Oversikt over relevante kurs innen IT-kurs i Brønnøysund

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% [-]

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% [-]

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

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% [-]

Dette kurset tilbys som bedriftsinternt kurs   Maskinlæring handler om sette datamaskiner i stand til å lære fra og utvikle atferd basert på data. Det vil si at en datamaskin kan løse en oppgave den ikke er eksplisitt programmert for å håndtere. I stedet er den i stand til å automatisk lære gjenkjenning av komplekse mønstre i data og gjøre beslutninger basert på dette disse. Maskinlæring gir store muligheter, men mange bedrifter har problemer med å ta teknologien i bruk. Nøyaktig hvilke oppgaver kan maskinlæring utføre, og hvordan kommer man i gang? Dette kurset gir oversikt over mulighetene som ligger i maskinlæring, og hvordan i tillegg til kunnskap om hvordan teknologien kan løse oppgaver og skape resultater i praksis. Hva er maskinlæring, datavitenskap og kunstig intelligens og hvordan det er relatert til statistikk og dataanalyse? Hvordan å utvinne kunnskap fra dataene dine? Hva betyr Big data og hvordan analyseres det? Hvor og hvordan skal du bruke maskinlæring til dine daglige forretningsproblemer? Hvordan bruke datamønstre til å ta avgjørelser og spådommer med eksempler fra den virkelige verden? Hvilke typer forretningsproblemer kan en maskinen lære å håndtere Muligheter som maskinlæring gir din bedrift Hva er de teoretiske aspekter på metoder innen maskinlæring? Hvilke ML-metoder som er relevante for ulike problemstillinger innen dataanalyse? Hvordan evaluere styrker og svakheter mellom disse algoritmene og velge den beste? Anvendt data science og konkrete kunde eksempler i praksis   Målsetning Kurset gir kunnskap om hvordan maskinlæring kan løse et bestemt problem og hvilke metoder som egner seg i en gitt situasjon. Du blir i stand til å kan skaffe deg innsikt i data, og vil kunne identifisere egenskapene som representerer dem best. Du kjenner de viktigste maskinlæringsalgoritmene og hvilke metoder som evaluerer ytelsen deres best. Dette gir grunnlag for kontinuerlig forbedring av løsninger basert på maskinlæring.   [-]

Vue.js, Vuex & Router Course [-]

ITIL® 4 Practitioner: Change Enablement [-]

  Studieår: 2013-2014   Gjennomføring: Høst og vår Antall studiepoeng: 5.0 Forutsetninger: IT Introduksjon eller tilsvarende. Innleveringer: Øvinger: 8 må være godkjent.  Personlig veileder: ja Vurderingsform: Skriftlig eksamen, 3 timer Ansvarlig: Tore Mallaug Eksamensdato: 09.12.13 / 08.05.14         Læremål: Etter å ha gjennomført emnet skal studenten ha følgende samlede læringsutbytte: KUNNSKAPER:Kandidaten skal:- kjenne sentrale begreper innen databaser og datamodellering, og kan gjøre rede for disse- forstå hvordan en relasjonsdatabase er bygd opp ved å se på relasjonene (tabellene) og tilhørende nøkler- forstå (tolke) et (E)ER-diagram modellert i fagets gjeldende notasjon, og vite hvordan dette kan oversettes til relasjonsmodellen- gjøre rede for hvordan databaser kan fungere i en klient/tjener-arkitektur. FERDIGHETER:Kandidaten skal kunne:- tegne sitt eget (E)ER-diagram for å oppnå en god databasestruktur- lage sin egen normaliserte relasjonsdatabase med nøkler og referanseintegritet, og opprette databasen i et valgt databaseverktøy (databasesystem)- utføre SQL-spørringer mot en gitt database- lage en relasjonsdatabase som støtter opp om funksjonaliteten til et gitt grafisk brukergrensesnitt GENERELL KOMPETANSEKandidaten- viser en bevisst holdning til strukturell lagring og representasjon av data i et informasjonssystem- viser en bevisst holdning til databasedesign for å unngå unødvendig dobbeltlagring av data i en database Innhold:Innføring i datamodellering med EER og UML-notasjon. Design av relasjonsdatabase inkl. bruk av nøkler, referanseintegritet og enkel normalisering. Databasedefinisjon (DDL) og datamanipulering (DML) i SQL. Bruk av et valgt databaseverktøy (MySQL), se sammenhengen mellom datamodell, databaseverktøy og applikasjon / web-grensesnitt (klient/tjener -arkitektur).Les mer om faget herDemo: Her er en introduksjonsvideo for faget Påmeldingsfrist: 25.08.13 / 25.01.14         Velg semester:  Høst 2013    Vår 2014     Fag Databaser 4980,-         Semesteravgift og eksamenskostnader kommer i tillegg.  [-]

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     [-]

COURSE OVERVIEW  In Microsoft 365 tenant and service management, you will examine all the key components that must be planned for when designing your Microsoft 365 tenant. Once this planning phase is complete, you will learn how to configure your Microsoft 365 tenant, including your organizational profile, tenant subscription options, component services, user accounts and licenses, and security groups. Finally, you will learn how to manage your tenant, which includes the configuration of tenant roles and managing your tenant health and services. With your Microsoft 365 tenant now firmly in place, you will examine the key components of Office 365 management. This begins with an overview of Office 365 product functionality, including Exchange Online, SharePoint Online, Microsoft Teams, additional product resources, and device management. You will then transition to configuring Office 365, with a primary focus on configuring Office client connectivity to Office 365. Finally, you will examine how to manage Office 365 ProPlus deployments, from user-driven client installations to centralized Office 365 ProPlus deployments. You will wrap up this section by learning how to configure Office Telemetry and Microsoft Analytics. The course concludes with an in-depth examination of Microsoft 365 identity synchronization, with a focus on Azure Active Directory Connect. You will learn how to plan for and implement Azure AD Connect, how to manage synchronized identities, and how to implement password management in Microsoft 365 using multi-factor authentication and self-service password management. This section wraps up with a comprehensive look at implementing application and external access. You will learn how to add and manage applications in Azure Active Directory, including how to configure multi-tenant applications. You will then examine how to configure Azure AD Application Proxy, including how to install and register a connector and how to publish an on-premises app for remote access. Finally, you will examine how to design and manage solutions for external access. This includes licensing guidance for Azure AD B2B collaboration, creating a collaborative user, and troubleshooting a B2B collaboration. TARGET AUDIENCE This course is designed for persons who are aspiring to the Microsoft 365 Enterprise Admin role and have completed one of the Microsoft 365 role-based administrator certification paths. COURSE OBJECTIVES Designing, configuring, and managing your Microsoft 365 tenant Office 365 product functionality Configuring Office 365 Managing Office 365 ProPlus deployments Planning and implementing identity synchronization Implementing application and external access COURSE CONTENT Module 1: Designing Your Microsoft 365 Tenant Planning Microsoft 365 in your On-premises Infrastructure Planning Your Identity and Authentication Solution Planning Your Service Setup Planning Your Hybrid Enviroment Planning Your Migration to Office 365 Module 2: Configuring Your Microsoft 365 Tenant Planning  Your Microsoft 365 Experience Configuring  Your Microsoft 365 Experience Managing User Accounts and Licenses in Microsoft 365 Managing Security Groups in Microsoft 365 Implementing Your Domain Services Leveraging FastTrack and Partner Services Module 3: Lab 1 - Configuring your Microsoft 365 Tenant Exercise 1 - Set up a Microsoft 365 Trial Tenant Module 4: Managing Your Microsoft 365 Tenant Configuring Tenant Roles Managing Tenant Health and Services Module 5: Lab 2 - Managing your Microsoft 365 Tenant Exercise 1 - Manage Administration Delegation Exercise 2 - Configure Office 365 Message Encryption (OME) Exercise 3 - Monitor and Troubleshoot Office 365 Module 6: Office 365 Overview Exchange Online Overview SharePoint Online Overview Teams Overview Additional Resources Overview Device Management Overview Module 7: Lab 3 - Office 365 Overview Exercise 1 - Exchange Online Overview Exercise 2 - SharePoint Online Overview Exercise 3 - Teams Overview Module 8: Configuring  Office 365 Office 365 Client Overview Configuring Office Client Connectivity to Office 365 Module 9: Managing Office 365 ProPlus Deployments Managing User-Driven Client Installations Managing Centralized Office 365 ProPlus Deployments Configuring Office Telemetry Configuring Microsoft Analytics Module 10: Lab 4 - Managing Office 365 ProPlus installations Exercise 1 - Prepare an Office 365 ProPlus Managed Installation Exercise 2 - Manage a Centralized Office 365 ProPlus Installation Exercise 3 - Deploy and Configure Office Telemetry Components Module 11: Planning and Implementing Identity Synchronization Introduction to Identity Synchronization Planning for Azure AD Connect Implementing Azure AD Connect Managing Synchronized Identities Password Management in Microsoft 365 Module 12: Lab 5 - Implementing Identity Synchronization Exercise 1 - Set up your organization for identity synchronization Exercise 2 - Implement Identity Synchronization Module 13: Implementing Application and External Access Implementing Applications in Azure AD Configuring Azure AD App Proxy Designing Solutions for External Access TEST CERTIFICATION This course helps you to prepare for exam MS100. But as this is part of an expert certification you should already own one of the Microsoft 365 Associate certifications:  Modern Desktop Teamwork Administrator Security Administrator Messaging Administrator. [-]

  Studieår: 2013-2014   Gjennomføring: Høst og vår Antall studiepoeng: 5.0 Forutsetninger: Studenten bør kunne installere linux, og kjenne til enkle linuxkommandoer som f.eks. «ls». Nybegynnere uten erfaring med linux anbefales å starte med emnet Praktisk Linux, som gir disse forkunnskapene. Innleveringer: Øvinger: 8 av 12 må være godkjent. Vurderingsform: Skriftlig eksamen 3t (60%) og mappe (40%), der alle øvinger er med i mappevurderingen. Ansvarlig: Helge Hafting Eksamensdato: 18.12.13 / 27.05.14         Læremål: Etter å ha gjennomført emnet skal studenten ha følgende samlede læringsutbytte: KUNNSKAPER:Kandidaten:- kan legge planer for en ny tjenermaskin- kan forklare bruk av ulike filsystemer, kvoter og aksesskontrollister FERDIGHETER:Kandidaten:- kan installere linux og vanlig tjenerprogramvare- kan vedlikeholde oppsettet på en tjenermaskin, som regel ved å tilpasse konfigurasjonsfiler- kan lete opp informasjon på nettet, for å løse drifts- og installasjonsproblemer GENERELL KOMPETANSE:Kandidaten:- kan vurdere linuxprogramvare for å dekke en organisasjons behov for tjenester Innhold:Planlegging av linuxtjenere, installasjon av tjenester som filtjener, utskrift, dns, dhcp, dynamisk webtjener, epost, katalogtjenester, fjernadministrasjon, scripting og automasjon.Les mer om faget her Påmeldingsfrist: 25.08.13 / 25.01.14         Velg semester:  Høst 2013    Vår 2014     Fag Linux systemdrift 4980,-         Semesteravgift og eksamenskostnader kommer i tillegg.    [-]

TOGAF® EA Course Combined [-]

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.   [-]

Bli en mester i Adobe InDesign, et ledende verktøy for profesjonell publisering, med kurset "InDesign: Komplett", ledet av Espen Faugstad hos Utdannet.no. InDesign er essensielt for alle som jobber med grafisk design, fra trykte materialer som bøker og brosjyrer til digitale formater som e-bøker og interaktive PDF-dokumenter. Dette kurset er ideelt for alle som ønsker å lære InDesign fra bunnen av, uavhengig av tidligere erfaring. Kurset gir deg en grundig gjennomgang av InDesign, fra organisering og tilgjengelige verktøy og paneler, til opprettelse av dokumenter og optimalisering av arbeidsflyten. Du vil lære å bruke grunnleggende og avanserte verktøy, arbeide effektivt med tekst og grafiske elementer, forstå mastersider, og håndtere farger og stiler. Kurset dekker også hvordan du effektivt kan bruke tabeller, pakke sammen, eksportere, og skrive ut prosjekter. Ved kursets slutt vil du ha en dyp forståelse av Adobe InDesign og være i stand til å produsere høykvalitets designmaterialer for en rekke formater og bruksområder. Kurset vil gi deg ferdighetene og kunnskapen som trengs for å bruke InDesign på et profesjonelt nivå.   Innhold: Kapittel 1: Introduksjon Kapittel 2: Dokument Kapittel 3: Verktøy Kapittel 4: Sider Kapittel 5: Tekst Kapittel 6: Objekt Kapittel 7: Farger Kapittel 8: Stiler Kapittel 9: Tabeller Kapittel 10: Print Kapittel 11: Avslutning   Varighet: 5 timer og 54 minutter   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. [-]

Excelkurs Basis - elæringskurs [-]