Kurs i Hirtshals

test [-]

Kurset er en del av sertifiseringsløpet "Sertifisert samspillsleder" som består av 6 moduler totalt.  Kurset gjennomføres som interaktivt nettkurs. De fleste er enige om at det er menneskene som er den største suksessfaktoren for et vellykket prosjekt. I samspillprosjekter skal vi integrere et helt nytt team med personer fra ulike organisasjoner. Høyt presterende team kommer ikke av seg selv, de må utvikles. Jo raskere vi klarer å bygge et sterkt team, jo bedre vil prosjektet bli.   For å imøtekomme et økende kompetansebehov i bransjen og bidra til omforent bruk av samspill som gjennomføringsmodell, lanserer Marstrand en sertifiseringsordning innen samspillsprosjekter – «Sertifisert samspillsleder». Marstrands sertifiseringsordning for samspill i bygg- og anleggsbransjen er den første og eneste på markedet, og vil bidra til å styrke bransjen evne til å lykkes med sine prosjekter. Hver gang! Kursprogrammet i «Sertifisert samspillsleder» består av totalt 6 moduler, hvorav to grunnleggende kurs i samspillprosjekter og fire praktiske workshops som utgjør spesialisering. Samlet vil dette gi deltakerne teoretisk kunnskap, funksjonell trening og nyttig erfaringsdeling i samspillsorienterte prosjektprosesser. Grunnkurs 1: Samspillprosjekter Grunnkurs 2: Samspillkontrakter og konkurranse Spesialisering 1: Evaluering og valg av team Spesialisering 2: Utvikling av team og samhandlingskultur Spesialisering 3: Organisering og prosjekteierstyring Spesialisering 4: Praktisk gjennomføring av samspill Grunnkursene gjennomføres som fysiske kurs, spesialiseringen som interaktive nettkurs. [-]

 test [-]

Velkommen til kurs i VG3 Salgsfaget hos AOF   Dette nettkurset er delt opp i fem moduler. Den består av: Bedriften Produkt, pris og markedsføring Salg- og service Oppgaveskriving Oppsummering og den avsluttende innsendingsoppgaven   Praksis gir dere et godt utgangspunkt Som praksiskandidater har dere allerede mye erfaring. Husk at dere allerede kan mye. Dette kurset er bygget opp på en slik måte at dere får testet deres kunnskaper, og læreboken vil gi ny kunnskap. Noe av det dere leser kan dere kanskje allerede. Ingenting er bedre enn det. Det viser at dere har lært mye på arbeidsplassen. Nettkurset er laget slik at dere kan lære pensum på en måte som gir et aktivt forhold til læreboken og fagfeltet dere arbeider på. Målet er å gi dere de beste forutsetningene for å bestå den teoretiske eksamen. For at dere skal bestå teorieksamen er det viktig at dere viser at dere klarer å kombinere teori og praksis. Nettkurset er sammen satt for at du skal få best mulig læringsutbytte, og bygger på ulike pedagogiske metoder. Kurset er sammensatt av en kombinasjon av: Tekst (i nettkurset og i læreboken) Øvelsesoppgaver Tankedelingsoppgaver Multiple choice oppgaver Innsendingsoppgaver Du vil underveis i kurset få oppfølging fra nettveileder.   Kurset har et omfang på ca 50 timer avhengig av dine forkunnskaper og digitale ferdigheter. I tillegg må du påregne tid til egenstudie.   Finansiering Dersom du er medlem i en fagforening vil du i mange tilfeller kunne søke om stipend til utdanning. En del fagforeninger gir stipend gjennom LO`s utdanningsfond. Stipendsatsen for fagbrevutdanning hor LO`s utdanningsfond er for tiden kr 10.500. Har du ikke rettigheter i LO`s utadanningsfod har også Fagforbundet og Handel og kontor egne stipendordninger. Ta kontakt med din fagforening for å få mer informasjon om finansiering. Pensum tar utgangspunkt i Salgsfaget VG3, boken kan du kjøpe på: http://yrkeslitteratur.no   Kjøp kurset her. [-]

In this course, you will learn about cloud-based Big Data solutions such as Amazon EMR, Amazon Redshift, Amazon Kinesis, and the rest of the AWS Big Data platform. We will show you how to use Amazon EMR to process data using the broad ecosystem of Hadoop tools like Hive and Hue. We will also teach you how to create Big Data environments, work with Amazon DynamoDB, Amazon Redshift, Amazon QuickSight, Amazon Athena, and Amazon Kinesis, and leverage best practices to design Big Data environments for security and cost-effectiveness. Course Objectives  In this course, you will learn to: Fit AWS solutions inside a Big Data ecosystem Leverage Apache Hadoop in the context of Amazon EMR Identify the components of an Amazon EMR cluster, then launch and configure an Amazon EMR cluster Use common programming frameworks available for Amazon EMR, including Hive, Pig, and streaming Improve the ease of use of Amazon EMR by using Hadoop User Experience (Hue) Use in-memory analytics with Apache Spark on Amazon EMR Choose appropriate AWS data storage options Identify the benefits of using Amazon Kinesis for near real-time Big Data processing Leverage Amazon Redshift to efficiently store and analyze data Comprehend and manage costs and security for a Big Data solution Identify options for ingesting, transferring, and compressing data Leverage Amazon Athena for ad-hoc query analytics Use AWS Glue to automate extract, transform, and load (ETL) workloads Use visualization software to depict data and queries using Amazon QuickSight   Delivery method This course will be delivered through a mix of: Instructor-led Training (ILT) Hands-on Labs   Hands-on activity This course allows you to test new skills and apply knowledge to your working environment through a variety of practical exercises   Course Outline Day 1 Overview of Big Data Ingestion Big Data streaming and Amazon Kinesis Using Kinesis to stream and analyze Apache server logs Storage Solutions Querying Big Data using Amazon Athena Using Amazon Athena to analyze log data Introduction to Apache Hadoop and Amazon EMR Day 2 Using Amazon Elastic MapReduce Storing and Querying Data on DynamoDB Hadoop Programming Frameworks Processing Server Logs with Hive on Amazon EMR Streamlining Your Amazon EMR Experience with Hue Running Pig Scripts in Hue on Amazon EMR Spark on Amazon EMR Processing New York Taxi dataset using Spark on Amazon EMR Day 3 Using AWS Glue to automate ETL workloads Amazon Redshift and Big Data Visualizing and Orchestrating Big Data Visualizing Managing Amazon EMR Costs Securing Big Data solutions Big Data Design Patterns   [-]

VAL FoU og Sikkerhetssenteret Rørvik AS har utviklet e-læringskurs for havbruksnæringa. Kurset er lovpålagt og dekker lov- og forskriftsfestede kompetansekrav for laksefisk. Kort om kursinnholdet Fiskevelferd Regelverk Forebyggende helsearbeid Helseutfordringer og sykdom Hvordan ivareta fiskevelferd under ulike arbeidsoperasjoner? Lus og avlusing Rensefisk Miljø og produksjonssystemer   Kurset er utviklet- og holdes av fiskehelsepersonell som jobber i havbruksnæringen.  [-]

In this course, the students will implement various data platform technologies into solutions that are in line with business and technical requirements including on-premises, cloud, and hybrid data scenarios incorporating both relational and No-SQL data. They will also learn how to process data using a range of technologies and languages for both streaming and batch data. The students will also explore how to implement data security including authentication, authorization, data policies and standards. They will also define and implement data solution monitoring for both the data storage and data processing activities. Finally, they will manage and troubleshoot Azure data solutions which includes the optimization and disaster recovery of big data, batch processing and streaming data solutions.   Course content This course contains these themes and modules: Module 1: Azure for the Data Engineer Explain the evolving world of data Survey the services in the Azure Data Platform Identify the tasks that are performed by a Data Engineer Describe the use cases for the cloud in a Case Study Module 2: Working with Data Storage Choose a data storage approach in Azure Create an Azure Storage Account Explain Azure Data Lake storage Upload data into Azure Data Lake Module 3: Enabling Team Based Data Science with Azure Databricks Explain Azure Databricks and Machine Learning Platforms Describe the Team Data Science Process Provision Azure Databricks and workspaces Perform data preparation tasks Module 4: Building Globally Distributed Databases with Cosmos DB Create an Azure Cosmos DB database built to scale Insert and query data in your Azure Cosmos DB database Provision a .NET Core app for Cosmos DB in Visual Studio Code Distribute your data globally with Azure Cosmos DB Module 5: Working with Relational Data Stores in the Cloud SQL Database and SQL Data Warehouse Provision an Azure SQL database to store data Provision and load data into Azure SQL Data Warehouse Module 6: Performing Real-Time Analytics with Stream Analytics Explain data streams and event processing Querying streaming data using Stream Analytics How to process data with Azure Blob and Stream Analytics How to process data with Event Hubs and Stream Analytics Module 7: Orchestrating Data Movement with Azure Data Factory Explain how Azure Data Factory works Create Linked Services and datasets Create pipelines and activities Azure Data Factory pipeline execution and triggers Module 8: Securing Azure Data Platforms Configuring Network Security Configuring Authentication Configuring Authorization Auditing Security Module 9: Monitoring and Troubleshooting Data Storage and Processing Data Engineering troubleshooting approach Azure Monitoring Capabilities Troubleshoot common data issues Troubleshoot common data processing issues Module 10: Integrating and Optimizing Data Platforms Integrating data platforms Optimizing data stores Optimize streaming data Manage disaster recovery [-]

The course includes full coverage of the core Azure IoT services such as IoT Hub, Device Provisioning Services, Azure Stream Analytics, Time Series Insights, and more. In addition to the focus on Azure PaaS services, the course includes sections on IoT Edge, device management, monitoring and troubleshooting, security concerns, and Azure IoT Central.   Topics covered: Create, configure, and manage an Azure IoT hub. Provision devices by using IoT Hub and DPS, including provisioning at scale. Establish secure 2-way communication between devices and IoT Hub. Implement message processing by using IoT Hub routing and Azure Stream Analytics. Configure the connection to Time Series Insights and support business integration requirements. Implement IoT Edge scenarios using marketplace modules and various edge gateway patterns. Implement IoT Edge scenarios that require developing and deploying custom modules and containers. Implement device management using device twins and direct methods. Implement solution monitoring, logging, and diagnostics testing. Recognize and address security concerns and implement Azure Security Center for IoT. Build an IoT Solution by using Azure IoT Central and recongize SaaS opportunities for IoT.   Module 1: Introduction to IoT and Azure IoT Services In this module, students will begin by examining the business considerations for various IoT implementations and reviewing how the Azure IoT Reference Architecture supports IoT solutions. This module also provides students with an overview of the Azure services commonly used in an IoT solution and provides an introduction to the Azure portal.Lessons Business Opportunities for IoT Introduction to IoT Solution Architecture IoT Hardware and Cloud Services Lab Scenarios for this Course   Lab: Getting Started with AzureLab : Setting Started with Azure IoT ServicesAfter completing this module, students will be able to: Explain how IoT and Azure IoT could be applied to their business Describe the core components of an Azure IoT Solution Architecture Describe the Azure IoT Services and how they relate to an IoT solution Create an Azure account and use the Azure portal to create an IoT Hub and DPS service   Module 2: Devices and Device Communication In this module, students will take a closer look at the Azure IoT Hub service and will learn how to configure secure two-way communication between IoT hub and devices. Students will also be introduced to IoT Hub features such as Device Twins and IoT Hub Endpoints that will be explored in more depth as the course continues.Lessons IoT Hub and Devices IoT Developer Tools Device Configuration and Communication   Lab: Setup the Development EnvironmentLab : Connect IoT Device to AzureAfter completing this module, students will be able to: Explain the core features of the IoT Hub services Describe the lifecycle of an Azure IoT device Describe how IoT Hub manages device identities and implements other security features Register devices with the IoT Hub using the Azure portal, Azure CLI, and Visual Studio Code Implement the IoT Hub Device and Service SDKs   Module 3: Device Provisioning at Scale In this module, students will focus on device provisioning and how to configure and manage the Azure Device Provisioning Service. Students will learn about the enrollment process, auto-provisioning and re-provisioning, disenrollment, and how to implement various attestation mechanisms.Lessons Device Provisioning Service Terms and Concepts Configure and Manage the Device Provisioning Service Device Provisioning Tasks   Lab: Individual Enrollment of Devices in DPSLab : Automatic Enrollment of Devices in DPSAfter completing this module, students will be able to: Explain the process of device provisioning and the features of the Device Provisioning Service Explain the security considerations associated with device provisioning and how they are managed Implement the Device Provisioning Service SDKs Manage the device enrollment process, including deprovisioning and disenrollment   Module 4: Message Processing and Analytics In this module, students will examine how IoT Hub and other Azure services can be used to process messages. Students will begin with an investigation of how to configure message and event routing and how to implement routing to built-in and custom endpoints. Students will learn about some of the Azure storage options that are common for IoT solutions. To round out his module, students will implement Azure Stream Analytics and queries for a number of ASA patterns.Lessons Messages and Message Processing Data Storage Options Azure Stream Analytics   Lab: Device Message RoutingLab : Filtering and Aggregating Message DataAfter completing this module, students will be able to: Configure message and event routing Route data to the built-in and custom endpoints Implement message enrichment Implement Azure Stream Analytics Inputs, Queries, and Outputs Store message data in a warm storage for historical purposes and additional analysis Use an Azure Function within a message processing and analytics solution   Module 5: Insights and Business Integration In this module, students will learn about the Azure services and other Microsoft tools that can be used to generate business insights and enable business integration. Students will implement Azure Logic Apps and Event Grid, and they will configure the connection and data transformations for data visualization tools such as Time Series Insights and Power BI.Lessons Business Integration for IoT Solutions Data Visualization with Time Series Insights Data Visualization with Power BI   Lab: Integrate IoT Hub with Event GridLab : Explore and Analyze Time Stamped Data with Time Series InsightsAfter completing this module, students will be able to: Explain the options for business integration within an IoT solution and how to achieve them Develop business integration support using Logic Apps and Event Grid Configure IoT Data for Visualization in Time Series Insights Configure IoT Data for Visualization in Power BI   Module 6: Azure IoT Edge Deployment Process In this module, students will learn how to deploy a module to an Azure IoT Edge device. Students will also learn how to configure and use an IoT Edge device as a gateway device.Lessons Introduction to Azure IoT Edge Edge Deployment Process Edge Gateway Devices   Lab: Introduction to IoT EdgeLab : Set Up an IoT Edge GatewayAfter completing this module, students will be able to: Describe the difference between an IoT device and an IoT Edge device Configure an IoT Edge device Implement an IoT Edge deployment using a deployment manifest Configure an IoT Edge device as a gateway device   Module 7: Azure IoT Edge Modules and Containers In this module, students will develop and deploy custom edge modules, and will implement support for an offline scenario that relies on local storage. Students will use Visual Studio Code to build custom modules as containers using a supported container engine.Lessons Develop Custom Edge Modules Offline and Local Storage   Lab: Develop, Deploy, and Debug a Custom Module on Azure IoT EdgeLab : Run an IoT Edge Device in Restricted Network and OfflineAfter completing this module, students will be able to: Explain the requirements for building a custom edge module Configure Visual Studio Code for developing containerized modules Deploy a custom module to an IoT Edge device Implement local storage on an IoT Edge device in support of an offline scenario   Module 8: Device Management In this module, students will learn how to implement device management for their IoT solution. Students will develop device management solutions that use devoice twins and solutions that use direct methods.Lessons Introduction to IoT Device Management Manage IoT and IoT Edge Devices Device Management at Scale   Lab: Remotely Monitor and Control Devices with Azure IoT HubLab : Automatic Device ManagementAfter completing this module, students will be able to: Describe the most common device management patterns and configuration best practices Describe when and how to use device twins and direct methods to implement device management Implement device management for various patterns using device twins and direct methods Implement device management at scale using automatic device management and jobs   Module 9: Solution Testing, Diagnostics, and Logging In this module, students will configure logging and diagnostic tools that help developers to test their IoT solution. Students will use IoT Hub and Azure Monitor to configure alerts and track conditions such as device connection state that can be used to troubleshoot issues.Lessons Monitoring and Logging Troubleshooting   Lab: Configure Metrics and Logs in Azure IoT HubLab : Monitor and Debug Connection FailuresAfter completing this module, students will be able to: Describe the options for monitoring and logging an Azure IoT solution Configure Azure Monitor to support of an IoT solution Configure IoT Hub Metrics to support of an IoT solution Implement diagnostics logging Troubleshoot IoT device connection and communication issues Module 10: Azure Security Center and IoT Security Considerations In this module, students will examine the security considerations that apply to an IoT solution. Students will begin by investigating security as it applies to the solution architecture and best practices, and then look at how Azure Security Center for IoT supports device deployment and IoT Hub integration. Students then use Azure Security Center for IoT Agents to enhance the security of their solution.Lessons Security Fundamentals for IoT Solutions Introduction to Azure Security Center for IoT Enhance Protection with Azure Security Center for IoT Agents   Lab: Implementing Azure Security Center for IoTAfter completing this module, students will be able to: Describe security concerns and best practices for an IoT solution Describe the Azure IoT Security Architecture and Threat Modeling Describe the features and support provided by Azure Security Center for IoT Configure Security Agents and Security Module Twins Aggregate Azure Security Center for IoT Events   Module 11: Build an IoT Solution with IoT Central In this module, students will learn how configure and implement Azure IoT Central as a SaaS solution for IoT. Students will begin with a high-level investigation of IoT Central and how it works. With a basic understanding of IoT central establish, students will move on to creating and managing device templates, and then managing devices in their IoT Central application.Lessons Introduction to IoT Central Create and Manage Device Templates Manage Devices in Azure IoT Central   Lab: Get Started with Azure IoT CentralLab : Implementing IoT Solutions with Azure IoT CentralAfter completing this module, students will be able to: Describe the difference between Azure IoT Central and the Azure IoT PaaS services Describe the features provided by Azure IoT Central Describe the purpose and components of a Device Template Create and publish a Device Template Manage devices using rules and notifications Mange devices at scale using jobs     [-]

Flere oppgir å slite med psykiske problemer knyttet til vanskelig liv og oppvekst. Hvordan kan du som lærer forstå mer og snakke om psykisk helse med elever? Dette nettkurset tar sikte på å styrke deg i møtet med utsatte elever som sliter med depresjon, angst og mobbing og på den måten forhindre utenforskap og isolasjon. Kurset gir deg også kunnskap om sårbarhetsfaktorer, økt emosjonell kompetanse og råd til hvordan du kan styrke og engasjere eleven i målet om å få det bedre med seg selv. I løpet av kurset vil du få en innføring i hvordan du kan ha gode og støttende samtaler, ta utgangspunkt i caser og gjennom dette gi deg økt kompetanse og styrke deg i ditt arbeid med å skape et mer åpent og inkluderende mangfoldsmiljø i skolen. Kursholder Anders Røyneberg er psykiatrisk sykepleier og sexologisk rådgiver. Han jobber som terapeut ved Institutt for klinisk sexologi og terapi. Anders skriver også i spalten Si;D i Aftenposten og underviser ved OsloMet.  [-]

Vasking av lister Fjerne duplikater Tette hull i lister Dele kolonner Power Query Import av data Enkel behandling av data Bruk av data fra Power Query i Excel Pivottabeller i Excel Ulike kilder Effektiv bruk av pivot Beregninger: Akkumulering, prosentfordeling osv PowerPivot og Datamodell Laste data inn i Datamodell Lage ulike pivottabeller og pivotdiagram fra Datamodell Koble ulike tabeller Bruk av kalendertabell Nyttige funksjoner Excel til bruk ved rapportarbeid FinnRad, FinnKolonne, Indeks Sammenligne Forskyvning Dashboard og meny Lage hovedside men meny og slicers Bruk av Stiler og Tema Samle relevant informasjon til et sted [-]

VAL FoU og Sikkerhetssenteret Rørvik AS har utviklet e-læringskurs for havbruksnæringa. Kurset er lovpålagt og dekker lov- og forskriftsfestede kompetansekrav for laksefisk. Fiskevelferd Laksens naturlige behov og adferd Regelverk Velferdsindikator Stress og stressfaktorer Forebyggende helsearbeid Helseutfordringer og sykdom Fiskevelferd ved slakting Miljø og produksjonssystemer   Kurset er utviklet- og holdes av fiskehelsepersonell som jobber i havbruksnæringen.  [-]

Målgruppe Montører, lærlinger, elektroentreprenører, saksbehandlere, rådgivende ingeniører, tilsynsingeniører, elektropersonell i industrien og elektrolærere Kursinnhold Maskindirektivet/Forskrift om maskiner/ NEK EN 60204-1 Hva er en maskin? Hvor går grensesnittet mellom en installasjon og en maskin Gjennomgang av sentrale paragrafer i Forskrift om maskiner Gjennomgang av sentrale deler av NEK EN 60204-1:2018 Ombygging, endringer og vedlikehold av maskiner Krav til kvalifikasjoner Varighet6 timer [-]

In this course, you will learn the most common DevOps patterns to develop, deploy, and maintain applications on the AWS platform. We will explore the core principles of the DevOps methodology and examine a number of use cases applicable to startup, small- to medium-sized business, and enterprise development scenarios. Course objectives: In this course, you will learn to: Use the principal concepts and practices behind the DevOps methodology Design and implement an infrastructure on AWS that supports one or more DevOps development projects Use AWS CloudFormation and AWS OpsWorks to deploy the infrastructure necessary to create development, test, and production environments for a software development project Use AWS CodeCommit and AWS CodeBuild to understand the array of options for enabling a continuous integration (CI) environment on AWS Use AWS CodePipeline to design and implement a continuous integration and continuous delivery (CI/CD) pipeline on AWS Use AWS CodeStar to manage all software development activities in one place Implement several common continuous deployment (CD) use cases using AWS technologies, including blue/green deployment and A/B testing Distinguish between the array of application deployment technologies available on AWS, including AWS CodeDeploy, AWS OpsWorks, AWS Elastic Beanstalk, Amazon Elastic Container Service (ECS), and Amazon Elastic Container Registry (ECR), and decide which technology best fits a given scenario Use Amazon EC2 Systems Manager for patch management Leverage automated testing in different stages of a CI/CD pipeline Fine-tune the applications you deliver on AWS for high performance, and use AWS tools and technologies to monitor your application and environment for potential issues   Delivery Method Instructor-Led Training (ILT) Hands-on Labs   Course outline Day 1 Introduction to DevOps AWS Command Line Interface Introduction to DevSecOps Deployment Strategies and Developer Tools Day 2 Infrastructure as Code Deep Dive into AWS Developer Tools Automated Testing on AWS Day 3 Configuration Management AMI Building and Amazon EC2 Systems Manager Containers: Docker and Amazon ECS DevOps Customer Case Studies Course Wrap-Up [-]

Besides covering the basics of HTML, CSS and JavaScript, we’ll also deep dive into each of those technologies. You’ll learn to create sites that dynamically adjust to screen sizes with use of CSS Flexbox, CSS Grid, relative font sizes and responsive images. How to have a normal website and also let it appear as a traditional application or native mobile application? Want to write apps that are powered by Web technologies and delivered with Web infrastructure? Welcome to Progressive Web Apps. PWAs are secure and installable, can run offline and can display system notifications even when the app is not running. Accessibility and Video will be covered extensively. Finally you’ll learn how to measure and optimise performance, as well as debugging and testing. Basically this course will teach the most popular and successful web technologies in use today. Course will be updated regularly to keep up with the latests developments.   Course content: ECMAScript 2018 and beyond (JavaScript) We’ll cover basic syntax as well as useful new additions to the language. Most notable: Variable declarations with let, var and const; Class syntax; Modules; Template strings; Arrow functions; Default parameters in functions; for..of loops; Promises and async/await asynchronous function handling; Destructuring; Rest and Spread operators for quick array and object manipulations; Maps (key-value pairs) and Sets (unique values). Layout CSS Flexbox (1 dimension layout) and CSS Grid (2 dimension layout) will be covered. The Grid allows for layouts that were previously impossible or could only be achieved with JavaScript. Grid also surpasses Bootstrap in possibilities, flexibility and has a simpler markup. Flexible layouts depending on the screen capabilities are covered. HTML 5.2 and beyond Basics like the Document Object Model (DOM) and manipulation thereof as well as the working of the event-system are covered. We’ll dive deeper into handling both touch and mouse event to work with all types of devices, not only desktops. HTML Forms input types and validation methods are covered. CSS (Snapshot 2017 and beyond) Explaining CSS precedence. Both well known and the latests selectors, pseudo classes and pseudo elements are discussed as well as style attributes. Techniques to adjust to the screen size are responsive images as well as Web Fonts and flexible font sizes. Progressive Web Apps Progressive Web Apps (PWA) is a set of technologies you can use to enhance a website with application behaviour: Installable: The website can be added to the home screen of the device. Web App Manifest: Defining the icon, home screen title, splash screen, background color and home page. Fetch: Simple and powerful API to fetch resources (successor of XMLHttpRequest). Offline and online behaviour: No matter if there is a network connection, the app will work using Service Workers to control requests and storing responses to requests offline using the Cache API, Web Storage or IndexedDB. Notifications. Send updates from the server to the app with the Push API and use the Notification API to generate system notifications even if the app is not running. Safe: Only https (or localhost for development). Progressive: Use the above technologies if they are available, but the page (app) will work if the device’s browser is less capable. PWA can even be published to the Microsoft Store and become Windows Apps. Storage Data storage within the browser are covered with 3 techniques: Storage API, Local & Session storage when you don’t want to store too much data. IndexedDB API (version 2), is an object database with more capacity. Cache API for use with Progressive Web Apps. Video & Audio Answers: How to add video & audio elements? How to create subtitles and picture-in-picture video? Accessibility Explaining techniques to keep your designs accessible for the visual impaired. Performance Subjects: Compression and minification of images, scripts, css, etc. To inline or not to inline? Async loading of scripts. How to measure performance? Testing Browsers offer support to test on devices with different capabilities like screen sizes and connection speeds. We’ll also cover writing JavaScript unit tests. Debugging Explaining how to use the developer tools in the browser: Setting breakpoints in code or when events happen Using the console Dynamically making changes for immediate feedback Debugging CSS specificity and computed styles. [-]

Gain the necessary knowledge about how to use Azure services to develop, train and deploy machine learning solutions. The course starts with an overview of Azure services that support data science. From there, it focuses on using Azure's premier data science service, Azure Machine Learning service, to automate the data science pipeline.   IMPORTANT NOTICE! This course is focused on Azure and does not teach the student how to do data science. It is assumed students already know that.   Course content Module 1: Doing Data Science on Azure Introduce the Data Science ProcessOverview of Azure Data Science OptionsIntroduce Azure Notebooks Module 2: Doing Data Science with Azure Machine Learning service Introduce Azure Machine Learning (AML) serviceRegister and deploy ML models with AML service Module 3: Automate Machine Learning with Azure Machine Learning service Automate Machine Learning Model SelectionAutomate Hyperparameter Tuning with HyperDrive Module 4: Manage and Monitor Machine Learning Models with the Azure Machine Learning service Manage and Monitor Machine Learning Models [-]