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Nettkurs 12 måneder 9 000 kr
ITIL® 4 Specialist: Create, Deliver and Support dekker «kjernen» i ITIL®, aktiviteter rundt administrasjon av tjenester, og utvider omfanget av ITIL® til å omfatte «oppre... [+]
Kurset fokuserer på integrering av forskjellige verdistrømmer og aktiviteter for å lage, levere og støtte IT-aktiverte produkter og tjenester, samtidig som den dekker støtte for praksis, metoder og verktøy. Kurset gir kandidatene forståelse for tjenestekvalitet og forbedringsmetoder. E-læringskurset inneholder 18 timer med undervisning, og er delt inn i 8 moduler. Les mer om ITIL® 4 på AXELOS sine websider. Inkluderer: Tilgang til ITIL® 4 Specialist: Create, Deliver and Support e-læring (engelsk) i 12 måneder. ITIL® 4 Specialist: Create, Deliver and Support online voucher til sertifiseringstest.   ITIL®/PRINCE2®/MSP®/MoP® are registered trademarks of AXELOS Limited, used under permission of AXELOS Limited. All rights reserved. [-]
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Nettkurs 1 dag 3 800 kr
Lær å bruke Google Analytics (GA) for å få innsikt i trafikk og aktivitet på ditt nettsted. Webanalyse er essensielt for alle som ønsker å utvikle og forbedre digitale lø... [+]
I dette kurset kombinerer vi teori med praksis. Gjennom relevante oppgaver får du forståelse og ferdigheter til å trekke ut data og gjøre analyser av hva som skjer på ditt nettsted. Du vil lære hvordan du kan måle effekt av endringer i løsningen, design og markedsføringstiltak. Google Analytics gir deg det datagrunnlaget du trenger for å lage rapporter og analyser for en faktabasert forståelse av hvordan den digitale løsningen fungerer.  Etter kurset vil du kunne hente ut data og lage analyserapporter som gir innsikt og støtte til din markedsføring og kommunikasjon, samt en god utvikling og forbedring av nettstedet. Noen av temaene som dekkes i kurset er: Hva er webanalyse og hvordan fungerer Google Analytics Sentrale begreper De viktigste rapportene Eventtracking / brukeradferd Hva må du vite om oppsett KPIer og måling - hva er viktig å måle Hvordan bruke GA sammen med andre relevante verktøy som Google Data Studio, Google Tag Manager, Google Search Console [-]
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Oslo 3 dager 21 000 kr
07 Oct
07 Oct
ITIL® Specialist - Drive Stakeholder Value [+]
ITIL® Specialist - Drive Stakeholder Value [-]
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1 dag 8 000 kr
This course introduces fundamentals concepts related to artificial intelligence (AI), and the services in Microsoft Azure that can be used to create AI solutions. [+]
COURSE OVERVIEW The course is not designed to teach students to become professional data scientists or software developers, but rather to build awareness of common AI workloads and the ability to identify Azure services to support them. The course is designed as a blended learning experience that combines instructor-led training with online materials on the Microsoft Learn platform (https://azure.com/learn). The hands-on exercises in the course are based on Learn modules, and students are encouraged to use the content on Learn as reference materials to reinforce what they learn in the class and to explore topics in more depth. TARGET AUDIENCE The Azure AI Fundamentals course is designed for anyone interested in learning about the types of solution artificial intelligence (AI) makes possible, and the services on Microsoft Azure that you can use to create them. You don’t need to have any experience of using Microsoft Azure before taking this course, but a basic level of familiarity with computer technology and the Internet is assumed. Some of the concepts covered in the course require a basic understanding of mathematics, such as the ability to interpret charts. The course includes hands-on activities that involve working with data and running code, so a knowledge of fundamental programming principles will be helpful. COURSE OBJECTIVES  After completing this course, you will be able to: Describe Artificial Intelligence workloads and considerations Describe fundamental principles of machine learning on Azure Describe features of computer vision workloads on Azure Describe features of Natural Language Processing (NLP) workloads on Azure Describe features of conversational AI workloads on Azure   COURSE CONTENT Module 1: Introduction to AI In this module, you'll learn about common uses of artificial intelligence (AI), and the different types of workload associated with AI. You'll then explore considerations and principles for responsible AI development. Artificial Intelligence in Azure Responsible AI After completing this module you will be able to Describe Artificial Intelligence workloads and considerations Module 2: Machine Learning Machine learning is the foundation for modern AI solutions. In this module, you'll learn about some fundamental machine learning concepts, and how to use the Azure Machine Learning service to create and publish machine learning models. Introduction to Machine Learning Azure Machine Learning After completing this module you will be able to Describe fundamental principles of machine learning on Azure Module 3: Computer Vision Computer vision is a the area of AI that deals with understanding the world visually, through images, video files, and cameras. In this module you'll explore multiple computer vision techniques and services. Computer Vision Concepts Computer Vision in Azure After completing this module you will be able to Describe features of computer vision workloads on Azure Module 4: Natural Language Processing This module describes scenarios for AI solutions that can process written and spoken language. You'll learn about Azure services that can be used to build solutions that analyze text, recognize and synthesize speech, translate between languages, and interpret commands. After completing this module you will be able to Describe features of Natural Language Processing (NLP) workloads on Azure Module 5: Conversational AI Conversational AI enables users to engage in a dialog with an AI agent, or *bot*, through communication channels such as email, webchat interfaces, social media, and others. This module describes some basic principles for working with bots and gives you an opportunity to create a bot that can respond intelligently to user questions. Conversational AI Concepts Conversational AI in Azure After completing this module you will be able to Describe features of conversational AI workloads on Azure   TEST CERTIFICATION Recommended as preparation for the following exams: Exam AI-900: Microsoft Azure AI Fundamentals. 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 [-]
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Webinar + nettkurs 1 dag 5 590 kr
Kurset er rettet mot deg som skal armere i Autodesk Revit. [+]
Kurset er rettet mot deg som skal armere i Autodesk Revit. Dette er et praktisk kurs som gjør deg i stand til å armere betongkonstruksjoner, lage armeringstegninger og bøyelister. Hensikten med kurset er å gjøre deg i stand til bruke armerinsgverktøyene i Revit samt lage armeringstegninger og bøyelister ved hjelp av verktøyene som ligger i Revit-applikasjonen Focus RAT Bygg. Du vil lære hvordan manuelt armere betongkonstruksjoner. Du vil også lære verktøyene for å lage løpemeterarmering, armeringsnett og kantarmering. Du vil lære å bruke Revit Extensions for å armere konstruksjoner automatisk. Vi skal også lage armeringstegninger og bøyelister i henhold til NS 3766. Kursinnhold: Manuell armering av betongkonstruksjoner Løpemeterarmering Kantarmering Armeringsnett Automatisk armering av betongkonstruksjoner med Revit Extensions Armere avanserte betongkonstruksjoner Lage armeringstegninger Lage bøyelister [-]
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Virtuelt klasserom 4 dager 25 000 kr
In this course, the student will learn about the data engineering patterns and practices as it pertains to working with batch and real-time analytical solutions using Azu... [+]
COURSE OVERVIEW Students will begin by understanding the core compute and storage technologies that are used to build an analytical solution. They will then explore how to design an analytical serving layers and focus on data engineering considerations for working with source files. The students will learn how to interactively explore data stored in files in a data lake. They will learn the various ingestion techniques that can be used to load data using the Apache Spark capability found in Azure Synapse Analytics or Azure Databricks, or how to ingest using Azure Data Factory or Azure Synapse pipelines. The students will also learn the various ways they can transform the data using the same technologies that is used to ingest data. The student will spend time on the course learning how to monitor and analyze the performance of analytical system so that they can optimize the performance of data loads, or queries that are issued against the systems. They will understand the importance of implementing security to ensure that the data is protected at rest or in transit. The student will then show how the data in an analytical system can be used to create dashboards, or build predictive models in Azure Synapse Analytics. TARGET AUDIENCE The primary audience for this course is data professionals, data architects, and business intelligence professionals who want to learn about data engineering and building analytical solutions using data platform technologies that exist on Microsoft Azure. The secondary audience for this course data analysts and data scientists who work with analytical solutions built on Microsoft Azure. COURSE OBJECTIVES   Explore compute and storage options for data engineering workloads in Azure Design and Implement the serving layer Understand data engineering considerations Run interactive queries using serverless SQL pools Explore, transform, and load data into the Data Warehouse using Apache Spark Perform data Exploration and Transformation in Azure Databricks Ingest and load Data into the Data Warehouse Transform Data with Azure Data Factory or Azure Synapse Pipelines Integrate Data from Notebooks with Azure Data Factory or Azure Synapse Pipelines Optimize Query Performance with Dedicated SQL Pools in Azure Synapse Analyze and Optimize Data Warehouse Storage Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link Perform end-to-end security with Azure Synapse Analytics Perform real-time Stream Processing with Stream Analytics Create a Stream Processing Solution with Event Hubs and Azure Databricks Build reports using Power BI integration with Azure Synpase Analytics Perform Integrated Machine Learning Processes in Azure Synapse Analytics COURSE CONTENT Module 1: Explore compute and storage options for data engineering workloads This module provides an overview of the Azure compute and storage technology options that are available to data engineers building analytical workloads. This module teaches ways to structure the data lake, and to optimize the files for exploration, streaming, and batch workloads. The student will learn how to organize the data lake into levels of data refinement as they transform files through batch and stream processing. Then they will learn how to create indexes on their datasets, such as CSV, JSON, and Parquet files, and use them for potential query and workload acceleration. Introduction to Azure Synapse Analytics Describe Azure Databricks Introduction to Azure Data Lake storage Describe Delta Lake architecture Work with data streams by using Azure Stream Analytics Lab 1: Explore compute and storage options for data engineering workloads Combine streaming and batch processing with a single pipeline Organize the data lake into levels of file transformation Index data lake storage for query and workload acceleration After completing module 1, students will be able to: Describe Azure Synapse Analytics Describe Azure Databricks Describe Azure Data Lake storage Describe Delta Lake architecture Describe Azure Stream Analytics Module 2: Design and implement the serving layer This module teaches how to design and implement data stores in a modern data warehouse to optimize analytical workloads. The student will learn how to design a multidimensional schema to store fact and dimension data. Then the student will learn how to populate slowly changing dimensions through incremental data loading from Azure Data Factory. Design a multidimensional schema to optimize analytical workloads Code-free transformation at scale with Azure Data Factory Populate slowly changing dimensions in Azure Synapse Analytics pipelines Lab 2: Designing and Implementing the Serving Layer Design a star schema for analytical workloads Populate slowly changing dimensions with Azure Data Factory and mapping data flows After completing module 2, students will be able to: Design a star schema for analytical workloads Populate a slowly changing dimensions with Azure Data Factory and mapping data flows Module 3: Data engineering considerations for source files This module explores data engineering considerations that are common when loading data into a modern data warehouse analytical from files stored in an Azure Data Lake, and understanding the security consideration associated with storing files stored in the data lake. Design a Modern Data Warehouse using Azure Synapse Analytics Secure a data warehouse in Azure Synapse Analytics Lab 3: Data engineering considerations Managing files in an Azure data lake Securing files stored in an Azure data lake After completing module 3, students will be able to: Design a Modern Data Warehouse using Azure Synapse Analytics Secure a data warehouse in Azure Synapse Analytics Module 4: Run interactive queries using Azure Synapse Analytics serverless SQL pools In this module, students will learn how to work with files stored in the data lake and external file sources, through T-SQL statements executed by a serverless SQL pool in Azure Synapse Analytics. Students will query Parquet files stored in a data lake, as well as CSV files stored in an external data store. Next, they will create Azure Active Directory security groups and enforce access to files in the data lake through Role-Based Access Control (RBAC) and Access Control Lists (ACLs). Explore Azure Synapse serverless SQL pools capabilities Query data in the lake using Azure Synapse serverless SQL pools Create metadata objects in Azure Synapse serverless SQL pools Secure data and manage users in Azure Synapse serverless SQL pools Lab 4: Run interactive queries using serverless SQL pools Query Parquet data with serverless SQL pools Create external tables for Parquet and CSV files Create views with serverless SQL pools Secure access to data in a data lake when using serverless SQL pools Configure data lake security using Role-Based Access Control (RBAC) and Access Control List After completing module 4, students will be able to: Understand Azure Synapse serverless SQL pools capabilities Query data in the lake using Azure Synapse serverless SQL pools Create metadata objects in Azure Synapse serverless SQL pools Secure data and manage users in Azure Synapse serverless SQL pools Module 5: Explore, transform, and load data into the Data Warehouse using Apache Spark This module teaches how to explore data stored in a data lake, transform the data, and load data into a relational data store. The student will explore Parquet and JSON files and use techniques to query and transform JSON files with hierarchical structures. Then the student will use Apache Spark to load data into the data warehouse and join Parquet data in the data lake with data in the dedicated SQL pool. Understand big data engineering with Apache Spark in Azure Synapse Analytics Ingest data with Apache Spark notebooks in Azure Synapse Analytics Transform data with DataFrames in Apache Spark Pools in Azure Synapse Analytics Integrate SQL and Apache Spark pools in Azure Synapse Analytics Lab 5: Explore, transform, and load data into the Data Warehouse using Apache Spark Perform Data Exploration in Synapse Studio Ingest data with Spark notebooks in Azure Synapse Analytics Transform data with DataFrames in Spark pools in Azure Synapse Analytics Integrate SQL and Spark pools in Azure Synapse Analytics After completing module 5, students will be able to: Describe big data engineering with Apache Spark in Azure Synapse Analytics Ingest data with Apache Spark notebooks in Azure Synapse Analytics Transform data with DataFrames in Apache Spark Pools in Azure Synapse Analytics Integrate SQL and Apache Spark pools in Azure Synapse Analytics Module 6: Data exploration and transformation in Azure Databricks This module teaches how to use various Apache Spark DataFrame methods to explore and transform data in Azure Databricks. The student will learn how to perform standard DataFrame methods to explore and transform data. They will also learn how to perform more advanced tasks, such as removing duplicate data, manipulate date/time values, rename columns, and aggregate data. Describe Azure Databricks Read and write data in Azure Databricks Work with DataFrames in Azure Databricks Work with DataFrames advanced methods in Azure Databricks Lab 6: Data Exploration and Transformation in Azure Databricks Use DataFrames in Azure Databricks to explore and filter data Cache a DataFrame for faster subsequent queries Remove duplicate data Manipulate date/time values Remove and rename DataFrame columns Aggregate data stored in a DataFrame After completing module 6, students will be able to: Describe Azure Databricks Read and write data in Azure Databricks Work with DataFrames in Azure Databricks Work with DataFrames advanced methods in Azure Databricks Module 7: Ingest and load data into the data warehouse This module teaches students how to ingest data into the data warehouse through T-SQL scripts and Synapse Analytics integration pipelines. The student will learn how to load data into Synapse dedicated SQL pools with PolyBase and COPY using T-SQL. The student will also learn how to use workload management along with a Copy activity in a Azure Synapse pipeline for petabyte-scale data ingestion. Use data loading best practices in Azure Synapse Analytics Petabyte-scale ingestion with Azure Data Factory Lab 7: Ingest and load Data into the Data Warehouse Perform petabyte-scale ingestion with Azure Synapse Pipelines Import data with PolyBase and COPY using T-SQL Use data loading best practices in Azure Synapse Analytics After completing module 7, students will be able to: Use data loading best practices in Azure Synapse Analytics Petabyte-scale ingestion with Azure Data Factory Module 8: Transform data with Azure Data Factory or Azure Synapse Pipelines This module teaches students how to build data integration pipelines to ingest from multiple data sources, transform data using mapping data flowss, and perform data movement into one or more data sinks. Data integration with Azure Data Factory or Azure Synapse Pipelines Code-free transformation at scale with Azure Data Factory or Azure Synapse Pipelines Lab 8: Transform Data with Azure Data Factory or Azure Synapse Pipelines Execute code-free transformations at scale with Azure Synapse Pipelines Create data pipeline to import poorly formatted CSV files Create Mapping Data Flows After completing module 8, students will be able to: Perform data integration with Azure Data Factory Perform code-free transformation at scale with Azure Data Factory Module 9: Orchestrate data movement and transformation in Azure Synapse Pipelines In this module, you will learn how to create linked services, and orchestrate data movement and transformation using notebooks in Azure Synapse Pipelines. Orchestrate data movement and transformation in Azure Data Factory Lab 9: Orchestrate data movement and transformation in Azure Synapse Pipelines Integrate Data from Notebooks with Azure Data Factory or Azure Synapse Pipelines After completing module 9, students will be able to: Orchestrate data movement and transformation in Azure Synapse Pipelines Module 10: Optimize query performance with dedicated SQL pools in Azure Synapse In this module, students will learn strategies to optimize data storage and processing when using dedicated SQL pools in Azure Synapse Analytics. The student will know how to use developer features, such as windowing and HyperLogLog functions, use data loading best practices, and optimize and improve query performance. Optimize data warehouse query performance in Azure Synapse Analytics Understand data warehouse developer features of Azure Synapse Analytics Lab 10: Optimize Query Performance with Dedicated SQL Pools in Azure Synapse Understand developer features of Azure Synapse Analytics Optimize data warehouse query performance in Azure Synapse Analytics Improve query performance After completing module 10, students will be able to: Optimize data warehouse query performance in Azure Synapse Analytics Understand data warehouse developer features of Azure Synapse Analytics Module 11: Analyze and Optimize Data Warehouse Storage In this module, students will learn how to analyze then optimize the data storage of the Azure Synapse dedicated SQL pools. The student will know techniques to understand table space usage and column store storage details. Next the student will know how to compare storage requirements between identical tables that use different data types. Finally, the student will observe the impact materialized views have when executed in place of complex queries and learn how to avoid extensive logging by optimizing delete operations. Analyze and optimize data warehouse storage in Azure Synapse Analytics Lab 11: Analyze and Optimize Data Warehouse Storage Check for skewed data and space usage Understand column store storage details Study the impact of materialized views Explore rules for minimally logged operations After completing module 11, students will be able to: Analyze and optimize data warehouse storage in Azure Synapse Analytics Module 12: Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link In this module, students will learn how Azure Synapse Link enables seamless connectivity of an Azure Cosmos DB account to a Synapse workspace. The student will understand how to enable and configure Synapse link, then how to query the Azure Cosmos DB analytical store using Apache Spark and SQL serverless. Design hybrid transactional and analytical processing using Azure Synapse Analytics Configure Azure Synapse Link with Azure Cosmos DB Query Azure Cosmos DB with Apache Spark pools Query Azure Cosmos DB with serverless SQL pools Lab 12: Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link Configure Azure Synapse Link with Azure Cosmos DB Query Azure Cosmos DB with Apache Spark for Synapse Analytics Query Azure Cosmos DB with serverless SQL pool for Azure Synapse Analytics After completing module 12, students will be able to: Design hybrid transactional and analytical processing using Azure Synapse Analytics Configure Azure Synapse Link with Azure Cosmos DB Query Azure Cosmos DB with Apache Spark for Azure Synapse Analytics Query Azure Cosmos DB with SQL serverless for Azure Synapse Analytics Module 13: End-to-end security with Azure Synapse Analytics In this module, students will learn how to secure a Synapse Analytics workspace and its supporting infrastructure. The student will observe the SQL Active Directory Admin, manage IP firewall rules, manage secrets with Azure Key Vault and access those secrets through a Key Vault linked service and pipeline activities. The student will understand how to implement column-level security, row-level security, and dynamic data masking when using dedicated SQL pools. Secure a data warehouse in Azure Synapse Analytics Configure and manage secrets in Azure Key Vault Implement compliance controls for sensitive data Lab 13: End-to-end security with Azure Synapse Analytics Secure Azure Synapse Analytics supporting infrastructure Secure the Azure Synapse Analytics workspace and managed services Secure Azure Synapse Analytics workspace data After completing module 13, students will be able to: Secure a data warehouse in Azure Synapse Analytics Configure and manage secrets in Azure Key Vault Implement compliance controls for sensitive data Module 14: Real-time Stream Processing with Stream Analytics In this module, students will learn how to process streaming data with Azure Stream Analytics. The student will ingest vehicle telemetry data into Event Hubs, then process that data in real time, using various windowing functions in Azure Stream Analytics. They will output the data to Azure Synapse Analytics. Finally, the student will learn how to scale the Stream Analytics job to increase throughput. Enable reliable messaging for Big Data applications using Azure Event Hubs Work with data streams by using Azure Stream Analytics Ingest data streams with Azure Stream Analytics Lab 14: Real-time Stream Processing with Stream Analytics Use Stream Analytics to process real-time data from Event Hubs Use Stream Analytics windowing functions to build aggregates and output to Synapse Analytics Scale the Azure Stream Analytics job to increase throughput through partitioning Repartition the stream input to optimize parallelization After completing module 14, students will be able to: Enable reliable messaging for Big Data applications using Azure Event Hubs Work with data streams by using Azure Stream Analytics Ingest data streams with Azure Stream Analytics Module 15: Create a Stream Processing Solution with Event Hubs and Azure Databricks In this module, students will learn how to ingest and process streaming data at scale with Event Hubs and Spark Structured Streaming in Azure Databricks. The student will learn the key features and uses of Structured Streaming. The student will implement sliding windows to aggregate over chunks of data and apply watermarking to remove stale data. Finally, the student will connect to Event Hubs to read and write streams. Process streaming data with Azure Databricks structured streaming Lab 15: Create a Stream Processing Solution with Event Hubs and Azure Databricks Explore key features and uses of Structured Streaming Stream data from a file and write it out to a distributed file system Use sliding windows to aggregate over chunks of data rather than all data Apply watermarking to remove stale data Connect to Event Hubs read and write streams After completing module 15, students will be able to: Process streaming data with Azure Databricks structured streaming Module 16: Build reports using Power BI integration with Azure Synpase Analytics In this module, the student will learn how to integrate Power BI with their Synapse workspace to build reports in Power BI. The student will create a new data source and Power BI report in Synapse Studio. Then the student will learn how to improve query performance with materialized views and result-set caching. Finally, the student will explore the data lake with serverless SQL pools and create visualizations against that data in Power BI. Create reports with Power BI using its integration with Azure Synapse Analytics Lab 16: Build reports using Power BI integration with Azure Synpase Analytics Integrate an Azure Synapse workspace and Power BI Optimize integration with Power BI Improve query performance with materialized views and result-set caching Visualize data with SQL serverless and create a Power BI report After completing module 16, students will be able to: Create reports with Power BI using its integration with Azure Synapse Analytics Module 17: Perform Integrated Machine Learning Processes in Azure Synapse Analytics This module explores the integrated, end-to-end Azure Machine Learning and Azure Cognitive Services experience in Azure Synapse Analytics. You will learn how to connect an Azure Synapse Analytics workspace to an Azure Machine Learning workspace using a Linked Service and then trigger an Automated ML experiment that uses data from a Spark table. You will also learn how to use trained models from Azure Machine Learning or Azure Cognitive Services to enrich data in a SQL pool table and then serve prediction results using Power BI. Use the integrated machine learning process in Azure Synapse Analytics Lab 17: Perform Integrated Machine Learning Processes in Azure Synapse Analytics Create an Azure Machine Learning linked service Trigger an Auto ML experiment using data from a Spark table Enrich data using trained models Serve prediction results using Power BI After completing module 17, students will be able to: Use the integrated machine learning process in Azure Synapse Analytics     [-]
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Oslo Bodø Og 5 andre steder 2 dager 8 900 kr
06 May
06 May
03 Jun
Excel Grunnkurs [+]
Excel Grunnkurs [-]
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Oslo 3 dager 20 000 kr
17 Jun
17 Jun
09 Sep
AZ-700: Designing and Implementing Microsoft Azure Networking Solutions [+]
AZ-700: Designing and Implementing Microsoft Azure Networking Solutions [-]
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Nettkurs 4 timer 349 kr
Adobe Audition er et profesjonelt lydredigeringsprogram for innspilling, miksing og bearbeiding av lyd. Programmet passer for alle som jobber med lyd, dette være seg: vid... [+]
Adobe Audition er et profesjonelt lydredigeringsprogram som gir deg muligheten til å spille inn, mikse og bearbeide lyd. Dette programmet er ideelt for alle som arbeider med lyd, uavhengig om det er for video, animasjon, radio, podkast eller spill. Audition støtter et bredt spekter av lydformater, inkludert ASIO, VST og MIDI. Du kan også enkelt importere prosjekter fra Adobe Premiere Pro, noe som gjør Audition til et utmerket valg for de som ønsker førsteklasses lyd til sine videoproduksjoner. For å få tilgang til Adobe Audition må du abonnere på Adobe Creative Cloud (kr 590 per måned). I dette omfattende kurset vil Espen Faugstad veilede deg gjennom hele programmet, fra begynnelse til slutt. Du vil lære alt du trenger for å kunne bruke programmet på en effektiv måte. Kurset dekker emner som hvordan du importerer, organiserer og redigerer lydfiler. Du vil også lære å fjerne bakgrunnsstøy, fremheve stemmer, legge til lydeffekter, og mye mer. Kursinnhold: Introduksjon Lydterminologi Importering av lyd Redigering med Waveform Editor (del 1) Redigering med Waveform Editor (del 2) Multitrack Editor (del 1) Multitrack Editor (del 2) Bruk av paneler og verktøy Essential Sound-panelet Eksportering av prosjekt og samarbeid med Premiere Pro Etter fullføring av kurset, vil du ha den nødvendige kunnskapen og ferdighetene til å arbeide effektivt med Adobe Audition for å oppnå høykvalitetslyd i dine prosjekter.   Varighet: 4 timer og 5 minutter   Om Utdannet.no: Utdannet.no tilbyr noen av de beste digitale nettkursene i landet. 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 tilgjengelige. Med over 30 000 registrerte brukere og 1,5 millioner videoavspillinger har vi opplevd betydelig vekst de siste årene. Vårt mål er å gjøre kompetanseutvikling engasjerende, spennende og tilgjengelig for alle, og vi har støtte fra Innovasjon Norge og Forskningsrådet. [-]
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Bedriftsintern 1 dag 6 200 kr
Dette kurset gir en grunnleggende oversikt og innføring i noen av de viktigste delene av Microsoft 365, og hvordan du kan bruke det på en effektiv måte. [+]
Dette kurset tilbys som bedriftsinternt kurs   Deltakerne får god kjennskap til lagring og administrasjon av filer i sky, deling av filer, samt produktivitetsverktøy som To Do og OneNote. I tillegg gir vi en introduksjon til verktøy for samhandling via SharePoint og Teams.    Kursinnhold     • Grunnleggende innføring i hva Microsoft Office 365 er     • OneDrive - Lagring i sky     • SharePoint - Biblioteker, lister og samhandling     • Synkronisering av filer mellom pc og sky     • Deling av filer og dokumenter     • Introduksjon til produktivitetsverktøyene OneNote og To Do     • Teams - Navet i Microsoft 365    Målgruppe Alle som ønsker en grunnleggende forståelse av Microsoft 365 og som trenger en innføring i de viktigste delene.   Forkunnskaper Det er ikke krav til forkunnskaper, men det forventes at deltagerne har generell kjennskap til Windows, Office-pakken (Word, Excel, Outlook) og Internett.    [-]
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Nettkurs 5 dager 16 500 kr
ISO/IEC 27001 Lead Implementer [+]
ISO/IEC 27001 Lead Implementer [-]
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4 dager 23 000 kr
This course teaches Azure administrators how to plan, deliver, and manage virtual desktop experiences and remote apps, for any device, on Azure. [+]
 Students will learn through a mix of demonstrations and hands-on lab experiences deploying virtual desktop experiences and apps on Windows Virtual Desktop and optimizing them to run in multi-session virtual environments.Students for AZ-140: Configuring and Operating Windows Virtual Desktop on Microsoft Azure are interested in delivering applications on Windows Virtual Desktop and optimizing them to run in multi-session virtual environments. As a Windows Virtual Desktop administrator, you will closely with the Azure Administrators and Architects, along with Microsoft 365 Administrators. Windows Virtual Desktop administrator responsibilities include planning, deploying, packaging, updating, and maintaining the Azure Windows Virtual Desktop infrastructure. They also create session host images, implement and manage FSLogix, monitor Windows Virtual Desktop performance, and automate Windows Virtual Desktop management tasks. COURSE OBJECTIVES   Select an appropriate licensing model for Windows Virtual Desktop Implement networking for Windows Virtual Desktop Manage Windows Virtual Desktop session hosts by using Azure Bastion Configure storage for FSLogix components Create and manage session host images Implement Azure roles and role-based access control (RBAC) for Windows Virtual Desktop Configure user Windows Virtual Desktop experience settings Install and configure apps on a session host Implement business continuity and disaster recovery Monitor and manage Windows Virtual Desktop performance     COURSE CONTENT Module 1: Plan a Windows Virtual Desktop Architecture In this module, you will learn how to assess existing physical and virtual desktop environments, plan and configure name resolution for Active Directory (AD) and Azure Active Directory Domain Services (Azure AD DS), and plan for Windows Virtual Desktop client deployments. LESSONS M1 Windows Virtual Desktop Architecture Design the WVD architecture Design for user identities and profiles LAB: PREPARE FOR DEPLOYMENT OF AZURE WINDOWS VIRTUAL DESKTOP (AZURE AD DS) LAB: PREPARE FOR DEPLOYMENT OF AZURE WINDOWS VIRTUAL DESKTOP (AD DS) After completing module 1, students will be able to: Understand Windows Virtual Desktop Components Understand personal and pooled desktops Recommend an operating system for a WVD implementation Plan a host pools architecture Module 2: Implement a WVD Infrastructure In this module, you will learn how to manage connectivity to the internet and on-premises networks, create a host pool by using the Azure portal, deploy host pools and hosts by using Azure Resource Manager templates, apply OS and application updates to a running WVD host, and create a master image. LESSONS M2 Implement and manage networking for WVD Implement and manage storage for WVD Create and configure host pools and session hosts Create and manage session host image LAB: CREATE AND CONFIGURE HOST POOLS AND SESSION HOSTS (AZURE AD DS) LAB: DEPLOY HOST POOLS AND SESSION HOSTS BY USING THE AZURE PORTAL (AD DS) LAB: IMPLEMENT AND MANAGE STORAGE FOR WVD (AZURE AD DS) LAB: DEPLOY HOST POOLS AND HOSTS BY USING AZURE RESOURCE MANAGER TEMPLATES LAB: DEPLOY AND MANAGE HOST POOLS AND HOSTS BY USING POWERSHELL After completing module 2, students will be able to: Implement Azure virtual network connectivity Manage connectivity to the internet and on-premises networks Understanding Windows Virtual Desktop network connectivity Configure WVD session hosts using Azure Bastion Configure storage for FSLogix components Configure disks and file shares Modify a session host image Create and use a Shared Image Gallery (SIG) Module 3: Manage Access and Security In this module, you will learn how to plan and implement Azure roles and RBAC for WVD, implement Conditional Access policies for connections, plan and implement MFA, and manage security by using Azure Security Center. LESSONS M3 Manage access Manage security LAB: CONFIGURE CONDITIONAL ACCESS POLICIES FOR CONNECTIONS TO WVD (AD DS) After completing module 3, students will be able to: Manage local roles, groups and rights assignment on WVD session hosts. Configure user restrictions by using AD group policies and Azure AD policies Understand Conditional Access policy components Prepare for Azure Active Directory (Azure AD)-based Conditional Access for Windows Virtual Desktop Implement Azure AD-based Conditional Access for Windows Virtual Desktop Module 4: Manage User Environments and Apps In this module, you will learn how to plan for FSLogix, install FSLogix, configure Cloud Cache, deploy an application as a RemoteApp, and implement and manage OneDrive for Business for a multi-session environment. LESSONS M4 Implement and manage FSLogix Configure user experience settings Install and configure apps on a session host LAB: WINDOWS VIRTUAL DESKTOP PROFILE MANAGEMENT (AZURE AD DS) LAB: WINDOWS VIRTUAL DESKTOP PROFILE MANAGEMENT (AD DS) LAB: WINDOWS VIRTUAL DESKTOP APPLICATION PACKAGING (AD DS) After completing module 4, students will be able to: Configure Profile Containers Configure Azure Files to store profile containers for WVD in an AAD DS environment Implement FSLogix based profiles for Windows Virtual Desktop in Azure AD DS environment Implement FSLogix based profiles for Windows Virtual Desktop Prepare for and create MSIX app packages Implement MSIX app attach container for Windows Virtual Desktop in AD DS environmen Module 5: Monitor and maintain a WVD infrastructure In this module, you will learn how to plan and implement a disaster recovery plan for WVD, configure automation for WVD, implement autoscaling in host pools, and optimize session host capacity and performance. LESSONS M5 Plan and implement business continuity and disaster recovery Automate WVD management tasks Monitor and manage performance and health LAB: IMPLEMENT AUTOSCALING IN HOST POOLS (AD DS) After completing module 5, students will be able to: Plan and implement a disaster recovery plan for WVD Configure automation for WVD Monitor WVD by using Azure Monitor Customize Azure Workbooks for WVD monitoring Configure autoscaling of Windows Virtual Desktop session hosts Verify autoscaling of Windows Virtual Desktop session host [-]
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Virtuelt klasserom 4 dager 23 000 kr
Python is an object oriented rapid development language deployed in many scenarios in the modern world. [+]
COURSE OVERVIEW   This Python Programming 1 course is designed to give delegates the knowledge to develop and maintain Python scripts using the current version (V3) of Python. There are many similarities between Python V2 and Python V3. The skills gained on this course will allow the delegate to develop their own skills further using Python V2 or V3 to support the development and maintenance of scripts. The Python Programming 1 course comprises sessions dealing with syntax,variables and data types,operators and expressions,conditions and loops,functions,objects,collections,modules and packages,strings,pattern matching,exception handling,binary and text files,and databases. Exercises and examples are used throughout the course to give practical hands-on experience with the techniques covered. TARGET AUDIENCE The Python Programming 1 course course is aimed at those who want to improve their Python programming skills,and for developers/engineers who want to migrate to Python from another language,particularly those with little or no object-oriented knowledge. For those wishing to learn Python and have no previous knowledge of programming,they should look to attend our foundation course Introduction to Programming - Python. COURSE OBJECTIVES This course aims to provide the delegate with the knowledge to be able to produce Python scripts and applications that exploit all core elements of the language including variables,expressions,selection and iteration,functions,objects,collections,strings,modules,pattern matching,exception handling,I/O,and classes. COURSE CONTENT DAY 1 COURSE INTRODUCTION Administration and Course Materials Course Structure and Agenda Delegate and Trainer Introductions SESSION 1: GETTING STARTED About Python Python versions Python documentation Python runtimes Installing Python The REPL shell Python editors SESSION 2: PYTHON SCRIPTS & SYNTAX Script naming Comments Docstring Statements The backslash Code blocks Whitespace Console IO (to enable the writing of simple programs) A first Python program Script execution SESSION 3: VARIABLES & DATA TYPES Literals Identifiers Assignment Numbers (bool,int,float,complex) Binary,octal,and hexadecimal numbers Floating point accuracy Collections (str,list,tuple,set,dict) None Implicit and explicit type conversion (casting) The type function SESSION 4: OPERATORS & EXPRESSIONS Arithmetic Operators Assignment Operators Comparison Operators Logical Operators Membership Operators Bitwise Operators Identity Operators SESSION 5: CONDITIONS & LOOPS Conditional statements (if,elif,else) Nested conditional statements Short hand if/if else Python's alternative to the ternary operator Iterative statements (while,for,else) The range function Iterating over a list Break Continue Nested conditional/iterative statements COURSE CONTENTS - DAY 2 SESSION 6: FUNCTIONS Declaration Invocation Default values for parameters Named arguments args and kwargs Returning multiple values None returned Variable scope Masking and shadowing The pass keyword Recursive functions SESSION 7: OBJECTS AND CLASSES About objects Attributes and the dot notation The dir function Dunder attributes Mutability The id function Pass by reference Introduction to Classes Class Declaration and Instantiation Data attributes Methods Composition SESSION 8: LISTS About lists List syntax including slicing Getting and setting list elements Iterating over a list Checking for the presence of a value The len function List methods incl. append,insert,remove,pop,clear,copy,sort,reverse etc. The del keyword Appending to and combining lists List comprehension SESSION 9: TUPLES About tuples Tuple syntax Getting tuple elements including unpacking Iterating over a tuple Checking for the presence of a value The len function Appending to and combining tuples SESSION 10: SETS About Sets Dictionary syntax Creating,adding and removing set elements Iterating over a set Membership Testing Sorting Copying Set methods incl. union,intersection,difference,symmetric_difference etc. COURSE CONTENTS - DAY 3 SESSION 11: DICTIONARIES About dictionaries Dictionary syntax Getting and setting dictionary elements Iterating over a dictionary (keys,values,and items) Checking for the presence of a key The len function Dictionary methods incl. keys,values,items,get,pop,popitem,clear etc. The del keyword Dictionary comprehension SESSION 12: STRINGS About strings String syntax including slicing Escape characters Triple-quoted strings Concatenation Placeholders The format method Other methods e.g. endswith,find,join,lower,replace,split,startswith,strip,upper etc. A string as a list of bytes SESSION 13: MODULES & PACKAGES About modules Inbuilt modules math,random and platform the dir() and help() functions Creating and using modules the __pycache__ and the .pyc files The module search path Importing modules Namespaces Importing module objects The import wildcard Aliases Importing within a function Executable modules Reloading a module About packages Importing packaged modules Importing packaged module objects Package initialisation Subpackages Referencing objects in sibling packages The Standard Library Installing modules and packages using pip SESSION 14: PATTERN MATCHING About regular expressions Regular expression special characters Raw strings About the re module re module functions incl. match,search,findall,full match,split,sub   COURSE CONTENTS - DAY 4 SESSION 15: EXCEPTION HANDLING About exceptions and exception handling Handling exceptions (try,except,else,finally) Exception types The exception object Raising exceptions Custom exception types Built-in exceptions hierarchy SESSION 16: FILES & THE FILESYSTEM The open function Methods for seeking (seekable,seek) Methods for reading from a file (readable,read,readline,readlines) Iterating over a file Methods for writing to a file (writable,write,writelines) Introduction to context managers Text encoding schemes,codepoints,codespace ASCII and UNICODE (UTF schemes) UTF-8,binary and hexadecimal representations The ord() and chr() functions Binary files,bytes and bytearray I/O layered abstraction. About the os module os module functions incl. getcwd,listdir,mkdir,chdir,remove,rmdir etc. OSError numbers and the errno module SESSION 17: DATABASES The DB-API DP-API implementations Establishing a connection Creating a cursor Executing a query Fetching results Transactions Inserting,updating,and deleting records FOLLOW ON COURSES Python Programming 2  Data Analysis Python  Apache Web Server PHP Programming  PHP & MySQL for Web Development  PHP & MariaDB for Web Development  Perl Programming  Ruby Programming  Introduction to MySQL  Introduction to MariaDB [-]
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1 dag 9 500 kr
Power BI er et fleksibelt verktøy som dekker hele prosessen fra datafangst til deling av rapporter. Selv om det virker lett på overflaten har Power BI funksjoner som åpne... [+]
Power BI er et fleksibelt verktøy som dekker hele prosessen fra datafangst til deling av rapporter. Selv om det virker lett på overflaten har Power BI funksjoner som åpner uendelige muligheter. Det er flere triks som skaper en bedre brukeropplevelse av Power BI samtidig som at man bruker Power BI til å gjennomføre avanserte kalkulasjoner.   Dette kurset bygger på filmdata som deltakerne hentes direkte fra IMDB via funksjoner i Power Query, og brukes til å lage en lekker rapport i Power BI. Kurset bygger ikke direkte på «Selvbetjening og analyse med Power BI», men bygger videre på flere av teknikkene som ble gjennomgått her, og er myntet på de som allerede har litt erfaring med bruk av Power BI. Deltakerne vil få en grunnleggende innføring i:  Henter og renser ekte data fra IMDB via Power Query. Power Query funksjoner Avansert bruk av bokmerker Et dypdykk i design Dynamiske overskrifter med DAX Variabelsyntaks i DAX Filter- og radkontekst  Dynamisk link til nettsider med DAX Egendefinerte verktøytips Betinget formatering Målgruppe: Kurset er for folk som har jobbet med Power BI i minst et par måneder. Det er en fordel hvis man har gjennomført «Selvbetjening og analyse med Power BI», men det er ikke et krav. Ta med egen PC med nyeste versjon av Power BI. Du bør også ha signet opp med en 30-dagers gratislisens for Power BI Pro.    Kursinnhold Henter og renser ekte data fra IMDB via Power Query Vi henter data direkte fra nettsiden til IMDB (scraping) og transformerer dette til et format som er egnet i Power BI. Power Query funksjoner Vi bruker eksempeldata til å trene Power BI til å gjenkjenne mønstre i nettsiden slik at logikken kan transformeres til en funksjon og brukes på flere filmer. Avansert bruk av bokmerker Bokmerker er en funksjon som hjelper deg med å lage interaktive rapporter. I dette kurset bruker vi denne funksjonen til å lage et dynamisk filterpanel. Et dypdykk i design Design er med på å skape et helhetlig inntrykk av rapporten, og bidrar til at rapporten blir mer brukervennlig. Kurset gjennomgår generelle designtriks sammen med teori rundt universal utforming. Dynamiske overskrifter med DAX Du kan enkelt lage statiske overskrifter i Power BI, men med overskrifter som endrer seg etter innholdet, tar du rapporten din til et nytt nivå. Variabelsyntaks i DAX Variabelsyntaksen i DAX skaper en leservennlig struktur i koden samtidig som det kan forbedre ytelsen. Kurset gjennomgår hvordan man bruker variabelsyntaksen og fordelene med det.  Filter- og radkontekst Ord som filterkontekst og radkontekst brukes mye når man snakker om Power BI, men det er ikke alltid tydelig hva det betyr. Kurset gjennomgår forskjellene og illustrerer dette med å tvinge radkontekst i et mål via AVERAGEX funksjonen. Dynamisk link til nettsider med DAX Power BI kan linke til nettsider eller andre Power BI rapporter. I dette kurset lager vi en link som fører direkte til nettsiden for valgt film.  Egendefinerte verktøytips Vektøystips er viser ekstra informasjon når brukeren holder over et element i Power BI. Det finnes en standard visning hvor man kan gjøre et par justeringer, men i dette kurset lager vi et helt eget verktøystips slik at vi har full kontroll over størrelse og innhold. Betinget formatering Betinget formatering lar deg endre formateringen basert på en betinget verdi. I dette kurset lager vi datastolper og fargeendringer via både brukergrensesnittet og DAX [-]
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