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.

Get to grips with building reliable, scalable, and maintainable database solutions for enterprises and production databases Key Features Implement PostgreSQL 13 features to perform end-to-end modern database management Design, manage, and build enterprise database solutions using a unique recipe-based approach Solve common and not-so-common challenges faced while working to achieve optimal database performance Book DescriptionPostgreSQL has become the most advanced open source database on the market. This book follows a step-by-step approach, guiding you effectively in deploying PostgreSQL in production environments. The book starts with an introduction to PostgreSQL and its architecture. You'll cover common and not-so-common challenges faced while designing and managing the database. Next, the book focuses on backup and recovery strategies to ensure your database is steady and achieves optimal performance. Throughout the book, you'll address key challenges such as maintaining reliability, data integrity, a fault-tolerant environment, a robust feature set, extensibility, consistency, and authentication. Moving ahead, you'll learn how to manage a PostgreSQL cluster and explore replication features for high availability. Later chapters will assist you in building a secure PostgreSQL server, along with covering recipes for encrypting data in motion and data at rest. Finally, you'll not only discover how to tune your database for optimal performance but also understand ways to monitor and manage maintenance activities, before learning how to perform PostgreSQL upgrades during downtime. By the end of this book, you'll be well-versed with the essential PostgreSQL 13 features to build enterprise relational databases. What you will learn Understand logical and physical backups in Postgres Demonstrate the different types of replication methods possible with PostgreSQL today Set up a high availability cluster that provides seamless automatic failover for applications Secure a PostgreSQL encryption through authentication, authorization, and auditing Analyze the live and historic activity of a PostgreSQL server Understand how to monitor critical services in Postgres 13 Manage maintenance activities and performance tuning of a PostgreSQL cluster Who this book is forThis PostgreSQL book is for database architects, database developers and administrators, or anyone who wants to become well-versed with PostgreSQL 13 features to plan, manage, and design efficient database solutions. Prior experience with the PostgreSQL database and SQL language is expected.

Unsere Umwelt wird mehr und mehr gepragt durch Automatische Datenverarbeitung. Weil die technische Seite dabei von der Elektronik beherrscht wird, verwendet man durch weg den Begriff Elektronische Datenverarbeitung (EDV). Es gibt eigentlich kaum noch einen Bereich im menschlichen Zusammenleben, der nicht mit EDV zumindest in Be ruhrung kame. Beinahe taglich begegnet uns EDV direkt oder indirekt - bewulSt und unbewulSt - beispielsweise in Form von Abrechnungen, Benachrichtigungen, beim Ein kauf, in der Verwaltung und vor allem im Berufsleben. Das ist der aktuelle Bezug fur das vorliegende Lehrbuch. Und nicht etwa modische Aspekte ver: anlassen zu der Aufforderung, Prinzipien und Einzelheiten der EDV zu studieren. Fur den angehenden Techniker und Ingenieur ergibt sich eine Notwendigkeit fur ein vertieftes Studium daraus, daIS man bei der Berufsausubung von ihm Verstandnis fur oder gar Detail kenntnisse uber EDV erwartet. Das wird besonders unabwendbar, wenn es sich um einen Absolventen mit elektrotechnischer Spl'/ialausbildung handelt. Der Bedeutung der EDV wird inzwischen mit einer Vielzahl von Abhandlungen und Lehr buchern gerecht. Das errichtete N iveau reicht dabei von einfachsten Darstellungen fur Jedermann bis zu wissenschaftlichen Arbeiten, die nur von Spezialisten lesbar sind."

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.

Das Arbeitsergebnis des Studienkreises Dr. Parli wurde zunachst in Form eines internen Arbeitsberichtes den Mitgliedern des Foerderervereins des Betriebswirtschaftlichen Instituts fur Organisation und Automation an der Universitat zu Koeln (BIFOA) zur Verfugung gestellt. Die sich daraus er- gebende Diskussion zeigte, dass die Probleme der Istaufnahme bei automati- sierter Datenverarbeitung (ADV) nach wie vor in Wissenschaft und Praxis von hoher Aktualitat sind, so dass mir nunmehr - nicht zuletzt aufgrund zahlreicher Anfragen aus der Wirtschaftspraxis - eine Publikation in der Instituts-Schriftenreihe sinnvoll erscheint. Damit werden die Ergebnisse einem groesseren Interessentenkreis zuganglich und koennen insbesondere mittleren und kleineren Unternehmungen und Einheiten der oeffentlichen Verwaltung, die aufgrund des vielfaltigen Angebots unterschiedlicher Computergroessen ebenfalls in den Kreis der Anwender von Anlagen der automatisierten Datenverarbeitung geruckt sind, als Orientierungshilfe dienen. In der vorliegenden Arbeit werden die Erfahrungen von Wirtschaftsprakti- kern aus Grossunternehmungen verschiedener Branchen sowie der oeffent- lichen Verwaltung systematisiert und auf ihre Allgemeingultigkeit unter- sucht. Es handelt sich um Erfahrungen, die aus Unternehmungen stammen, die sich aufgrund ihres Geschaftsumfanges schon fruhzeitig zum Einsatz von ADV-Anlagen entschliessen mussten und die teilweise - entsprechend den Stufen der technischen und organisatorischen Entwicklung - mit den Istaufnahmeproblemen unterschiedlichster Art konfrontiert wurden. Das Hauptanliegen der Schrift besteht nicht in einer rein theoretischen Durchdringung des Problemkreises Istaufnahme und Automatisierte Daten- verarbeitung, sondern in einer praxisbezogenen Aufbereitung und Systema- tisierung empirischen Wissens auf diesem Gebiet.

Many organizations, including government institutions and agencies, continue to increase their financial investment on information technology (IT) solutions. Despite these investments, during the global pandemic, employees and managers are either struggling or unequipped to use these tools effectively and efficiently for sustainability, competitive advantage, and decision making. Due to global pandemics, companies must harness the power of various digital channels such as big data analytics and artificial intelligence to better serve their customers and business partners. Using Information Technology Advancements to Adapt to Global Pandemics provides insights and understanding on how companies and organizations are using advances in IT to adapt to global pandemics such as COVID-19. It explores how the various IT approaches can be used for strategic purposes. Covering topics such as higher education institutions, religious organizations, and telework, this premier reference source is an essential resource for government officials, business leaders and managers, industry professionals, IT specialists, policymakers, libraries, academicians, students, and researchers.

Learn how to gain insights from your data as well as machine learning and become a presentation pro who can create interactive dashboards Key Features Enhance your presentation skills by implementing engaging data storytelling and visualization techniques Learn the basics of machine learning and easily apply machine learning models to your data Improve productivity by automating your data processes Book DescriptionData Analytics Made Easy is an accessible beginner's guide for anyone working with data. The book interweaves four key elements: Data visualizations and storytelling - Tired of people not listening to you and ignoring your results? Don't worry; chapters 7 and 8 show you how to enhance your presentations and engage with your managers and co-workers. Learn to create focused content with a well-structured story behind it to captivate your audience. Automating your data workflows - Improve your productivity by automating your data analysis. This book introduces you to the open-source platform, KNIME Analytics Platform. You'll see how to use this no-code and free-to-use software to create a KNIME workflow of your data processes just by clicking and dragging components. Machine learning - Data Analytics Made Easy describes popular machine learning approaches in a simplified and visual way before implementing these machine learning models using KNIME. You'll not only be able to understand data scientists' machine learning models; you'll be able to challenge them and build your own. Creating interactive dashboards - Follow the book's simple methodology to create professional-looking dashboards using Microsoft Power BI, giving users the capability to slice and dice data and drill down into the results. What you will learn Understand the potential of data and its impact on your business Import, clean, transform, combine data feeds, and automate your processes Influence business decisions by learning to create engaging presentations Build real-world models to improve profitability, create customer segmentation, automate and improve data reporting, and more Create professional-looking and business-centric visuals and dashboards Open the lid on the black box of AI and learn about and implement supervised and unsupervised machine learning models Who this book is forThis book is for beginners who work with data and those who need to know how to interpret their business/customer data. The book also covers the high-level concepts of data workflows, machine learning, data storytelling, and visualizations, which are useful for managers. No previous math, statistics, or computer science knowledge is required.

1m Oktober 1968 trafen Klinikchefs mit Spezialisten aus dem Bereich der Hoch- schulen und der Computer-lndustrie in Reinhartshausen zusammen, urn innerhalb der raschen Entwicklung der sogenannten zweiten technischen Revolution den Trend der modernen Medizin aufzusptiren. Ais Diskussionsgrundlage dienten ausgewillllte Refe- rate. Ein tiberblick tiber den Verlauf dieser Tagung Ui.l3t es niitzlich erscheinen, die Thematik einem grol3eren Kreis zugiinglich zu machen. So haben wir uns entschlossen, die Manuskripte der Autoren zu einem Werk zusammenzuschliel3en. Die technischen Grundlagen der elektronischen Datenverarbeitung sollen dabei allerdings unbertick- sichtigt bleiben. Die Durchsicht der Beitrage mag den Eindruck erwecken, dal3 anscheinend bereits zurtickliegende Entwicklungsphasen mit phantasievollen Forderungen an die Zukunft inhomogen zusammengestellt seien. Aber es kommt uns darauf an, in der bestaunens- wert en Schnelligkeit, mit der sich eine elektronische Informationsverarbeitung - oder besser formuliert - die moderne Wissenschaft der Informatik vollzieht, den gegen- wartigen Zustand in der Medizin aufzuzeigen und in ihm an den Einzelheiten die Ten- denzen darzustellen, die sich bald aus den ursprtinglichen mechanischen Formen der Erfassung und Verarbeitung von Daten, bald aus dem Bild der Zukunft deutlicher ab- zeichnen. Wir hegen die Hoffnung, dal3 auf dieser Basis sich pragende Konzeptionen fUr die Gestaltung der Zukunft ergeben. Herrn Kollegen NORBERT EICHENSEHER danken wir fUr seine wertvolle Unter- stiltzung bei den Korrekturen und der Abfassung des Sachverzeichnisses.

This report discusses the role computer-assisted personal interviewing (CAPI) can play in transforming survey data collection to allow better monitoring of the Sustainable Development Goals. The first part of this publication provides rigorous quantitative evidence on why CAPI is a better alternative to the traditional pen and paper interviewing method, particularly in the context of nationally representative surveys. The second part discusses the benefits of delivering CAPI training to statisticians using the popular massive online open course format. The final part provides a summary of existing CAPI platforms and offers some preliminary advice for NSOs to consider when selecting a CAPI platform for their institution. This is a Special Supplement to the Key Indicators for Asia and the Pacific 2019.

Als Stahl bezeichnet man heute alle Eisenlegierungen - mit Ausnahme der nicht schmiedbaren hochkohlenstoffhaltigen Gu sorten wie Grauguli, Hartguf und Ternperguf - ohne Riicksichr auf ihre Eigenschaften. Friiher wurde als wesentliches Merkmal des Stahles die Hartbarkeit angesehen. Es gibt aber eine ganze Reihe von Stahlen, die sich nicht harten lassen, die durch das Abschrecken aus hohen Temperaturen im Gegenteil sogar weicher, zaher werden. Edelstdble werden vielfach solche Stahle genannt, die au er mit Kohlenstoff auch noch mit anderen Grundstoffen, z. B. mit Chrom, Nickel, Wolfram, Vanadin usw. legiert sind. Diese Begriffsbestim- mung ist jedoch nicht erschopfend und auch anfechtbar, Denn man wird einen reinen Kohlenstoffstahl, der sorgfaltig erzeugt und auf dem ganzen Wege der Herstellung - vom Gu bis zum Versand - immer wieder gewissenhaft gepriift worden ist, zweifellos auch zu den Edelstahlen rechnen miissen. Andererseits enthalten manchmal Massenstahle - auch als unbeabsichtigte Verunreinigungen - ge- wisse Mengen von Legierungselementen. Das Richtige wird man treffen, wenn man die bei den grofsen Hiittenwerken in grofien Mengen erzeugten billigen Stahle als .Mas- senstahle bezeichnet, die von einem Edelstahlwerk mit Sorgfalt und unter scharfster Kontrolle hergestellten Stahle dagegen als Edelstahle. Die billigen Massenstahle werden meistens nach Festigkeit ver- kauft, die Edelstahle dagegen nach dem Verwendungszweck und unter einer Markenbezeichnung.

Leverage the Azure analytics platform's key analytics services to deliver unmatched intelligence for your data Key Features Learn to ingest, prepare, manage, and serve data for immediate business requirements Bring enterprise data warehousing and big data analytics together to gain insights from your data Develop end-to-end analytics solutions using Azure Synapse Book DescriptionAzure Synapse Analytics, which Microsoft describes as the next evolution of Azure SQL Data Warehouse, is a limitless analytics service that brings enterprise data warehousing and big data analytics together. With this book, you'll learn how to discover insights from your data effectively using this platform. The book starts with an overview of Azure Synapse Analytics, its architecture, and how it can be used to improve business intelligence and machine learning capabilities. Next, you'll go on to choose and set up the correct environment for your business problem. You'll also learn a variety of ways to ingest data from various sources and orchestrate the data using transformation techniques offered by Azure Synapse. Later, you'll explore how to handle both relational and non-relational data using the SQL language. As you progress, you'll perform real-time streaming and execute data analysis operations on your data using various languages, before going on to apply ML techniques to derive accurate and granular insights from data. Finally, you'll discover how to protect sensitive data in real time by using security and privacy features. By the end of this Azure book, you'll be able to build end-to-end analytics solutions while focusing on data prep, data management, data warehousing, and AI tasks. What you will learn Explore the necessary considerations for data ingestion and orchestration while building analytical pipelines Understand pipelines and activities in Synapse pipelines and use them to construct end-to-end data-driven workflows Query data using various coding languages on Azure Synapse Focus on Synapse SQL and Synapse Spark Manage and monitor resource utilization and query activity in Azure Synapse Connect Power BI workspaces with Azure Synapse and create or modify reports directly from Synapse Studio Create and manage IP firewall rules in Azure Synapse Who this book is forThis book is for data architects, data scientists, data engineers, and business analysts who are looking to get up and running with the Azure Synapse Analytics platform. Basic knowledge of data warehousing will be beneficial to help you understand the concepts covered in this book more effectively.

Understand the complexities of modern-day data engineering platforms and explore strategies to deal with them with the help of use case scenarios led by an industry expert in big data Key Features Become well-versed with the core concepts of Apache Spark and Delta Lake for building data platforms Learn how to ingest, process, and analyze data that can be later used for training machine learning models Understand how to operationalize data models in production using curated data Book DescriptionIn the world of ever-changing data and schemas, it is important to build data pipelines that can auto-adjust to changes. This book will help you build scalable data platforms that managers, data scientists, and data analysts can rely on. Starting with an introduction to data engineering, along with its key concepts and architectures, this book will show you how to use Microsoft Azure Cloud services effectively for data engineering. You'll cover data lake design patterns and the different stages through which the data needs to flow in a typical data lake. Once you've explored the main features of Delta Lake to build data lakes with fast performance and governance in mind, you'll advance to implementing the lambda architecture using Delta Lake. Packed with practical examples and code snippets, this book takes you through real-world examples based on production scenarios faced by the author in his 10 years of experience working with big data. Finally, you'll cover data lake deployment strategies that play an important role in provisioning the cloud resources and deploying the data pipelines in a repeatable and continuous way. By the end of this data engineering book, you'll know how to effectively deal with ever-changing data and create scalable data pipelines to streamline data science, ML, and artificial intelligence (AI) tasks. What you will learn Discover the challenges you may face in the data engineering world Add ACID transactions to Apache Spark using Delta Lake Understand effective design strategies to build enterprise-grade data lakes Explore architectural and design patterns for building efficient data ingestion pipelines Orchestrate a data pipeline for preprocessing data using Apache Spark and Delta Lake APIs Automate deployment and monitoring of data pipelines in production Get to grips with securing, monitoring, and managing data pipelines models efficiently Who this book is forThis book is for aspiring data engineers and data analysts who are new to the world of data engineering and are looking for a practical guide to building scalable data platforms. If you already work with PySpark and want to use Delta Lake for data engineering, you'll find this book useful. Basic knowledge of Python, Spark, and SQL is expected.

Data is an increasingly important business asset and enabler for organisational activities. Data quality is a key aspect of data management and failure to understand it increases organisational risk and decreases efficiency and profitability. This book explains data quality management in practical terms, focusing on three key areas - the nature of data in enterprises, the purpose and scope of data quality management, and implementing a data quality management system, in line with ISO 8000-61.

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.