Oversikt over relevante kurs innen Kurs i programvare og applikasjoner i Møre og Romsdal

Du vil få tilsendt en «Core guidance» bok og sertifiserings-voucher slik at du kan ta sertifiseringstesten for eksempel hjemme eller på jobb. Denne vil være gyldig i ett år. Tid for sertifiseringstest avtales som beskrevet i e-post med voucher. Eksamen overvåkes av en web-basert eksamensvakt.   Eksamen er på engelsk. Eksamensformen er multiple choice - 50 spørsmål skal besvares, og du består ved 50% korrekte svar (dvs 25 av 50 spørsmål). Deltakerne har 40 minutter til rådighet på eksamen.  Ingen hjelpemidler er tillatt.   Nødvendige forkunnskaper: Ingen [-]

Objectives This course teaches participants the following skills: Identify the purpose and value of each of the Google Cloud Platform products and services Interact with Google Cloud Platform services Describe ways in which customers have used Google Cloud Platform Choose among and use application deployment environments on Google Cloud Platform: Google App Engine, Google Kubernetes Engine, and Google Compute Engine Choose among and use Google Cloud Platform storage options: Google Cloud Storage, Google Cloud SQL, Google Cloud Bigtable, and Google Cloud Datastore Make basic use of BigQuery, Google’s managed data warehouse for analytics Make basic use of Cloud Deployment Manager, Google’s tool for creating and managing cloud resources through templates Make basic use of Google Stackdriver, Google’s monitoring, logging, and diagnostics system All courses will be delivered in partnership with ROI Training, Google Cloud Premier Partner, using a Google Authorized Trainer. Course Outline Module 1: Introducing Google Cloud Platform -Explain the advantages of Google Cloud Platform-Define the components of Google's network infrastructure, including: Points of presence, data centers, regions, and zones-Understand the difference between Infrastructure-as-a-Service (IaaS) and Platform-as-a-Service (PaaS) Module 2: Getting Started with Google Cloud Platform -Identify the purpose of projects on Google Cloud Platform-Understand the purpose of and use cases for Identity and Access Management-List the methods of interacting with Google Cloud Platform-Lab: Getting Started with Google Cloud Platform Module 3: Virtual Machines and Networks in the Cloud -Identify the purpose of and use cases for Google Compute Engine.-Understand the various Google Cloud Platform networking and operational tools and services.-Lab: Compute Engine Module 4: Storage in the Cloud -Understand the purpose of and use cases for: Google Cloud Storage, Google Cloud SQL, Google Cloud Bigtable, and Google Cloud Datastore.-Learn how to choose between the various storage options on Google Cloud Platform.-Lab: Cloud Storage and Cloud SQL Module 5: Containers in the Cloud -Define the concept of a container and identify uses for containers.-Identify the purpose of and use cases for Google Kubernetes Engine and Kubernetes.-Lab: Kubernetes Engine Module 6: Applications in the Cloud -Understand the purpose of and use cases for Google App Engine.-Contrast the App Engine Standard environment with the App Engine Flexible environment.-Understand the purpose of and use cases for Google Cloud Endpoints.-Lab: App Engine Module 7: Developing, Deploying, and Monitoring in the Cloud -Understand options for software developers to host their source code.-Understand the purpose of template-based creation and management of resources.-Understand the purpose of integrated monitoring, alerting, and debugging.-Lab: Deployment Manager and Stackdriver Module 8: Big Data and Machine Learning in the Cloud -Understand the purpose of and use cases for the products and services in the Google Cloud big data and machine learning platforms.-Lab: BigQuery [-]

Fleksible kurs for fremtidenNy kunnskap skal gi umiddelbar effekt, og samtidig være holdbar og bærekraftig på lang sikt.NTI AS har 30 års erfaring innen kurs og kompetanseheving, og utdanner årlig rundt 10.000 personer i Nord Europa innen CAD, BIM, industri, design og konstruksjon.   Solibri Anywhere og Site   På kurset vil du lære å: Sammenstille flere IFC-modeller og navigere i disse Velge ut grupper av objekter for nærmere studier Legge inn snitt, måle, markere og opprette slides fra visninger av modellen Opprette rapporter og kommentere «issues» i Excel og BCF-format Se på resultatet av utførte regelsjekker i modellen Se på resultatet av utførte informasjons- og mengdeuttak fra modellen Høste informasjon og mengder fra modellen basert på eksisterende maler og klassifikasjoner Skape egne klassifikasjoner og definisjoner for megndeuttak   Dette er et populært kurs, meld deg på nå! Spesialtilpasset kurs: NTI anbefaler spesialtilpassede kurs for bedrifter som planlegger å sende to eller flere deltakere på Solibri-kurs. Grunnen til dette er at Solibri brukes av mange forskjellige aktører og profesjoner i BAE-bransjen, og følgelig blir de åpne kursene ofte for generelle for enkelte kursdeltakere. I et spesialtilpasset kurs vil vår kurskonsulent kartlegge fokusområdene i forkant av kurset, og gjennomføre kurset i henhold til selskapets behov, gjerne basert på kundens egne modeller. Utbyttet av kurset blir følgelig mye større.  Ta kontakt med oss på telefon 483 12 300, epost: salg-no@nti.biz eller les mer på www.nti.biz   [-]

Studieår: 2013-2014   Gjennomføring: Høst og vår Antall studiepoeng: 5.0 Forutsetninger: Kunnskaper om Windows server eller gode generelle nettverkskunnskaper eller tilsvarende. Innleveringer: 8 av 12 øvinger må være godkjent. Personlig veileder: ja Vurderingsform: 3 timers individuell skriftlig eksamen Ansvarlig: Jostein Lund Eksamensdato: 02.12.13 / 05.05.14         Læremål: KUNNSKAPER:Kandidaten:- har innsikt i drift av epost- og database-servere- kjenner til løsninger for å eksportere og importere data for epost- og database-servere FERDIGHETER:Kandidaten kan:- installere, konfigurere, drifte og sikre en Exchange epost-server- sette opp og distribuere Outlook til klienter- bruke PowerShell til å automatisere driftsoppgaver i Exchange- installere, konfigurere og drifte en SQL server GENERELL KOMPETANSE:Kandidaten har:- perspektiv og kompetanse i å velge riktige og tilpassete driftsløsninger- kompetanse i å formidle driftsterminologi, både muntlig og skriftlig Innhold:Installasjon, konfigurering og bruk av epost-tjener og Outlook klient. Bruk av PowerShell for å drifte Exchange server. Installasjon, konfigurering og bruk av SQL-tjener. Utveksling av data mellom løst sammenkoblede systemer. Finne, dele og publisere informasjon. Følgende programvare vil bli gjennomgått som supplement for å belyse den teoretiske gjennomgangen: Microsoft Exchange Server, Microsoft SharePoint Portal Server, Microsoft SQL Server. Nødvendig programvare kan fritt lastes ned.Les mer om faget her Påmeldingsfrist: 25.08.13 / 25.01.14         Velg semester:  Høst 2013    Vår 2014     Fag Drift av MS Exchange og MS SQL Server 4980,-         Semesteravgift og eksamenskostnader kommer i tillegg.   [-]

AZ-204: Developing Solutions for Microsoft Azure [-]

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     [-]

  Jobber du med store datamengder? Vil du få kontroll over dataene dine? Har du problemer med utskrift fra Excel? Her vil du få kjennskap til en rekke gode metoder for å jobbe med lister.  Webinaret kan også spesialtilpasses og holdes bedriftsinternt kun for din bedrift.   Kursinnhold:   Prinsipper for å arbeide med lister  Få med deg råd og regler som gjelder for et godt grunnlag   Effektiv merking og navigasjon   Flere måter å sortere grunnlaget på  Sortering etter verdier Sortering etter cellefarge, skriftfarge og celleikon Sortering etter egendefinert liste   Delsammendrag  Lag enkle rapporter ved å bruke delsammendrag verktøyet Kopiere delsammendrag   Filtrering  Se hvordan du finner relevante data i et stort grunnlag Filtrering etter farge og ikon   Fryse første rad og første kolonne     Skjule / vise rader og kolonner     Utskriftinnstillinger  Tilpass utskrift til en side Gjenta rader eller kolonner ved utskrift av flere sider Tilpass utskriftområdet     [-]

COURSE OVERVIEW You’ll learn how to implement and secure a wireless network infrastructure and use Cisco Identity Service Engine (ISE), Cisco Prime Infrastructure (PI), and Cisco Connect Mobile Experience to monitor and troubleshoot network issues.   The course provides hands-on labs to reinforce concepts including deploying Cisco Prime Infrastructure Release 3.5, Cisco Catalyst 9800 Wireless Controller Release IOS XE Gibraltar 16.10, Cisco Digital Network Architecture (DNA) Center Release 1.2.8, Cisco CMX Release 10.5, Cisco MSE Release 8.0 features and Cisco Identity Services Engine (ISE) Release 2.4.   This course also helps you prepare to take the Implementing Cisco Enterprise Wireless Networks (300-430 ENWLSI) exam, which is part of the new CCNP Enterprise certification. Passing the exam will also provide you with the Cisco Certified Specialist - Enterprise Wireless Implementation certification.   TARGET AUDIENCE Individuals needing to understand how to implement, secure and troubleshoot a Cisco Enterprise Wireless Network.   COURSE OBJECTIVES After completing this course you should be able to: Implement network settings to provide a secure wireless network infrastructure Troubleshoot security issues as it relates to the wireless network infrastructure Implement a secure wireless client and troubleshoot wireless client connectivity issues Implement and troubleshoot QoS in wireless networks Implement and troubleshoot advanced capabilities in wireless network services   COURSE CONTENT Securing and Troubleshooting the Wireless Network Infrastructure Implement Secure Access to the WLCs and Access Points Configure the Network for Access Point 802.1X Authentication Use Cisco DNA Center for Controller and AP Auto Install Implement Cisco Prime Infrastructure Define Network Troubleshooting Techniques Troubleshoot Access Point Join Issues Monitor the Wireless Network Implementing and Troubleshooting Secure Client Connectivity Configure the Cisco WLC for Wireless Client 802.1x Authentication Configure the Wireless Client for 802.1X Authentication Configure a Wireless LAN for FlexConnect Implement Guest Services in the Wireless Network Configure the Cisco WLC for Centralized Web Authentication Configure Central Web Authentication on Cisco ISE Implement BYOD Implement Location-Aware Guest Services Troubleshoot Client Connectivity Describe Issues that Affect Client Performance Monitor Wireless Clients Implementing and Troubleshooting QoS in Wireless Networks Implement QoS in the Wireless Network Configure the Cisco WLC to Support Voice Traffic Optimize Wireless Utilization on the Cisco WLC Implement Cisco AVC in the Wireless Network Implement Multicast Services Implement mDNS Service Implement Cisco Media Stream Troubleshoot QoS Issues in the Wireless Network Troublehoot mDNS Issues Troubleshoot Media Stream Issues Implementing and Troubleshooting Advanced Wireless Network Services Implement Base Location Services on Cisco Prime Infrastructure Implement Hyperlocation in the Wireless Network Implement Detect and Locate Services on Cisco CMX Implement Analytics on Cisco CMX Implement Presence Services on Cisco CMX Monitor and Locate Rogue Devices with Cisco Prime Infrastructure and Cisco CMX Monitor and Detect Wireless Clients with Cisco CMX and Cisco DNA Center Run Analytics on Wireless Clients Troubleshoot Location Accuracy with Cisco Hyperlocation Monitor and Manage RF Interferers on the Cisco WLC Monitor and Manager RF Interferers on Cisco Prime Infrastructure and Cisco CMX Labs Lab Familiarization (Base Learning Lab) Configure Secure Management Access for WLCs and APs Add Network Devices and External Resources to Cisco Prime Infrastructure Capture a Successful AP Authentication Implement AAA Services for Central Mode WLANs Implement AAA Services for FlexConnect Mode WLANs Configure Guest Services in the Wireless Network Configure BYOD in the Wireless Network Capture a Successful Client Authentications Configure QoS in the Wireless Network for Voice and Video Services Configure Cisco AVC in the Wireless Network Capture Successful QoS Traffic Marking in the Wireless Network Configure Detect and Locate Services on the Cisco CMX Identify Wireless Clients and Security Threats [-]

JOBB SMART OG EFFEKTIVT! E-læringskurset gir deg en opplevelsesrik og praktisk opplæring i e-postprogrammet Outlook 2016. En kombinasjon av videoer, teori, oppskrifter, oppgaver og tester gjør det enkelt å lære seg de nye funksjonene og verktøyene. E-læringskurset inneholder 46 opplæringsvideoer. E-læringskurset er tilpasset Office 365. * Office 365 gir deg alltid den nyeste versjonen av Outlook. * Navigasjonsfeltet gjør det enkelt å bytte mellom visningene i Outlook 2016. * Egen modus som er optimalisert for berøring. * Microsoft-kontoen kobler enheten til OneDrive, slik at du alltid har tilgang til filene dine. * Enklere søk etter kommandoer, handlinger og hjelp. * Mulighet for automatisk komprimering av store bildevedlegg. * Den første linjen i meldingsteksten vises som standard i meldingslisten og gir god oversikt. * Enklere filtrering av uleste meldinger. * Et integrert utskriftsmiljø med både utskriftsinnstillinger og forhåndsvisning. * Svarknapper gjør det enkelt å svare på eller videresende meldinger direkte fra leseruten. * Enklere sortering og gruppering av meldinger. * Hurtigtrinn kan brukes for å utføre flere handlinger samtidig. * Automatiske svar håndterer meldinger mens du er borte fra kontoret. * Møteinvitasjoner kan opprettes direkte basert på en melding. *Møteinvitasjoner viser et bilde av kalenderen, slik at du kan sjekke om du har ledig tid. * Personkortet i Outlook 2016 inneholder alle viktige detaljer om en kontakt samlet på ett sted. INNHOLDSFORTEGNELSE FØR DU STARTER Hva er Office 365? Elektronisk post Programvinduet Berøringsmodus Visninger Microsoft-konto Hjelp SENDING AV MELDINGER Nettetikette Sending av meldinger Meldingsformater Vedlegg Signatur Viktighet Svarknapper Leverings- og lesebekreftelse BEHANDLING AV MELDINGER Sending og mottak av meldinger Meldingslisten Bildenedlasting Forhåndsvisning og utskrift av meldinger Flagg for oppfølging Svar og videresending Sortering av meldinger Kolonner Direktesøk Organisering av meldinger Hurtigtrinn Søppelpost Automatiske svar KALENDER Kalenderen Avtaler Hendelser Møter Møtesvar Forhåndsvisning og utskrift av kalenderen Værdata PERSONER Kontakter Kontaktgrupper Forhåndsvisning og utskrift av kontakter OPPGAVER OG NOTATER Oppgaver Notater [-]

  Studieår: 2013-2014   Gjennomføring: Høst og vår Antall studiepoeng: 5.0 Forutsetninger: Du må ha god kjennskap til Windows 2008 server og oppsett av AD og DNS og installasjon av SQL 2008 server. For deg som ikke har kjennskap til Windows 2008 server og/eller som heller vil bruke mer tid på SharePoint kan vi tilby en variant der du får ferdig oppsatte servere og SharePoint installert. Innleveringer: 8 obligatoriske øvinger. Personlig veileder: ja Vurderingsform: 2 dagers praktisk hjemmeeksamen med både teoretiske og praktiske oppgaver. Ansvarlig: Stein Meisingseth Eksamensdato: 19.12.13 / 15.05.14         Læremål: KUNNSKAPER:Kandidaten må:- kjenne til bruken av SharePoint i forskjellige situasjoner i en bedrift/organisasjon- kunne gjøre rede for hvordan SharePoint brukes i samskriving- kunne beskrive hvordan ulike roller i en organisasjon kan bruke SharePoint- ha kjennskap til hva det vil si å ha ansvaret for bruken av SharePoint i en bedrift/organisasjon FERDIGHETER:Kandidaten må:- kunne opprette webområder for deling av informasjon med andre- kunne sette opp administrasjon av dokumenter- kunne sette opp publisering av rapporter- kunne sette opp sidestruktur og brukerrettigheter, sideoppsett og tillatelser- kunne opprette infrastruktur for webområder- kunne sette opp muligheter for søking- kunne konfigurere systemet for bruk fra Internett- kunne konfigurerer Office Web Apps- kunne sette opp muligheter for å benytte arbeidsflyt GENERELL KOMPETANSE:Kandidaten:- har kompetanse til selvstendig både å formidle og å ta i bruk sine kunnskaper og ferdigheter i en bedrift som vil bruke SharePoint- kan i et praktisk prosjekt forklare og gjøre bruk av sin kunnskap for bruk av SharePoint Innhold:Hva er SharePoint. Roller. Tilgang til SharePoint. Opprette og bruke Team Sites. Navigering. Lister og Libraries. Home page. Opprette og bruke webpart. Opprette og bruke SharePoint Sites and Site Collections. Opprette og bruke maler. Sikkerhet i SharePoint. Opprette og bruke Wiki. Dokumentdeling. Søking.Les mer om faget her Påmeldingsfrist: 25.08.13 / 25.01.14         Velg semester:  Høst 2013    Vår 2014     Fag SharePoint 4980,-         Semesteravgift og eksamenskostnader kommer i tillegg.    [-]

. Students will learn how to implement Azure compute solutions, create Azure Functions, implement and manage web apps, develop solutions utilizing Azure storage, implement authentication and authorization, and secure their solutions by using KeyVault and Managed Identities. Students will also learn how to connect to and consume Azure services and third-party services, and include event- and message-based models in their solutions. The course also covers monitoring, troubleshooting, and optimizing Azure solutions.   TARGET AUDIENCE Students in this course are interested in Azure development or in passing the Microsoft Azure Developer Associate certification exam.   COURSE CONTENT Module 1: Creating Azure App Service Web Apps Students will learn how to build a web application on the Azure App Service platform. They will learn how the platform functions and how to create, configure, scale, secure, and deploy to the App Service platform. Azure App Service core concepts Creating an Azure App Service Web App Configuring and Monitoring App Service apps Scaling App Service apps Azure App Service staging environments Module 2: Implement Azure functions This module covers creating Functions apps, and how to integrate triggers and inputs/outputs in to the app. Azure Functions overview Developing Azure Functions Implement Durable Functions Module 3: Develop solutions that use blob storage Students will learn how Azure Blob storage works, how to manage data through the hot/cold/archive blob storage lifecycle, and how to use the Azure Blob storage client library to manage data and metadata. Azure Blob storage core concepts Managing the Azure Blob storage lifecycle Working with Azure Blob storage Module 4: Develop solutions that use Cosmos DB storage Students will learn how Cosmos DB is structured and how data consistency is managed. Students will also learn how to create Cosmos DB accounts and create databases, containers, and items by using a mix of the Azure Portal and the .NET SDK. Azure Cosmos DB overview Azure Cosmos DB data structure Working with Azure Cosmos DB resources and data Module 5: Implement IaaS solutions This module instructs students on how to use create VMs and container images to use in their solutions. It covers creating VMs, using ARM templates to automate resource deployment, create and manage Docker images, publishing an image to the Azure Container Registry, and running a container in Azure Container Instances. Provisioning VMs in Azure Create and deploy ARM templates Create container images for solutions Publish a container image to Azure Container Registry Create and run container images in Azure Container Instances Module 6: Implement user authentication and authorization Students will learn how to leverage the Microsoft Identity Platform v2.0 to manage authentication and access to resources. Students will also learn how to use the Microsoft Authentication Library and Microsoft Graph to authenticate a user and retrieve information stored in Azure, and how and when to use Shared Access Signatures. Microsoft Identity Platform v2.0 Authentication using the Microsoft Authentication Library Using Microsoft Graph Authorizing data operations in Azure Storage Module 7: Implement secure cloud solutions This module covers how to secure the information (keys, secrets, certificates) an application uses to access resources. It also covers securing application configuration information. Manage keys, secrets, and certificates by using the KeyVault API Implement Managed Identities for Azure resources Secure app configuration data by using Azure App Configuration Module 8: Implement API Management Students will learn how to publish APIs, create policies to manage information shared through the API, and to manage access to their APIs by using the Azure API Management service. API Management overview Defining policies for APIs Securing your APIs Module 9: Develop App Service Logic Apps This module teaches students how to use Azure Logic Apps to schedule, automate, and orchestrate tasks, business processes, workflows, and services across enterprises or organizations. Azure Logic Apps overview Creating custom connectors for Logic Apps Module 10: Develop event-based solutions Students will learn how to build applications with event-based architectures. Implement solutions that use Azure Event Grid Implement solutions that use Azure Event Hubs Implement solutions that use Azure Notification Hubs Module 11: Develop message-based solutions Students will learn how to build applications with message-based architectures. Implement solutions that use Azure Service Bus Implement solutions that use Azure Queue Storage queues Module 12: Monitor and optimize Azure solutions This module teaches students how to instrument their code for telemetry and how to analyze and troubleshoot their apps. Overview of monitoring in Azure Instrument an app for monitoring Analyzing and troubleshooting apps Implement code that handles transient faults Module 13: Integrate caching and content delivery within solutions Students will learn how to use different caching services to improve the performance of their apps. Develop for Azure Cache for Redis Develop for storage on CDNs [-]

Agenda Module 1: Creating Azure App Service Web Apps -Azure App Service core concepts-Creating an Azure App Service Web App-Configuring and Monitoring App Service apps-Scaling App Service apps-Azure App Service staging environments Module 2: Implement Azure functions -Azure Functions overview-Developing Azure Functions-Implement Durable Functions Module 3: Develop solutions that use blob storage -Azure Blob storage core concepts-Managing the Azure Blob storage lifecycle-Working with Azure Blob storage Module 4: Develop solutions that use Cosmos DB storage -Azure Cosmos DB overview-Azure Cosmos DB data structure-Working with Azure Cosmos DB resources and data Module 5: Implement IaaS solutions -Provisioning VMs in Azure-Create and deploy ARM templates-Create container images for solutions-Publish a container image to Azure Container Registry-Create and run container images in Azure Container Instances Module 6: Implement user authentication and authorization -Microsoft Identity Platform v2.0-Authentication using the Microsoft Authentication Library-Using Microsoft Graph-Authorizing data operations in Azure Storage Module 7: Implement secure cloud solutions -Manage keys, secrets, and certificates by using the KeyVault API-Implement Managed Identities for Azure resources-Secure app configuration data by using Azure App Configuration Module 8: Implement API Management -API Management overview-Defining policies for APIs-Securing your APIs Module 9: Develop App Service Logic Apps -Azure Logic Apps overview-Creating custom connectors for Logic Apps Module 10: Develop event-based solutions -Implement solutions that use Azure Event Grid-Implement solutions that use Azure Event Hubs-Implement solutions that use Azure Notification Hubs Module 11: Develop message-based solutions -Implement solutions that use Azure Service Bus-Implement solutions that use Azure Queue Storage queues Module 12: Monitor and optimize Azure solutions -Overview of monitoring in Azure-Instrument an app for monitoring-Analyzing and troubleshooting apps-Implement code that handles transient faults Module 13: Integrate caching and content delivery within solutions -Develop for Azure Cache for Redis-Develop for storage on CDNs [-]

Fleksible kurs for fremtidenNy kunnskap skal gi umiddelbar effekt, og samtidig være holdbar og bærekraftig på lang sikt. NTI AS har 30 års erfaring innen kurs og kompetanseheving, og utdanner årlig rundt 10.000 personer i Nord Europa innen CAD, BIM, industri, design og konstruksjon.   AutoCAD & LT Basis II Her er et utvalg av temaene du vil lære på kurset: Oppbygning av brukerflaten Blokker med attributter og uttrekk til regneark Tabeller og Fields Eksterne referanser Annotative objekter Utdypning av plott og layout Tips og triks Funksjoner i Express Tools Der kurset «AutoCAD & AutoCAD LT 2D Basis I” ga en enkel innføring i de forskjellige delene i tegneprosessen, fokuserer ”AutoCAD & AutoCAD LT 2D Basis II” på en effektivisering av tegneprosessen. Det skjer blant annet ved bruk av blokker med attributter og uttrekk av disse til regneark, tabeller med fields (blant annet til bruk ved arealberegning), eksterne referanser som sparer tid og plass på nettverket, samt annotative objekter som automatisk tilpasses layoutets målforhold. Dette er et populært kurs, meld deg på nå!   Tilpassete kurs for bedrifterVi vil at kundene våre skal være best på det de gjør - hele tiden.  Derfor tenker vi langsiktig om kompetanseutvikling og ser regelmessig kunnskapsløft som en naturlig del av en virksomhet. Vårt kurskonsept bygger på et moderne sett av ulike læringsmiljøer, som gjør det enkelt å finne riktig løsning uansett behov. Ta kontakt med oss på telefon 483 12 300, epost: salg@nticad.no eller les mer på www.nticad.no [-]

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. After completing this course, students will be able to: 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 prerequisites Successful students start this course with knowledge of cloud computing and core data concepts and professional experience with data solutions.Recommended prerequisites:M-DP900 - Microsoft Azure Data FundamentalsM-AZ900 - Microsoft Azure Fundamentals Agenda 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. 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. 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. 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). 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. Module 16: Build reports using Power BI integration with Azure Synapase 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. 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. [-]

Lær å lage nettsider med WordPress Dette kurset passer for deg som skal designe eller bruke nettsider laget med WordPress og som trenger å forstå hvordan det virker. Du lærer også hvordan du forandrer og tilpasser designmaler, bruker utvidelser, knytter nettsiden mot sosiale medier, og hvordan du søkemotoroptimaliserer nettsidene slik at du blir funnet av brukerene. WordPress er en publiseringsløsning som gjør det enkelt å lage profesjonelle nettsider og det har den fordelen at kunden kan oppdatere og legge inn eget innhold. Dette er verdens største publiseringsverktøy og det finnes utallige designmaler som gjør det mulig for deg å endre utseende på nettsiden uten å endre innholdet. Dette lærer du: Bli kjent med løsningen Opprette nettsider på eget domene Du lærer å lage nettsider ved hjelp av ferdige maler Endre designmaler Publisere nettsider og innlegg Legge inn bilder Gjøre nettsiden din søkbar Bruk av plugins og widgets slik som f. eks: deling i sosiale medier, slideshow, kalenderfunksjon og mye mer Enkel bilde­redigering i WordPress https://igm.no/wordpress-kurs/ [-]