) that appreciate in value when constructed and deployed correctly Book DescriptionIn today's digital era, every organization has data, but just possessing enormous amounts of data is not a sufficient market discriminator. The Economics of Data, Analytics, and Digital Transformation aims to provide actionable insights into the real market discriminators, including an organization's data-fueled analytics products that inspire innovation, deliver insights, help make practical decisions, generate value, and produce mission success for the enterprise. The book begins by first building your mindset to be value-driven and introducing the Big Data Business Model Maturity Index, its maturity index phases, and how to navigate the index. You will explore value engineering, where you will learn how to identify key business initiatives, stakeholders, advanced analytics, data sources, and instrumentation strategies that are essential to data science success. The book will help you accelerate and optimize your company's operations through AI and machine learning. By the end of the book, you will have the tools and techniques to drive your organization's digital transformation. Here are a few words from Dr. Kirk Borne, Data Scientist and Executive Advisor at Booz Allen Hamilton, about the book: Data analytics should first and foremost be about action and value. Consequently, the great value of this book is that it seeks to be actionable. It offers a dynamic progression of purpose-driven ignition points that you can act upon. What you will learn Train your organization to transition from being data-driven to being value-driven Navigate and master the big data business model maturity index Learn a methodology for determining the economic value of your data and analytics Understand how AI and machine learning can create analytics assets that appreciate in value the more that they are used Become aware of digital transformation misconceptions and pitfalls Create empowered and dynamic teams that fuel your organization's digital transformation Who this book is forThis book is designed to benefit everyone from students who aspire to study the economic fundamentals behind data and digital transformation to established business leaders and professionals who want to learn how to leverage data and analytics to accelerate their business careers.

Vehicular traffic congestion and accidents remain universal issues in today's world. Due to the continued growth in the use of vehicles, optimizing traffic management operations is an immense challenge. To reduce the number of traffic accidents, improve the performance of transportation systems, enhance road safety, and protect the environment, vehicular ad-hoc networks have been introduced. Current developments in wireless communication, computing paradigms, big data, and cloud computing enable the enhancement of these networks, equipped with wireless communication capabilities and high-performance processing tools. Cloud-Based Big Data Analytics in Vehicular Ad-Hoc Networks is a pivotal reference source that provides vital research on cloud and data analytic applications in intelligent transportation systems. While highlighting topics such as location routing, accident detection, and data warehousing, this publication addresses future challenges in vehicular ad-hoc networks and presents viable solutions. This book is ideally designed for researchers, computer scientists, engineers, automobile industry professionals, IT practitioners, academicians, and students seeking current research on cloud computing models in vehicular networks.

Learn Azure's cloud capabilities with the help of this introductory guide to employing Azure for your cloud infrastructure needs. Key Features Get a clear overview of Azure's capabilities and benefits, and learn how to get started efficiently Develop the ability to opt for cloud architecture and design that best fits your organization Leverage Azure opportunities for cost savings and optimization Book DescriptionMicrosoft Azure is a powerful cloud computing platform that offers a multitude of services and capabilities for organizations of any size moving to a cloud strategy. Azure Strategy and Implementation Guide Third Edition encapsulates the entire spectrum of measures involved in Azure deployment that includes understanding Azure fundamentals, choosing a suitable cloud architecture, building on design principles, becoming familiar with Azure DevOps, and learning best practices for optimization and management. The book begins by introducing you to the Azure cloud platform and demonstrating the substantial scope of digital transformation and innovation that can be achieved by leveraging Azure's capabilities. The guide further acquaints you with practical insights on application modernization, Azure Infrastructure as a Service (IaaS) deployment, infrastructure management, key application architectures, best practices of Azure DevOps, and Azure automation. By the end of this book, you will be proficient in driving Azure operations right from the planning and cloud migration stage to cost management and troubleshooting. What you will learn Deploy and run Azure infrastructure services Carry out detailed planning for migrating applications to the cloud with Azure Move underlying code class structure into a serverless model Use a gateway to isolate your services and applications Define roles and responsibilities in DevOps Implement release & deployment coordination and automation Who this book is forAzure Strategy and Implementation Guide Third Edition is designed to benefit Azure architects, cloud solution architects, Azure developers, Azure administrators, and anyone who wants to develop an expertise in operating and administering the Azure cloud. A basic familiarity with operating systems and databases will help you grasp the concepts covered in this book.

Learn how to bring your data to life with this hands-on guide to visual analytics with Tableau Key Features Master the fundamentals of Tableau Desktop and Tableau Prep Learn how to explore, analyze, and present data to provide business insights Build your experience and confidence with hands-on exercises and activities Book DescriptionLearning Tableau has never been easier, thanks to this practical introduction to storytelling with data. The Tableau Workshop breaks down the analytical process into five steps: data preparation, data exploration, data analysis, interactivity, and distribution of dashboards. Each stage is addressed with a clear walkthrough of the key tools and techniques you'll need, as well as engaging real-world examples, meaningful data, and practical exercises to give you valuable hands-on experience. As you work through the book, you'll learn Tableau step by step, studying how to clean, shape, and combine data, as well as how to choose the most suitable charts for any given scenario. You'll load data from various sources and formats, perform data engineering to create new data that delivers deeper insights, and create interactive dashboards that engage end-users. All concepts are introduced with clear, simple explanations and demonstrated through realistic example scenarios. You'll simulate real-world data science projects with use cases such as traffic violations, urban populations, coffee store sales, and air travel delays. By the end of this Tableau book, you'll have the skills and knowledge to confidently present analytical results and make data-driven decisions. What you will learn Become an effective user of Tableau Prep and Tableau Desktop Load, combine, and process data for analysis and visualization Understand different types of charts and when to use them Perform calculations to engineer new data and unlock hidden insights Add interactivity to your visualizations to make them more engaging Create holistic dashboards that are detailed and user-friendly Who this book is forThis book is for anyone who wants to get started on visual analytics with Tableau. If you're new to Tableau, this Workshop will get you up and running. If you already have some experience in Tableau, this book will help fill in any gaps, consolidate your understanding, and give you extra practice of key tools.

Learn to extract actionable insights from your big data in real time using a range of Microsoft Azure features Key Features Updated with the latest features and new additions to Microsoft Azure Master the fundamentals of cloud analytics using Azure Learn to use Azure Synapse Analytics (formerly known as Azure SQL Data Warehouse) to derive real-time customer insights Book DescriptionCloud Analytics with Microsoft Azure serves as a comprehensive guide for big data analysis and processing using a range of Microsoft Azure features. This book covers everything you need to build your own data warehouse and learn numerous techniques to gain useful insights by analyzing big data The book begins by introducing you to the power of data with big data analytics, the Internet of Things (IoT), machine learning, artificial intelligence, and DataOps. You will learn about cloud-scale analytics and the services Microsoft Azure offers to empower businesses to discover insights. You will also be introduced to the new features and functionalities added to the modern data warehouse. Finally, you will look at two real-world business use cases to demonstrate high-level solutions using Microsoft Azure. The aim of these use cases will be to illustrate how real-time data can be analyzed in Azure to derive meaningful insights and make business decisions. You will learn to build an end-to-end analytics pipeline on the cloud with machine learning and deep learning concepts. By the end of this book, you will be proficient in analyzing large amounts of data with Azure and using it effectively to benefit your organization. What you will learn Explore the concepts of modern data warehouses and data pipelines Discover unique design considerations while applying a cloud analytics solution Design an end-to-end analytics pipeline on the cloud Differentiate between structured, semi-structured, and unstructured data Choose a cloud-based service for your data analytics solutions Use Azure services to ingest, store, and analyze data of any scale Who this book is forThis book is designed to benefit software engineers, Azure developers, cloud consultants, and anyone who is keen to learn the process of deriving business insights from huge amounts of data using Azure. Though not necessary, a basic understanding of data analytics concepts such as data streaming, data types, the machine learning life cycle, and Docker containers will help you get the most out of the book.

Design, secure, and protect the privacy of edge analytics applications using platforms and tools such as Microsoft's Azure IoT Edge, MicroPython, and Open Source Computer Vision (OpenCV) Key Features Become well-versed with best practices for implementing automated analytical computations Discover real-world examples to extend cloud intelligence Develop your skills by understanding edge analytics and applying it to research activities Book DescriptionEdge analytics has gained attention as the IoT model for connected devices rises in popularity. This guide will give you insights into edge analytics as a data analysis model, and help you understand why it's gaining momentum. You'll begin with the key concepts and components used in an edge analytics app. Moving ahead, you'll delve into communication protocols to understand how sensors send their data to computers or microcontrollers. Next, the book will demonstrate how to design modern edge analytics apps that take advantage of the processing power of modern single-board computers and microcontrollers. Later, you'll explore Microsoft Azure IoT Edge, MicroPython, and the OpenCV visual recognition library. As you progress, you'll cover techniques for processing AI functionalities from the server side to the sensory side of IoT. You'll even get hands-on with designing a smart doorbell system using the technologies you've learned. To remove vulnerabilities in the overall edge analytics architecture, you'll discover ways to overcome security and privacy challenges. Finally, you'll use tools to audit and perform real-time monitoring of incoming data and generate alerts for the infrastructure. By the end of this book, you'll have learned how to use edge analytics programming techniques and be able to implement automated analytical computations. What you will learn Discover the key concepts and architectures used with edge analytics Understand how to use long-distance communication protocols for edge analytics Deploy Microsoft Azure IoT Edge to a Raspberry Pi Create Node-RED dashboards with MQTT and Text to Speech (TTS) Use MicroPython for developing edge analytics apps Explore various machine learning techniques and discover how machine learning is related to edge analytics Use camera and vision recognition algorithms on the sensory side to design an edge analytics app Monitor and audit edge analytics apps Who this book is forIf you are a data analyst, data architect, or data scientist who is interested in learning and practicing advanced automated analytical computations, then this book is for you. You will also find this book useful if you're looking to learn edge analytics from scratch. Basic knowledge of data analytics concepts is assumed to get the most out of this book.

Solve real-world business problems by learning how to create common industry key performance indicators and other calculations using DAX within Microsoft products such as Power BI, SQL Server, and Excel. Key Features Learn to write sophisticated DAX queries to solve business intelligence and data analytics challenges Handle performance issues and optimization within the data model, DAX calculations and more Solve business issues with Microsoft Excel, Power BI, and SQL Server using DAX queries Book DescriptionDAX provides an extra edge by extracting key information from the data that is already present in your model. Filled with examples of practical, real-world calculations geared toward business metrics and key performance indicators, this cookbook features solutions that you can apply for your own business analysis needs. You'll learn to write various DAX expressions and functions to understand how DAX queries work. The book also covers sections on dates, time, and duration to help you deal with working days, time zones, and shifts. You'll then discover how to manipulate text and numbers to create dynamic titles and ranks, and deal with measure totals. Later, you'll explore common business metrics for finance, customers, employees, and projects. The book will also show you how to implement common industry metrics such as days of supply, mean time between failure, order cycle time and overall equipment effectiveness. In the concluding chapters, you'll learn to apply statistical formulas for covariance, kurtosis, and skewness. Finally, you'll explore advanced DAX patterns for interpolation, inverse aggregators, inverse slicers, and even forecasting with a deseasonalized correlation coefficient. By the end of this book, you'll have the skills you need to use DAX's functionality and flexibility in business intelligence and data analytics. What you will learn Understand how to create common calculations for dates, time, and duration Create key performance indicators (KPIs) and other business calculations Develop general DAX calculations that deal with text and numbers Discover new ideas and time-saving techniques for better calculations and models Perform advanced DAX calculations for solving statistical measures and other mathematical formulas Handle errors in DAX and learn how to debug DAX calculations Understand how to optimize your data models Who this book is forBusiness users, BI developers, data analysts, and SQL users who are looking for solutions to the challenges faced while solving analytical operations using DAX techniques and patterns will find this book useful. Basic knowledge of the DAX language and Microsoft services is mandatory.

Tragwerke, die besonderen Belastungen oder au13ergewohnlichen Kraft- einwirkungen ausgesetzt sind, durch die Kiihnheit ihrer Konstruktion bzw. eine nicht alltagliche Zweckbestimmung sich auszeichnen, endlich solche, die aus neuartigem, noch nicht geniigend durchforschtem Material bestehen, werden unmittelbar vor Dbergabe an den Betrieb einer amtlichen Belastungsprobe unterzogen, die in bestimmten Zeitabschnitten wiederholt wird (periodische Erprobung). In Verbindung mit den Ergebnissen einer plangema13en Dberpriifung stehen uns damit ausreichende Anhalte zur Abgabe eines zutreffenden Urteiles iiber die Giite des in Frage stehenden Bauwerkes zur Verfiigung. Die periodischen Deformationsmessungen an bereits im Betriebe stehenden Tragwerken geben wieder Aufschlu13 iiber zwischenzeitlich etwa entstandene, optisch nur schwer wahmehmbare Mangel an lebenswichtigen Traggliedern, mit deren Bestehen immer dann zu rechnen ist, wenn im Vergleiche mit den Ergebnissen voran- gegangener Proben unzulassig gro13e Deformationen auftreten. Die Wichtigkeit und Notwendigkeit solcher diagnostisch unentbehrlichen Messungen wurde schon friih erkannt. Fehldiagnosen sind allerdings nicht ausgeschlossen, besonders wenn die verschiedenen Faktoren, die das Me13ergebnis ma13geblich beeinflussen, nicht richtig gedeutet und gegeneinander abgeschatzt werden. Hier setzt die ebenso schwierige wie verantwortungsvolle Tatigkeit des mit der Priifung und Erprobung betrauten Ingenieurs ein, der daher nicht nur iiber griindliches Wissen und reiche Erfahrung, sondern dariiber hinaus auch liber gewisse technisch- diagnostische F iihigkeiten verfligen mli13te. Andernfalls besteht, wie die Erfahrung lehrt, die nicht llnbegriindete Befiirchtung, daB z. B. Rekonstruktionen angeordnet werden, die, abgesehen von den Kosten, zumindest abwegig, wenn nicht gar system widrig sein konnen.

A practical guide to implementing a scalable and fast state-of-the-art analytical data estate Key Features Store and analyze data with enterprise-grade security and auditing Perform batch, streaming, and interactive analytics to optimize your big data solutions with ease Develop and run parallel data processing programs using real-world enterprise scenarios Book DescriptionAzure Data Lake, the modern data warehouse architecture, and related data services on Azure enable organizations to build their own customized analytical platform to fit any analytical requirements in terms of volume, speed, and quality. This book is your guide to learning all the features and capabilities of Azure data services for storing, processing, and analyzing data (structured, unstructured, and semi-structured) of any size. You will explore key techniques for ingesting and storing data and perform batch, streaming, and interactive analytics. The book also shows you how to overcome various challenges and complexities relating to productivity and scaling. Next, you will be able to develop and run massive data workloads to perform different actions. Using a cloud-based big data-modern data warehouse-analytics setup, you will also be able to build secure, scalable data estates for enterprises. Finally, you will not only learn how to develop a data warehouse but also understand how to create enterprise-grade security and auditing big data programs. By the end of this Azure book, you will have learned how to develop a powerful and efficient analytical platform to meet enterprise needs. What you will learn Implement data governance with Azure services Use integrated monitoring in the Azure Portal and integrate Azure Data Lake Storage into the Azure Monitor Explore the serverless feature for ad-hoc data discovery, logical data warehousing, and data wrangling Implement networking with Synapse Analytics and Spark pools Create and run Spark jobs with Databricks clusters Implement streaming using Azure Functions, a serverless runtime environment on Azure Explore the predefined ML services in Azure and use them in your app Who this book is forThis book is for data architects, ETL developers, or anyone who wants to get well-versed with Azure data services to implement an analytical data estate for their enterprise. The book will also appeal to data scientists and data analysts who want to explore all the capabilities of Azure data services, which can be used to store, process, and analyze any kind of data. A beginner-level understanding of data analysis and streaming will be required.

Discover the true power of DAX and build advanced DAX solutions for practical business scenarios Key Features Solve complex business problems within Microsoft BI tools including Power BI, SQL Server, and Excel Develop a conceptual understanding of critical business data modeling principles Learn the subtleties of Power BI data visualizations, evaluation context, context transition, and filtering Book DescriptionThis book helps business analysts generate powerful and sophisticated analyses from their data using DAX and get the most out of Microsoft Business Intelligence tools. Extreme DAX will first teach you the principles of business intelligence, good model design, and how DAX fits into it all. Then, you'll launch into detailed examples of DAX in real-world business scenarios such as inventory calculations, forecasting, intercompany business, and data security. At each step, senior DAX experts will walk you through the subtleties involved in working with Power BI models and common mistakes to look out for as you build advanced data aggregations. You'll deepen your understanding of DAX functions, filters, and measures, and how and when they can be used to derive effective insights. You'll also be provided with PBIX files for each chapter, so that you can follow along and explore in your own time. What you will learn Understand data modeling concepts and structures before you start working with DAX Grasp how relationships in Power BI models are different from those in RDBMSes Secure aggregation levels, attributes, and hierarchies using PATH functions and row-level security Get to grips with the crucial concept of context Apply advanced context and filtering functions including TREATAS, GENERATE, and SUMMARIZE Explore dynamically changing visualizations with helper tables and dynamic labels and axes Work with week-based calendars and understand standard time-intelligence Evaluate investments intelligently with the XNPV and XIRR financial DAX functions Who this book is forExtreme DAX is written for analysts with a working knowledge of DAX in Power BI or other Microsoft analytics tools. It will help you upgrade your knowledge and work with analytical models more effectively, so you'll need practical experience with DAX before you can get started.