Oversikt over relevante kurs innen IT-kurs i Graubünden

Understand the purpose and key concepts of the Service Level Management Practice, elucidating its significance in defining, negotiating, and managing service levels to meet customer expectations. This eLearning is: Interactive Self-paced   Device-friendly   2-3 hours content   Mobile-optimised   Practical exercises   Exam: 20 questions Multiple choise 30 minutes Closed book Minimum required score to pass: 65% [-]

AZ-1008: Administer Active Directory Domain Services [-]

Understand the purpose and key concepts of Deployment Management, highlighting its importance in managing the deployment of new or changed services into the live environment. This eLearning is: Interactive Self-paced   Device-friendly   2-3 hours of content   Mobile-optimised   Exam: 20 questions Multiple choise 30 minutes Closed book Minimum required score to pass: 65% [-]

Understand the key concepts of Relationship Management, Supplier Management, Service Level Management, Continual Improvement, and Information Security Management, elucidating their significance in fostering collaboration, ensuring service quality, driving continual improvement, and maintaining information security. This eLearning is: Interactive Self-paced   Device-friendly   12 hours content   Mobile-optimised   Practical exercises   Exam: 60 questions Multiple choise 90 minutes Closed book Minimum required score to pass: 65% [-]

Blended Virtual CourseThe course is a hybrid of virtual training and self-study which will be a mixture of teaching using Microsoft Teams for short bursts at the beginning of the day, then setting work for the rest of the day and then coming back at the end of the day for another on-line session for any questions before setting homework in the form of practice exams for the evening. You do not have to install Microsoft Teams, you will receive a link and can access the course using the web browser.  Remote proctored examTake your exam from any location. Read about iSQI remote proctored exam here Requirements for the exam: The exam will be using Google Chrome and there is a plug-in that needs to be installed  You will need a laptop/PC with a camera and a microphone  A current ID with a picture    KursinnholdDette kurset forklarer det grunnleggende i softwaretesting. Det er basert på ISTQB- pensum og er akkreditert av BCS.    Kurset inneholder øvelser, prøveeksamener og spill for å fremheve sentrale deler av pensum. Dette sammen med kursmateriell og presentasjoner vil bistå i forståelse av begreper og metoder som blir presentert.   Bouvet sine kursdeltakeres testresultater vs ISTQB gjennomsnitt   «Særs godt kurs med mye fokus på praktiske oppgaver som gjør læring vesentlig lettere. Engasjert kursleder hjelper også. Kursleder starter på et nivå som alle føler seg komfortabel med.» Alexander Røstum Course content Fundamentals of Testing This section looks at why testing is necessary, what testing is, and explains general testing principles, the fundamental test process, and psychological aspects of testing.   Skills Gained • Learn about the differences between the testing levels and targets• Know how to apply both black and white box approaches to all levels of testing• Understand the differences between the various types of review and be aware of Static Analysis• Learn aspects of test planning, estimation, monitoring and control• Communicate better through understanding standard definitions of terms• Gain knowledge of the different types of testing tools and the best way of implementing those tools   Testing throughout the software lifecycle Explains the relationship between testing and life cycle development models, including the V-model and iterative development. Outlines four levels of testing:• Component testing• Integration testing• System testing• Acceptance testing Describes four test types, the targets of testing:• functional• non-functional characteristics• structural• change-related Outlines the role of testing in maintenance.   Static Techniques Explains the differences between the various types of review, and outlines the characteristics of a formal review. Describes how static analysis can find defects.   Test Design Techniques This section explains how to identify test conditions (things to test) and how to design test cases and procedures. It also explains the difference between white and black box testing. The following techniques are described in some detail with practical exercises :• Equivalence Partitioning• Boundary Value Analysis• Decision Tables• State Transition testing• Statement and Decision testingIn addition, use case testing and experience-based testing (such as exploratory testing) are described, and advice is given on choosing techniques.   Test Management This section looks at organisational implications for testing and describes test planning and estimation, test monitoring and control. The relationship of testing and risk is covered,and configuration management and incident management.   Tool Support for Testing Different types of tool support for testing are described throughout the course. This session summarises them, and discusses how to use them effectively and how best to introduce a new tool.   The Exam The ISTQB Foundation exam is a 1-hour, 40 question multiple choice exam. There is an extra 15 minutes allowed for candidates whose first language is not English.The pass mark is 65% (26/40) and there are no pre requisites to taking this exam.The exam is a remote proctored exam [-]

TARGET AUDIENCE Software engineers concerned with building, managing and deploying AI solutions that leverage Azure Cognitive Services, Azure Cognitive Search, and Microsoft Bot Framework. They are familiar with C#, Python, or JavaScript and have knowledge on using REST-based APIs to build computer vision, language analysis, knowledge mining, intelligent search, and conversational AI solutions on Azure. COURSE OBJECTIVES After completing this course you should be able to: Describe considerations for creating AI-enabled applications Identify Azure services for AI application development Provision and consume cognitive services in Azure Manage cognitive services security Monitor cognitive services Use a cognitive services container Use the Text Analytics cognitive service to analyze text Use the Translator cognitive service to translate text Use the Speech cognitive service to recognize and synthesize speech Use the Speech cognitive service to translate speech Create a Language Understanding app Create a client application for Language Understanding Integrate Language Understanding and Speech Use QnA Maker to create a knowledge base Use a QnA knowledge base in an app or bot Use the Bot Framework SDK to create a bot Use the Bot Framework Composer to create a bot Use the Computer Vision service to analyze images Use Video Indexer to analyze videos Use the Custom Vision service to implement image classification Use the Custom Vision service to implement object detection Detect faces with the Computer Vision service Detect, analyze, and recognize faces with the Face service Use the Computer Vision service to read text in images and documents Use the Form Recognizer service to extract data from digital forms Create an intelligent search solution with Azure Cognitive Search Implement a custom skill in an Azure Cognitive Search enrichment pipeline Use Azure Cognitive Search to create a knowledge store   COURSE CONTENT Module 1: Introduction to AI on Azure Artificial Intelligence (AI) is increasingly at the core of modern apps and services. In this module, you'll learn about some common AI capabilities that you can leverage in your apps, and how those capabilities are implemented in Microsoft Azure. You'll also learn about some considerations for designing and implementing AI solutions responsibly. Introduction to Artificial Intelligence Artificial Intelligence in Azure Module 2: Developing AI Apps with Cognitive Services Cognitive Services are the core building blocks for integrating AI capabilities into your apps. In this module, you'll learn how to provision, secure, monitor, and deploy cognitive services. Getting Started with Cognitive Services Using Cognitive Services for Enterprise Applications Lab: Get Started with Cognitive Services Lab: Get Started with Cognitive Services Lab: Monitor Cognitive Services Lab: Use a Cognitive Services Container Module 3: Getting Started with Natural Language Processing  Natural Language processing (NLP) is a branch of artificial intelligence that deals with extracting insights from written or spoken language. In this module, you'll learn how to use cognitive services to analyze and translate text. Analyzing Text Translating Text Lab: Analyze Text Lab: Translate Text Module 4: Building Speech-Enabled Applications Many modern apps and services accept spoken input and can respond by synthesizing text. In this module, you'll continue your exploration of natural language processing capabilities by learning how to build speech-enabled applications. Speech Recognition and Synthesis Speech Translation Lab: Recognize and Synthesize Speech Lab: Translate Speech Module 5: Creating Language Understanding Solutions To build an application that can intelligently understand and respond to natural language input, you must define and train a model for language understanding. In this module, you'll learn how to use the Language Understanding service to create an app that can identify user intent from natural language input. Creating a Language Understanding App Publishing and Using a Language Understanding App Using Language Understanding with Speech Lab: Create a Language Understanding App Lab: Create a Language Understanding Client Application Use the Speech and Language Understanding Services Module 6: Building a QnA Solution One of the most common kinds of interaction between users and AI software agents is for users to submit questions in natural language, and for the AI agent to respond intelligently with an appropriate answer. In this module, you'll explore how the QnA Maker service enables the development of this kind of solution. Creating a QnA Knowledge Base Publishing and Using a QnA Knowledge Base Lab: Create a QnA Solution Module 7: Conversational AI and the Azure Bot Service Bots are the basis for an increasingly common kind of AI application in which users engage in conversations with AI agents, often as they would with a human agent. In this module, you'll explore the Microsoft Bot Framework and the Azure Bot Service, which together provide a platform for creating and delivering conversational experiences. Bot Basics Implementing a Conversational Bot Lab: Create a Bot with the Bot Framework SDK Lab: Create a Bot with a Bot Freamwork Composer Module 8: Getting Started with Computer Vision Computer vision is an area of artificial intelligence in which software applications interpret visual input from images or video. In this module, you'll start your exploration of computer vision by learning how to use cognitive services to analyze images and video. Analyzing Images Analyzing Videos Lab: Analyse Images with Computer Vision Lab: Analyze Images with Video Indexer Module 9: Developing Custom Vision Solutions While there are many scenarios where pre-defined general computer vision capabilities can be useful, sometimes you need to train a custom model with your own visual data. In this module, you'll explore the Custom Vision service, and how to use it to create custom image classification and object detection models. Image Classification Object Detection Lab: Classify Images with Custom Vision Lab: Detect Objects in Images with Custom Vision Module 10: Detecting, Analyzing, and Recognizing Faces Facial detection, analysis, and recognition are common computer vision scenarios. In this module, you'll explore the user of cognitive services to identify human faces. Detecting Faces with the Computer Vision Service Using the Face Service Lab:Destect, Analyze and Recognize Faces Module 11: Reading Text in Images and Documents Optical character recognition (OCR) is another common computer vision scenario, in which software extracts text from images or documents. In this module, you'll explore cognitive services that can be used to detect and read text in images, documents, and forms. Reading text with the Computer Vision Service Extracting Information from Forms with the Form Recognizer service Lab: Read Text in IMages Lab: Extract Data from Forms Module 12: Creating a Knowledge Mining Solution Ultimately, many AI scenarios involve intelligently searching for information based on user queries. AI-powered knowledge mining is an increasingly important way to build intelligent search solutions that use AI to extract insights from large repositories of digital data and enable users to find and analyze those insights. Implementing an Intelligent Search Solution Developing Custom Skills for an Enrichment Pipeline Creating a Knowledge Store Lab: Create and Azure Cognitive Search Solution Create a Custom Skill for Azure Cognitive Search Create a Knowledge Store with Azure Cognitive Search   TEST CERTIFICATION Recommended as preparation for the following exams: AI-102 - Designing and Implementing a Microsoft Azure AI Solution - Part of the requirements for the Microsoft Certified Azure AI Engineer Associate Certification.   HVORFOR VELGE SG PARTNER AS:  Flest kurs med Startgaranti Rimeligste kurs Beste service og personlig oppfølgning Tilgang til opptak etter endt kurs Partner med flere av verdens beste kursleverandører [-]

The students will also explore how to design data security including data access, data policies and standards. They will also design Azure data solutions which includes the optimization, availability and disaster recovery of big data, batch processing and streaming data solutions. Agenda Module 1: Data Platform Architecture Considerations. -Core Principles of Creating Architectures-Design with Security in Mind-Performance and Scalability-Design for availability and recoverability-Design for efficiency and operations-Case Study Module 2: Azure Batch Processing Reference Architectures. -Lambda architectures from a Batch Mode Perspective-Design an Enterprise BI solution in Azure-Automate enterprise BI solutions in Azure-Architect an Enterprise-grade Conversational Bot in Azure Module 3: Azure Real-Time Reference Architectures. -Lambda architectures for a Real-Time Perspective-Lambda architectures for a Real-Time Perspective-Design a stream processing pipeline with Azure Databricks-Create an Azure IoT reference architecture Module 4: Data Platform Security Design Considerations. -Defense in Depth Security Approach-Network Level Protection-Identity Protection-Encryption Usage-Advanced Threat Protection Module 5: Designing for Resiliency and Scale. -Design Backup and Restore strategies-Optimize Network Performance-Design for Optimized Storage and Database Performance-Design for Optimized Storage and Database Performance-Incorporate Disaster Recovery into Architectures-Design Backup and Restore strategies Module 6: Design for Efficiency and Operations. -Maximizing the Efficiency of your Cloud Environment-Use Monitoring and Analytics to Gain Operational Insights-Use Automation to Reduce Effort and Error [-]

AI-102: Designing and Implementing a Microsoft Azure AI Solution [-]

Lightroom CC er et råflott bilderedigeringsverktøy for fotoentusiaster. Lightroom CC inneholder alt du trenger for å organisere, redigere, lagre og dele bildene dine på tvers av enheter - dette være seg datamaskin, nettbrett eller mobil. Det betyr at du kan redigere et bilde på datamaskinen og fortsette på mobilen. Bildene synkroniseres nemlig i skyen. I dette kurset kommer Espen Faugstad til å guide deg gjennom programmet fra A til Å. Du kommer til å lære å importere og organisere, redigere ved hjelp av enkle og avanserte verktøy, og eksportere og dele. Du kommer også til å lære hvordan den skybaserte lagringsplassen kommer til å påvirke, og ikke minst, forbedre din digitale arbeidsflyt.   Innhold: Kapittel 1: Introduksjon Kapittel 2: Importere Kapittel 3: Organisere Kapittel 4: Redigere (enkel) Kapittel 5: Beskjære Kapittel 6: Redigere (avansert) Kapittel 7: Eksportere Kapittel 8: Avslutning   Varighet: 2 timer og 16 minutter.   Hørt om Netflix? Vi er som dem, bare at vi lager nettkurs. Utdannet.no AS er en norsk startup som utvikler nettkurs i datateknologi, kreative fagfelt og grunnleggende forretningsferdigheter. Med støtte fra Innovasjon Norge og Forskningsrådet utvikler vi nestegenerasjons kursplattform, med mål om å gjøre kompetanseutvikling moro, spennende og tilgjengelig for alle. Med over 1 million videovisninger, 20.000 registrerte medlemmer og en gjennomsnittlig årlig vekst på 45 % er vi godt i gang med å befeste vår posisjon i det norske markedet. Vi har kunder fra bedrifter som: Adresseavisen, Coca-Cola, Helsedirektoratet, IKEA, Joblearn, NAV, Nordea, NorgesGruppen, NRK, Oslo kommune, Securitas, Telenor og Utdanningsforbundet.   [-]

Vue.js, Vuex & Router Course [-]

Dette kurset er innrettet mot dem som jobber med økonomisk oppfølging i bedriften. Vi går inn på prosessene fra innhenting av data, bearbeidelse av dataene, sammendrag og analyse av dataene, og til sist rapportering av dataene til bedriftens beslutningstagere. Vi bruker en god del tid på Pivot og Power Pivot her, men vi går ikke fullt så langt som i spesialkurset om Pivottabeller. Kurset forutsetter at man er godt kjent i Excel, og vant til å jobbe med litt kompliserte problemstillinger i Excel. Kontroll/gjennomgang av en del sentral funksjonalitet – bl.a. absolutte, relative og blandede referanser. Sammendrag av data fra flere ark i samme eller flere arbeidsbøker, bl.a. gjennomgående summering og tabulering v.hj.a. INDIREKTE-funksjonen. Betingende sammendrag v.hj.a. matriseformler og funksjoner Sentrale funksjoner, bl.a. HVIS, HVISFEIL, FINN.RAD, FINN.KOLONNE, ANTALL.HVIS, etc. Sammendrag av data med Pivottabell Power Pivot Formler Rapportering av data Statiske rapporter Rapporter med interaktivitet, forskjellige teknikker Visualisering av tallene Dashboard Aktuelle teknikker for å lage dashboards Avstemming av to eller flere lister mot hverandre, f.ex. bank Lister – verktøy i Excel som er aktuelle når vi jobber med lister Makroer/VBA – introduksjon til automatisering [-]

GDPR - Certified Data Protection Officer [-]

Utforsk Adobe Bridge til fulle med kurset "Bridge: Komplett" ledet av Espen Faugstad hos Utdannet.no. Adobe Bridge er et kraftig verktøy for å importere, organisere og vise digitale bilder, og er en viktig del av Creative Cloud-pakken. Dette kurset er designet for alle som ønsker å lære Adobe Bridge fra grunnen av, og ingen forkunnskaper er nødvendig. Du vil lære hvordan du effektivt importerer og organiserer bilder, rangerer og presenterer dem. Kurset vil gi deg en dyp forståelse av hvordan forskjellige paneler i Bridge, som Content-panelet, Filter-panelet, Collections-panelet, og Metadata-panelet, fungerer i praksis. Gjennom kurset vil du få praktisk erfaring med å bruke Bridge for å forbedre din arbeidsflyt og bildehåndtering. Ved slutten av kurset vil du ha oppnådd en omfattende forståelse av Adobe Bridge, noe som gjør deg i stand til å bruke programmet effektivt, enten du jobber alene eller sammen med andre Adobe-programmer som Photoshop.   Innhold: Kapittel 1: Introduksjon Kapittel 2: Grunnleggende Kapittel 3: Viderekommen Kapittel 4: Avslutning   Varighet: 1 time   Om Utdannet.no: Utdannet.no tilbyr noen av landets beste digitale nettkurs. Tjenesten fungerer på samme måte som strømmetjenester for musikk eller TV-serier. Våre kunder betaler en fast månedspris og får tilgang til alle kursene som er produsert så langt. Plattformen har hatt en god vekst de siste årene og kan skilte med 30.000 registrerte brukere og 1,5 millioner videoavspillinger. Vårt mål er å gjøre kompetanseutvikling moro, spennende og tilgjengelig for alle – og med oss har vi Innovasjon Norge og Forskningsrådet. [-]

Objectives This course teaches participants the following skills: Design and build data processing systems on Google Cloud Platform Process batch and streaming data by implementing autoscaling data pipelines on Cloud Dataflow Derive business insights from extremely large datasets using Google BigQuery Train, evaluate, and predict using machine learning models using Tensorflow and Cloud ML Leverage unstructured data using Spark and ML APIs on Cloud Dataproc Enable instant insights from streaming data   All courses will be delivered in partnership with ROI Training, Google Cloud Premier Partner, using a Google Authorized Trainer. Course Outline Module 1: Introduction to Data Engineering -Explore the role of a data engineer-Analyze data engineering challenges-Intro to BigQuery-Data Lakes and Data Warehouses-Demo: Federated Queries with BigQuery-Transactional Databases vs Data Warehouses-Website Demo: Finding PII in your dataset with DLP API-Partner effectively with other data teams-Manage data access and governance-Build production-ready pipelines-Review GCP customer case study-Lab: Analyzing Data with BigQuery Module 2: Building a Data Lake -Introduction to Data Lakes-Data Storage and ETL options on GCP-Building a Data Lake using Cloud Storage-Optional Demo: Optimizing cost with Google Cloud Storage classes and Cloud Functions-Securing Cloud Storage-Storing All Sorts of Data Types-Video Demo: Running federated queries on Parquet and ORC files in BigQuery-Cloud SQL as a relational Data Lake-Lab: Loading Taxi Data into Cloud SQL Module 3: Building a Data Warehouse -The modern data warehouse-Intro to BigQuery-Demo: Query TB+ of data in seconds-Getting Started-Loading Data-Video Demo: Querying Cloud SQL from BigQuery-Lab: Loading Data into BigQuery-Exploring Schemas-Demo: Exploring BigQuery Public Datasets with SQL using INFORMATION_SCHEMA-Schema Design-Nested and Repeated Fields-Demo: Nested and repeated fields in BigQuery-Lab: Working with JSON and Array data in BigQuery-Optimizing with Partitioning and Clustering-Demo: Partitioned and Clustered Tables in BigQuery-Preview: Transforming Batch and Streaming Data Module 4: Introduction to Building Batch Data Pipelines -EL, ELT, ETL-Quality considerations-How to carry out operations in BigQuery-Demo: ELT to improve data quality in BigQuery-Shortcomings-ETL to solve data quality issues Module 5: Executing Spark on Cloud Dataproc -The Hadoop ecosystem-Running Hadoop on Cloud Dataproc-GCS instead of HDFS-Optimizing Dataproc-Lab: Running Apache Spark jobs on Cloud Dataproc Module 6: Serverless Data Processing with Cloud Dataflow -Cloud Dataflow-Why customers value Dataflow-Dataflow Pipelines-Lab: A Simple Dataflow Pipeline (Python/Java)-Lab: MapReduce in Dataflow (Python/Java)-Lab: Side Inputs (Python/Java)-Dataflow Templates-Dataflow SQL Module 7: Manage Data Pipelines with Cloud Data Fusion and Cloud Composer -Building Batch Data Pipelines visually with Cloud Data Fusion-Components-UI Overview-Building a Pipeline-Exploring Data using Wrangler-Lab: Building and executing a pipeline graph in Cloud Data Fusion-Orchestrating work between GCP services with Cloud Composer-Apache Airflow Environment-DAGs and Operators-Workflow Scheduling-Optional Long Demo: Event-triggered Loading of data with Cloud Composer, Cloud Functions, -Cloud Storage, and BigQuery-Monitoring and Logging-Lab: An Introduction to Cloud Composer Module 8: Introduction to Processing Streaming Data Processing Streaming Data Module 9: Serverless Messaging with Cloud Pub/Sub -Cloud Pub/Sub-Lab: Publish Streaming Data into Pub/Sub Module 10: Cloud Dataflow Streaming Features -Cloud Dataflow Streaming Features-Lab: Streaming Data Pipelines Module 11: High-Throughput BigQuery and Bigtable Streaming Features -BigQuery Streaming Features-Lab: Streaming Analytics and Dashboards-Cloud Bigtable-Lab: Streaming Data Pipelines into Bigtable Module 12: Advanced BigQuery Functionality and Performance -Analytic Window Functions-Using With Clauses-GIS Functions-Demo: Mapping Fastest Growing Zip Codes with BigQuery GeoViz-Performance Considerations-Lab: Optimizing your BigQuery Queries for Performance-Optional Lab: Creating Date-Partitioned Tables in BigQuery Module 13: Introduction to Analytics and AI -What is AI?-From Ad-hoc Data Analysis to Data Driven Decisions-Options for ML models on GCP Module 14: Prebuilt ML model APIs for Unstructured Data -Unstructured Data is Hard-ML APIs for Enriching Data-Lab: Using the Natural Language API to Classify Unstructured Text Module 15: Big Data Analytics with Cloud AI Platform Notebooks -What’s a Notebook-BigQuery Magic and Ties to Pandas-Lab: BigQuery in Jupyter Labs on AI Platform Module 16: Production ML Pipelines with Kubeflow -Ways to do ML on GCP-Kubeflow-AI Hub-Lab: Running AI models on Kubeflow Module 17: Custom Model building with SQL in BigQuery ML -BigQuery ML for Quick Model Building-Demo: Train a model with BigQuery ML to predict NYC taxi fares-Supported Models-Lab Option 1: Predict Bike Trip Duration with a Regression Model in BQML-Lab Option 2: Movie Recommendations in BigQuery ML Module 18: Custom Model building with Cloud AutoML -Why Auto ML?-Auto ML Vision-Auto ML NLP-Auto ML Tables [-]

Dette nettkurset fokuserer på objektorientert programmering (OOP) i Python og er ideelt for de som allerede har grunnleggende ferdigheter i Python og ønsker å utvide sine kunnskaper. OOP gir deg muligheten til å skrive kode som er mer strukturert, gjenbrukbar og enklere å vedlikeholde. Kurset, ledet av erfaren systemutvikler og instruktør Magnus Kvendseth Øye, vil veilede deg gjennom nøkkelkonsepter innen OOP i Python. I løpet av kurset vil du lære å se på koden din som en samling av dynamiske objekter som samhandler med hverandre. Du vil utforske følgende emner: Kapittel 1: Introduksjon Kapittel 2: Klasser og egenskaper Kapittel 3: Metoder Kapittel 4: Representasjon Kapittel 5: Arv Kapittel 6: Prosjekt Kapittel 7: Avslutning Med Magnus Kvendseth Øye som din veileder, vil du få en solid forståelse av hvordan du kan bruke OOP-prinsipper i Python for å skape ren, effektiv og strukturert kode. Dette kurset gir deg muligheten til å ta dine Python-ferdigheter til neste nivå.   Varighet: 3 timer og 8 minutter   Om Utdannet.no: Utdannet.no tilbyr noen av landets beste digitale nettkurs. Vår tjeneste fungerer på samme måte som strømmetjenester for musikk eller TV-serier, der våre kunder betaler en fast månedspris for tilgang til alle kursene vi har tilgjengelig. Vi har opplevd betydelig vekst de siste årene, med over 30 000 registrerte brukere og 1,5 millioner videoavspillinger. Vårt mål er å gjøre kompetanseutvikling engasjerende, spennende og tilgjengelig for alle, og vi har støtte fra Innovasjon Norge og Forskningsrådet. [-]